From 0819a9f1a4db583d0bc35e6d6f2f735909bb40e3 Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:22:12 +0530
Subject: [PATCH 1/8] Delete notebooks/en/fine_tuning_vlm_trl.ipynb
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vKadZFQ2IdJb"
- },
- "source": [
- "# Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)\n",
- "\n",
- "\n",
- "\n",
- "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JATmSI8mcyW2"
- },
- "source": [
- "🚨 **WARNING**: This notebook is resource-intensive and requires substantial computational power. If you’re running this in Colab, it will utilize an A100 GPU.\n",
- "\n",
- "In this recipe, we’ll demonstrate how to fine-tune a [Vision Language Model (VLM)](https://huggingface.co/blog/vlms) using the Hugging Face ecosystem, specifically with the [Transformer Reinforcement Learning library (TRL)](https://huggingface.co/docs/trl/index).\n",
- "\n",
- "**🌟 Model & Dataset Overview**\n",
- "\n",
- "We’ll be fine-tuning the [Qwen2-VL-7B](https://qwenlm.github.io/blog/qwen2-vl/) model on the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset. This dataset includes images of various chart types paired with question-answer pairs—ideal for enhancing the model's visual question-answering capabilities.\n",
- "\n",
- "**📖 Additional Resources**\n",
- "\n",
- "If you’re interested in more VLM applications, check out:\n",
- "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms): where I guide you through building a RAG system using Document Retrieval (ColPali) and Vision Language Models (VLMs).\n",
- "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl): an excellent deep dive into fine-tuning multimodal LLMs with TRL.\n",
- "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main): a collection of engaging notebooks on cutting-edge vision and multimodal AI topics.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QoD6dxPeXDKR"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gSHmDKNFoqjC"
- },
- "source": [
- "## 1. Install Dependencies\n",
- "\n",
- "Let’s start by installing the essential libraries we’ll need for fine-tuning! 🚀\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "GCMhPmFdIGSb",
- "outputId": "016a9f29-9c8d-42c5-9187-813f5bdeb536"
- },
- "outputs": [],
- "source": [
- "!pip install -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
- "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "J4pAvoQaOJ1M"
- },
- "source": [
- "We’ll also need to install an earlier version of *PyTorch*, as the latest version has an issue that currently prevents this notebook from running correctly. You can learn more about the issue [here](https://github.com/pytorch/pytorch/issues/138340) and consider updating to the latest version once it’s resolved."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D8iRteA4oXVj",
- "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
- },
- "outputs": [],
- "source": [
- "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "V0-2Lso6wkIh"
- },
- "source": [
- "Log in to Hugging Face to upload your fine-tuned model! 🗝️\n",
- "\n",
- "You’ll need to authenticate with your Hugging Face account to save and share your model directly from this notebook.\n"
- ]
- },
- {
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- "execution_count": 3,
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- {
- "data": {
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- "text/plain": [
- "VBox(children=(HTML(value='
},\n",
- " {'type': 'text',\n",
- " 'text': 'Is the rightmost value of light brown graph 58?'}]},\n",
- " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'No'}]}]"
- ]
- },
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- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "train_dataset[200]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YY1Y_KDtoycB"
- },
- "source": [
- "## 3. Load Model and Check Performance! 🤔\n",
- "\n",
- "Now that we’ve loaded the dataset, let’s start by loading the model and evaluating its performance using a sample from the dataset. We’ll be using [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), a Vision Language Model (VLM) capable of understanding both visual data and text.\n",
- "\n",
- "If you're exploring alternatives, consider these open-source options:\n",
- "- Meta AI's [Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)\n",
- "- Mistral AI's [Pixtral-12B](https://huggingface.co/mistralai/Pixtral-12B-2409)\n",
- "- Allen AI's [Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924)\n",
- "\n",
- "Additionally, you can check the Leaderboards, such as the [WildVision Arena](https://huggingface.co/spaces/WildVision/vision-arena) or the [OpenVLM Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard), to find the best-performing VLMs.\n",
- "\n",
- "\n"
- ]
- },
- {
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- "colab": {
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- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
- ]
- },
- {
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- "0it [00:00, ?it/s]"
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- "source": [
- "import torch\n",
- "from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor\n",
- "\n",
- "model_id = \"Qwen/Qwen2-VL-7B-Instruct\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2HobU2iPUDWL"
- },
- "source": [
- "Next, we’ll load the model and the tokenizer to prepare for inference."
- ]
- },
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- "text": [
- "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
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- ],
- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7JtKcuuXUGUT"
- },
- "source": [
- "To evaluate the model's performance, we’ll use a sample from the dataset. First, let’s take a look at the internal structure of this sample.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "i-eIIdL9lqJJ",
- "outputId": "02eda1d8-f6e8-43e7-85f4-b58e50370da3"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]},\n",
- " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Yes'}]}]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hLaWWJk_RkVU"
- },
- "source": [
- "We’ll use the sample without the system message to assess the VLM's raw understanding. Here’s the input we will use:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ytnr1rePOamM",
- "outputId": "b38d536e-bfa2-49e8-eb0f-22c1c1cbcff2"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1:2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3IK2HOMuRtY_"
- },
- "source": [
- "Now, let’s take a look at the chart corresponding to the sample. Can you answer the query based on the visual information?\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 376
- },
- "id": "QavnLzjJUbxf",
- "outputId": "0b935e4d-3b13-4676-f3cc-1da64bc828ab"
- },
- "outputs": [
- {
- "data": {
- "image/jpeg": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gpLfsCUtUW6I"
- },
- "source": [
- "Let’s create a method that takes the model, processor, and sample as inputs to generate the model's answer. This will allow us to streamline the inference process and easily evaluate the VLM's performance.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "id": "_MoRTjFcE8qD"
- },
- "outputs": [],
- "source": [
- "from qwen_vl_utils import process_vision_info\n",
- "\n",
- "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
- " # Prepare the text input by applying the chat template\n",
- " text_input = processor.apply_chat_template(\n",
- " sample[1:2], # Use the sample without the system message\n",
- " tokenize=False,\n",
- " add_generation_prompt=True\n",
- " )\n",
- "\n",
- " # Process the visual input from the sample\n",
- " image_inputs, _ = process_vision_info(sample)\n",
- "\n",
- " # Prepare the inputs for the model\n",
- " model_inputs = processor(\n",
- " text=[text_input],\n",
- " images=image_inputs,\n",
- " return_tensors=\"pt\",\n",
- " ).to(device) # Move inputs to the specified device\n",
- "\n",
- " # Generate text with the model\n",
- " generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)\n",
- "\n",
- " # Trim the generated ids to remove the input ids\n",
- " trimmed_generated_ids = [\n",
- " out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)\n",
- " ]\n",
- "\n",
- " # Decode the output text\n",
- " output_text = processor.batch_decode(\n",
- " trimmed_generated_ids,\n",
- " skip_special_tokens=True,\n",
- " clean_up_tokenization_spaces=False\n",
- " )\n",
- "\n",
- " return output_text[0] # Return the first decoded output text"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "5UeNiMJC_uCk",
- "outputId": "6b7c1260-9980-442f-9d55-a8d7ebedaf94"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Example of how to call the method with sample:\n",
- "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ysh0e9DRUfF-"
- },
- "source": [
- "While the model successfully retrieves the correct visual information, it struggles to answer the question accurately. This indicates that fine-tuning might be the key to enhancing its performance. Let’s proceed with the fine-tuning process!\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Sw3b76rawti6"
- },
- "source": [
- "**Remove Model and Clean GPU**\n",
- "\n",
- "Before we proceed with training the model in the next section, let's clear the current variables and clean the GPU to free up resources.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dxkXZuUkvy8j"
- },
- "outputs": [],
- "source": [
- "import gc\n",
- "import time\n",
- "\n",
- "def clear_memory():\n",
- " # Delete variables if they exist in the current global scope\n",
- " if 'inputs' in globals(): del globals()['inputs']\n",
- " if 'model' in globals(): del globals()['model']\n",
- " if 'processor' in globals(): del globals()['processor']\n",
- " if 'trainer' in globals(): del globals()['trainer']\n",
- " if 'peft_model' in globals(): del globals()['peft_model']\n",
- " if 'bnb_config' in globals(): del globals()['bnb_config']\n",
- " time.sleep(2)\n",
- "\n",
- " # Garbage collection and clearing CUDA memory\n",
- " gc.collect()\n",
- " time.sleep(2)\n",
- " torch.cuda.empty_cache()\n",
- " torch.cuda.synchronize()\n",
- " time.sleep(2)\n",
- " gc.collect()\n",
- " time.sleep(2)\n",
- "\n",
- " print(f\"GPU allocated memory: {torch.cuda.memory_allocated() / 1024**3:.2f} GB\")\n",
- " print(f\"GPU reserved memory: {torch.cuda.memory_reserved() / 1024**3:.2f} GB\")\n",
- "\n",
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YIZOIVEzQqNg"
- },
- "source": [
- "## 4. Fine-Tune the Model using TRL\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "yIrR9gP2z90z"
- },
- "source": [
- "### 4.1 Load the Quantized Model for Training ⚙️\n",
- "\n",
- "Next, we’ll load the quantized model using [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index). If you want to learn more about quantization, check out [this blog post](https://huggingface.co/blog/merve/quantization) or [this one](https://www.maartengrootendorst.com/blog/quantization/).\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "b4e7dc3a0b6643a1b2c4124a11f1a932",
- "1065fc621b1f4ae38e021e464ba28340",
- "d66b6961a23f4c99872521d8b12b3c93",
- "67c2b6c826b4437ca9f6f354af959e9d",
- "dd37b7d9cd2a456986ac59ced8a35205",
- "1bf49b9b487442df874672b30e0af8da",
- "18e8ee9a09ab46c2833646651409b8b0",
- "dd477ac59fa6470ca0ef1f59c39f43cb",
- "df149f2746544b4a8c3cac6a2365e6db",
- "1b60256c2fa440ccbb085ada32702b1e",
- "7531040fc6c743e8bb85dcf07eb73560"
- ]
- },
- "id": "zm_bJRrXsESg",
- "outputId": "5a3ccdc6-9d40-43c8-df9e-9222a0656c2b"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "b4e7dc3a0b6643a1b2c4124a11f1a932",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from transformers import BitsAndBytesConfig\n",
- "\n",
- "# BitsAndBytesConfig int-4 config\n",
- "bnb_config = BitsAndBytesConfig(\n",
- " load_in_4bit=True,\n",
- " bnb_4bit_use_double_quant=True,\n",
- " bnb_4bit_quant_type=\"nf4\",\n",
- " bnb_4bit_compute_dtype=torch.bfloat16\n",
- ")\n",
- "\n",
- "# Load model and tokenizer\n",
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- " quantization_config=bnb_config\n",
- ")\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "65wfO29isQlX"
- },
- "source": [
- "### 4.2 Set Up QLoRA and SFTConfig 🚀\n",
- "\n",
- "Next, we will configure [QLoRA](https://github.com/artidoro/qlora) for our training setup. QLoRA enables efficient fine-tuning of large language models while significantly reducing the memory footprint compared to traditional methods. Unlike standard LoRA, which reduces memory usage by applying a low-rank approximation, QLoRA takes it a step further by quantizing the weights of the LoRA adapters. This leads to even lower memory requirements and improved training efficiency, making it an excellent choice for optimizing our model's performance without sacrificing quality.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ITmkRHWCKYjf",
- "outputId": "3ca824c9-4aca-4d5b-e942-7a1705939e08"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 2,523,136 || all params: 8,293,898,752 || trainable%: 0.0304\n"
- ]
- }
- ],
- "source": [
- "from peft import LoraConfig, get_peft_model\n",
- "\n",
- "# Configure LoRA\n",
- "peft_config = LoraConfig(\n",
- " lora_alpha=16,\n",
- " lora_dropout=0.05,\n",
- " r=8,\n",
- " bias=\"none\",\n",
- " target_modules=[\"q_proj\", \"v_proj\"],\n",
- " task_type=\"CAUSAL_LM\",\n",
- ")\n",
- "\n",
- "# Apply PEFT model adaptation\n",
- "peft_model = get_peft_model(model, peft_config)\n",
- "\n",
- "# Print trainable parameters\n",
- "peft_model.print_trainable_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "K5zzHM2GVtxD"
- },
- "source": [
- "We will use Supervised Fine-Tuning (SFT) to refine our model’s performance on the task at hand. To do this, we'll define the training arguments using the [SFTConfig](https://huggingface.co/docs/trl/sft_trainer) class from the [TRL library](https://huggingface.co/docs/trl/index). SFT allows us to provide labeled data, helping the model learn to generate more accurate responses based on the input it receives. This approach ensures that the model is tailored to our specific use case, leading to better performance in understanding and responding to visual queries.\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "id": "SbqX1pQUKaSM"
- },
- "outputs": [],
- "source": [
- "from trl import SFTConfig\n",
- "\n",
- "# Configure training arguments\n",
- "training_args = SFTConfig(\n",
- " output_dir=\"qwen2-7b-instruct-trl-sft-ChartQA\", # Directory to save the model\n",
- " num_train_epochs=3, # Number of training epochs\n",
- " per_device_train_batch_size=4, # Batch size for training\n",
- " per_device_eval_batch_size=4, # Batch size for evaluation\n",
- " gradient_accumulation_steps=8, # Steps to accumulate gradients\n",
- " gradient_checkpointing=True, # Enable gradient checkpointing for memory efficiency\n",
- " # Optimizer and scheduler settings\n",
- " optim=\"adamw_torch_fused\", # Optimizer type\n",
- " learning_rate=2e-4, # Learning rate for training\n",
- " lr_scheduler_type=\"constant\", # Type of learning rate scheduler\n",
- " # Logging and evaluation\n",
- " logging_steps=10, # Steps interval for logging\n",
- " eval_steps=10, # Steps interval for evaluation\n",
- " eval_strategy=\"steps\", # Strategy for evaluation\n",
- " save_strategy=\"steps\", # Strategy for saving the model\n",
- " save_steps=20, # Steps interval for saving\n",
- " metric_for_best_model=\"eval_loss\", # Metric to evaluate the best model\n",
- " greater_is_better=False, # Whether higher metric values are better\n",
- " load_best_model_at_end=True, # Load the best model after training\n",
- " # Mixed precision and gradient settings\n",
- " bf16=True, # Use bfloat16 precision\n",
- " tf32=True, # Use TensorFloat-32 precision\n",
- " max_grad_norm=0.3, # Maximum norm for gradient clipping\n",
- " warmup_ratio=0.03, # Ratio of total steps for warmup\n",
- " # Hub and reporting\n",
- " push_to_hub=True, # Whether to push model to Hugging Face Hub\n",
- " report_to=\"wandb\", # Reporting tool for tracking metrics\n",
- " # Gradient checkpointing settings\n",
- " gradient_checkpointing_kwargs={\"use_reentrant\": False}, # Options for gradient checkpointing\n",
- " # Dataset configuration\n",
- " dataset_text_field=\"\", # Text field in dataset\n",
- " dataset_kwargs={\"skip_prepare_dataset\": True}, # Additional dataset options\n",
- " #max_seq_length=1024 # Maximum sequence length for input\n",
- ")\n",
- "\n",
- "training_args.remove_unused_columns = False # Keep unused columns in dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pOUrD9P-y-Kf"
- },
- "source": [
- "### 4.3 Training the Model 🏃"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wjQGt-iZVyef"
- },
- "source": [
- "We will log our training progress using [Weights & Biases (W&B)](https://wandb.ai/). Let’s connect our notebook to W&B to capture essential information during training.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 228
- },
- "id": "ckVfXDWsoF4Y",
- "outputId": "bb7ce99c-ed2b-481c-a11f-27272ce8da91"
- },
- "outputs": [],
- "source": [
- "import wandb\n",
- "\n",
- "wandb.init(\n",
- " project=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
- " name=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
- " config=training_args,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ucTUbGURV2_-"
- },
- "source": [
- "We need a collator function to properly retrieve and batch the data during the training procedure. This function will handle the formatting of our dataset inputs, ensuring they are correctly structured for the model. Let's define the collator function below.\n",
- "\n",
- "👉 Check out the TRL official example [scripts]( https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87) for more details.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {
- "id": "pAzDovzylQeZ"
- },
- "outputs": [],
- "source": [
- "# Create a data collator to encode text and image pairs\n",
- "def collate_fn(examples):\n",
- " # Get the texts and images, and apply the chat template\n",
- " texts = [processor.apply_chat_template(example, tokenize=False) for example in examples] # Prepare texts for processing\n",
- " image_inputs = [process_vision_info(example)[0] for example in examples] # Process the images to extract inputs\n",
- "\n",
- " # Tokenize the texts and process the images\n",
- " batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True) # Encode texts and images into tensors\n",
- "\n",
- " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
- " labels = batch[\"input_ids\"].clone() # Clone input IDs for labels\n",
- " labels[labels == processor.tokenizer.pad_token_id] = -100 # Mask padding tokens in labels\n",
- "\n",
- " # Ignore the image token index in the loss computation (model specific)\n",
- " if isinstance(processor, Qwen2VLProcessor): # Check if the processor is Qwen2VLProcessor\n",
- " image_tokens = [151652, 151653, 151655] # Specific image token IDs for Qwen2VLProcessor\n",
- " else:\n",
- " image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)] # Convert image token to ID\n",
- "\n",
- " # Mask image token IDs in the labels\n",
- " for image_token_id in image_tokens:\n",
- " labels[labels == image_token_id] = -100 # Mask image token IDs in labels\n",
- "\n",
- " batch[\"labels\"] = labels # Add labels to the batch\n",
- "\n",
- " return batch # Return the prepared batch"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "skbpTuJlV8qN"
- },
- "source": [
- "Now, we will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "k_jk-U7ULYtA",
- "outputId": "0dc465a9-1744-4b9a-d090-22a63f2e48de"
- },
- "outputs": [],
- "source": [
- "from trl import SFTTrainer\n",
- "\n",
- "trainer = SFTTrainer(\n",
- " model=model,\n",
- " args=training_args,\n",
- " train_dataset=train_dataset,\n",
- " eval_dataset=eval_dataset,\n",
- " data_collator=collate_fn,\n",
- " peft_config=peft_config,\n",
- " processing_class=processor.tokenizer,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NlDsh4WvWCx0"
- },
- "source": [
- "Time to Train the Model! 🎉"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "p1rgMTBDLboO"
- },
- "outputs": [],
- "source": [
- "trainer.train()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "w6CykSCtX-Xa"
- },
- "source": [
- "Let's save the results 💾"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "27d3420d65a545e29cbdae604caa32f3",
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- },
- "id": "tE8usZw0lgrL",
- "outputId": "455a0714-04b7-4078-ca3f-ccba0ad01f13"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "27d3420d65a545e29cbdae604caa32f3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "trainer.save_model(training_args.output_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6yx_sGW42dN3"
- },
- "source": [
- "## 5. Testing the Fine-Tuned Model 🔍\n",
- "\n",
- "Now that we've successfully fine-tuned our Vision Language Model (VLM), it's time to evaluate its performance! In this section, we will test the model using examples from the ChartQA dataset to see how well it answers questions based on chart images. Let's dive in and explore the results! 🚀\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "i0KEPu6qYKqn"
- },
- "source": [
- "Let's clean up the GPU memory to ensure optimal performance 🧹"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Ttx6EK8Uy8t0"
- },
- "outputs": [],
- "source": [
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HwCTPHsfujn2"
- },
- "source": [
- "We will reload the base model using the same pipeline as before."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "dc179daa0be34359a3c9ec0224537c53",
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- ]
- },
- "id": "EFqTNUud2lA7",
- "outputId": "32f3a882-0fed-4527-ca83-74857afe658a"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "dc179daa0be34359a3c9ec0224537c53",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lRAPEYKkYSkB"
- },
- "source": [
- "We will attach the trained adapter to the pretrained model. This adapter contains the fine-tuning adjustments we made during training, allowing the base model to leverage the new knowledge without altering its core parameters. By integrating the adapter, we can enhance the model's capabilities while maintaining its original structure.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 81,
- "referenced_widgets": [
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- },
- "id": "mQi2xBXk4sHe",
- "outputId": "1096bbce-04e1-475d-c7ea-090ef2e5bf5b"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9f91c4c646c14451a8d9013ff7c8b754",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_config.json: 0%| | 0.00/650 [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1a953c336a9049b3a46f1895bcc03ed4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "adapter_path = \"sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA\"\n",
- "model.load_adapter(adapter_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pqryChyLWRmR"
- },
- "source": [
- "We will utilize the previous sample from the dataset that the model initially struggled to answer correctly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "3X9YGJUezZr6",
- "outputId": "0598cff6-dae8-4496-bef7-82e2015d12bf"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][:2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 376
- },
- "id": "2hLJrxxTVn6x",
- "outputId": "ee8fd396-f73b-4b8d-e10f-e4430a6a9b13"
- },
- "outputs": [
- {
- "data": {
- "image/jpeg": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "pdb9vErmzdAf",
- "outputId": "e5059c15-c1ec-4f9b-d642-820cac72bb59"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'Yes'"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "swibyq5AWctZ"
- },
- "source": [
- "Since this sample is drawn from the training set, the model has encountered it during training, which may be seen as a form of cheating. To gain a more comprehensive understanding of the model's performance, we will also evaluate it using an unseen sample.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2fEa9ChjZsJw"
- },
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "czZSBgnoef1E",
- "outputId": "350d3520-4973-444e-f2bc-12e44d548fe6"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'What is the value of Slovenia in the graph?'}]}]"
- ]
- },
- "execution_count": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test_dataset[10][:2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 617
- },
- "id": "ATuQ6ZS6eirO",
- "outputId": "c3adc0fd-0fdc-4ff4-cc4e-14b4d9039323"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test_dataset[10][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "9yHJMKHNWcMc",
- "outputId": "5cedc6aa-e375-4026-92f2-6be4d0e50d91"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'1'"
- ]
- },
- "execution_count": 84,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "output = generate_text_from_sample(model, processor, test_dataset[10])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NUr6jmnAIlh1"
- },
- "source": [
- "The model has successfully learned to respond to the queries as specified in the dataset. We've achieved our goal! 🎉✨"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Z_Ns3p0Dhjbr"
- },
- "source": [
- "💻 I’ve developed an example application to test the model, which you can find [here](https://huggingface.co/spaces/sergiopaniego/Qwen2-VL-7B-trl-sft-ChartQA). You can easily compare it with another Space featuring the pre-trained model, available [here](https://huggingface.co/spaces/GanymedeNil/Qwen2-VL-7B)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 822
- },
- "id": "dYJJ6ASKhJ5k",
- "outputId": "f010e580-a4a1-470b-84c4-95ee999774b8"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 96,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import IFrame\n",
- "\n",
- "IFrame(src=\"https://sergiopaniego-qwen2-vl-7b-trl-sft-chartqa.hf.space\", width=1000, height=800)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "daUMWw5xxhSc"
- },
- "source": [
- "## 6. Compare Fine-Tuned Model vs. Base Model + Prompting 📊\n",
- "\n",
- "We have explored how fine-tuning the VLM can be a valuable option for adapting it to our specific needs. Another approach to consider is directly using prompting or implementing a RAG system, which is covered in another [recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms).\n",
- "\n",
- "Fine-tuning a VLM requires significant amounts of data and computational resources, which can incur costs. In contrast, we can experiment with prompting to see if we can achieve similar results without the overhead of fine-tuning.\n",
- "\n",
- "Let's again clean up the GPU memory to ensure optimal performance 🧹"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ei-OZGGx4lHe",
- "outputId": "81bee1a4-4860-464a-bc6a-ae2fb1695236"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GPU allocated memory: 0.02 GB\n",
- "GPU reserved memory: 0.27 GB\n"
- ]
- }
- ],
- "source": [
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9NApMx5S4-sh"
- },
- "source": [
- "🏗️ First, we will load the baseline model following the same pipeline as before.\n"
- ]
- },
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- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
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- "metadata": {},
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- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
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- "source": [
- "📜 In this case, we will again use the previous sample, but this time we will include the system message as follows. This addition helps to contextualize the input for the model, potentially improving its response accuracy.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SNMKKvvZxqR8",
- "outputId": "fefa3c3a-f666-4c8f-fdef-38b16539c069"
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- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 93,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][:2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fou6XKGM5Uii"
- },
- "source": [
- "Let's see how it performs!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "eN3NkkpgR4do",
- "outputId": "5ed3fb26-580a-4c07-d626-cf8c66619b81"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'Yes'"
- ]
- },
- "execution_count": 94,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "text = processor.apply_chat_template(\n",
- " train_dataset[0][:2], tokenize=False, add_generation_prompt=True\n",
- ")\n",
- "\n",
- "image_inputs, _ = process_vision_info(train_dataset[0])\n",
- "\n",
- "inputs = processor(\n",
- " text=[text],\n",
- " images=image_inputs,\n",
- " return_tensors=\"pt\",\n",
- ")\n",
- "\n",
- "inputs = inputs.to(\"cuda\")\n",
- "\n",
- "generated_ids = model.generate(**inputs, max_new_tokens=1024)\n",
- "generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
- "\n",
- "output_text = processor.batch_decode(\n",
- " generated_ids_trimmed,\n",
- " skip_special_tokens=True,\n",
- " clean_up_tokenization_spaces=False\n",
- ")\n",
- "\n",
- "output_text[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "o9Id3dzV5Wwy"
- },
- "source": [
- "💡 As we can see, the model generates the correct answer using the pretrained model along with the additional system message, without any training. This approach may serve as a viable alternative to fine-tuning, depending on the specific use case."
- ]
- },
- {
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- "source": [
- "## 7. Continuing the Learning Journey 🧑🎓️\n",
- "\n",
- "To further enhance your understanding and skills in working with multimodal models, check out the following resources:\n",
- "\n",
- "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)\n",
- "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)\n",
- "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main)\n",
- "- [Quantize Your Qwen2-VL Model with AutoAWQ](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#quantize-your-own-model-with-autoawq)\n",
- "- [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm)\n",
- "- [Hugging Face Llama Recipes: SFT for VLM](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/sft_vlm.py)\n",
- "- [Hugging Face Llama Recipes: PEFT Fine-Tuning](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/peft_finetuning.py)\n",
- "- [Hugging Face Blog: IDEFICS2](https://huggingface.co/blog/idefics2)\n",
- "\n",
- "These resources will help you deepen your knowledge and skills in multimodal learning.\n",
- "\n"
- ]
- }
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From 205bd1514a2d0b73f455b9d8ccaf550984cb8e6c Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:22:53 +0530
Subject: [PATCH 2/8] Add files via upload
---
notebooks/en/fine_tuning_vlm_trl.ipynb | 11073 +++++++++++++++++++++++
1 file changed, 11073 insertions(+)
create mode 100644 notebooks/en/fine_tuning_vlm_trl.ipynb
diff --git a/notebooks/en/fine_tuning_vlm_trl.ipynb b/notebooks/en/fine_tuning_vlm_trl.ipynb
new file mode 100644
index 00000000..d2d55f37
--- /dev/null
+++ b/notebooks/en/fine_tuning_vlm_trl.ipynb
@@ -0,0 +1,11073 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vKadZFQ2IdJb"
+ },
+ "source": [
+ "# Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)\n",
+ "\n",
+ "\n",
+ "\n",
+ "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JATmSI8mcyW2"
+ },
+ "source": [
+ "🚨 **WARNING**: This notebook is resource-intensive and requires substantial computational power. If you’re running this in Colab, it will utilize an A100 GPU.\n",
+ "\n",
+ "In this recipe, we’ll demonstrate how to fine-tune a [Vision Language Model (VLM)](https://huggingface.co/blog/vlms) using the Hugging Face ecosystem, specifically with the [Transformer Reinforcement Learning library (TRL)](https://huggingface.co/docs/trl/index).\n",
+ "\n",
+ "**🌟 Model & Dataset Overview**\n",
+ "\n",
+ "We’ll be fine-tuning the [Qwen2-VL-7B](https://qwenlm.github.io/blog/qwen2-vl/) model on the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset. This dataset includes images of various chart types paired with question-answer pairs—ideal for enhancing the model's visual question-answering capabilities.\n",
+ "\n",
+ "**📖 Additional Resources**\n",
+ "\n",
+ "If you’re interested in more VLM applications, check out:\n",
+ "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms): where I guide you through building a RAG system using Document Retrieval (ColPali) and Vision Language Models (VLMs).\n",
+ "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl): an excellent deep dive into fine-tuning multimodal LLMs with TRL.\n",
+ "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main): a collection of engaging notebooks on cutting-edge vision and multimodal AI topics.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QoD6dxPeXDKR"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gSHmDKNFoqjC"
+ },
+ "source": [
+ "## 1. Install Dependencies\n",
+ "\n",
+ "Let’s start by installing the essential libraries we’ll need for fine-tuning! 🚀\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "GCMhPmFdIGSb",
+ "outputId": "016a9f29-9c8d-42c5-9187-813f5bdeb536"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
+ "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J4pAvoQaOJ1M"
+ },
+ "source": [
+ "We’ll also need to install an earlier version of *PyTorch*, as the latest version has an issue that currently prevents this notebook from running correctly. You can learn more about the issue [here](https://github.com/pytorch/pytorch/issues/138340) and consider updating to the latest version once it’s resolved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "D8iRteA4oXVj",
+ "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V0-2Lso6wkIh"
+ },
+ "source": [
+ "Log in to Hugging Face to upload your fine-tuned model! 🗝️\n",
+ "\n",
+ "You’ll need to authenticate with your Hugging Face account to save and share your model directly from this notebook.\n"
+ ]
+ },
+ {
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+ "VBox(children=(HTML(value='
},\n",
+ " {'type': 'text',\n",
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+ {
+ "cell_type": "markdown",
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+ "source": [
+ "## 3. Load Model and Check Performance! 🤔\n",
+ "\n",
+ "Now that we’ve loaded the dataset, let’s start by loading the model and evaluating its performance using a sample from the dataset. We’ll be using [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), a Vision Language Model (VLM) capable of understanding both visual data and text.\n",
+ "\n",
+ "If you're exploring alternatives, consider these open-source options:\n",
+ "- Meta AI's [Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)\n",
+ "- Mistral AI's [Pixtral-12B](https://huggingface.co/mistralai/Pixtral-12B-2409)\n",
+ "- Allen AI's [Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924)\n",
+ "\n",
+ "Additionally, you can check the Leaderboards, such as the [WildVision Arena](https://huggingface.co/spaces/WildVision/vision-arena) or the [OpenVLM Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard), to find the best-performing VLMs.\n",
+ "\n",
+ "\n"
+ ]
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+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+ ]
+ },
+ {
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+ "0it [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor\n",
+ "\n",
+ "model_id = \"Qwen/Qwen2-VL-7B-Instruct\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2HobU2iPUDWL"
+ },
+ "source": [
+ "Next, we’ll load the model and the tokenizer to prepare for inference."
+ ]
+ },
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+ },
+ "text/plain": [
+ "Downloading shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "4f032b607d6d4b048951ff88352fbb3d",
+ "version_major": 2,
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+ },
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+ ]
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+ },
+ {
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+ },
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "62ed3c82cffe40c98db49ef61f69626b",
+ "version_major": 2,
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+ },
+ "text/plain": [
+ "model-00004-of-00005.safetensors: 0%| | 0.00/3.86G [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "725ae4b3764643e489a8b68699f62c19",
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+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "generation_config.json: 0%| | 0.00/244 [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ },
+ "text/plain": [
+ "preprocessor_config.json: 0%| | 0.00/347 [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ "tokenizer_config.json: 0%| | 0.00/4.19k [00:00, ?B/s]"
+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ },
+ "text/plain": [
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+ ]
+ },
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+ },
+ {
+ "data": {
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+ "version_major": 2,
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+ },
+ "text/plain": [
+ "merges.txt: 0%| | 0.00/1.67M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "79a0537b94d640a48ed9ea959d1b83d3",
+ "version_major": 2,
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+ },
+ "text/plain": [
+ "tokenizer.json: 0%| | 0.00/7.03M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "5d5b83ff667745dd8504379eaa759cb7",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "chat_template.json: 0%| | 0.00/1.05k [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7JtKcuuXUGUT"
+ },
+ "source": [
+ "To evaluate the model's performance, we’ll use a sample from the dataset. First, let’s take a look at the internal structure of this sample.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "i-eIIdL9lqJJ",
+ "outputId": "02eda1d8-f6e8-43e7-85f4-b58e50370da3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]},\n",
+ " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Yes'}]}]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hLaWWJk_RkVU"
+ },
+ "source": [
+ "We’ll use the sample without the system message to assess the VLM's raw understanding. Here’s the input we will use:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ytnr1rePOamM",
+ "outputId": "b38d536e-bfa2-49e8-eb0f-22c1c1cbcff2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3IK2HOMuRtY_"
+ },
+ "source": [
+ "Now, let’s take a look at the chart corresponding to the sample. Can you answer the query based on the visual information?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376
+ },
+ "id": "QavnLzjJUbxf",
+ "outputId": "0b935e4d-3b13-4676-f3cc-1da64bc828ab"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gpLfsCUtUW6I"
+ },
+ "source": [
+ "Let’s create a method that takes the model, processor, and sample as inputs to generate the model's answer. This will allow us to streamline the inference process and easily evaluate the VLM's performance.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "_MoRTjFcE8qD"
+ },
+ "outputs": [],
+ "source": [
+ "from qwen_vl_utils import process_vision_info\n",
+ "\n",
+ "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
+ " # Prepare the text input by applying the chat template\n",
+ " text_input = processor.apply_chat_template(\n",
+ " sample[1:2], # Use the sample without the system message\n",
+ " tokenize=False,\n",
+ " add_generation_prompt=True\n",
+ " )\n",
+ "\n",
+ " # Process the visual input from the sample\n",
+ " image_inputs, _ = process_vision_info(sample)\n",
+ "\n",
+ " # Prepare the inputs for the model\n",
+ " model_inputs = processor(\n",
+ " text=[text_input],\n",
+ " images=image_inputs,\n",
+ " return_tensors=\"pt\",\n",
+ " ).to(device) # Move inputs to the specified device\n",
+ "\n",
+ " # Generate text with the model\n",
+ " generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)\n",
+ "\n",
+ " # Trim the generated ids to remove the input ids\n",
+ " trimmed_generated_ids = [\n",
+ " out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)\n",
+ " ]\n",
+ "\n",
+ " # Decode the output text\n",
+ " output_text = processor.batch_decode(\n",
+ " trimmed_generated_ids,\n",
+ " skip_special_tokens=True,\n",
+ " clean_up_tokenization_spaces=False\n",
+ " )\n",
+ "\n",
+ " return output_text[0] # Return the first decoded output text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "5UeNiMJC_uCk",
+ "outputId": "6b7c1260-9980-442f-9d55-a8d7ebedaf94"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Example of how to call the method with sample:\n",
+ "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ysh0e9DRUfF-"
+ },
+ "source": [
+ "While the model successfully retrieves the correct visual information, it struggles to answer the question accurately. This indicates that fine-tuning might be the key to enhancing its performance. Let’s proceed with the fine-tuning process!\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Sw3b76rawti6"
+ },
+ "source": [
+ "**Remove Model and Clean GPU**\n",
+ "\n",
+ "Before we proceed with training the model in the next section, let's clear the current variables and clean the GPU to free up resources.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dxkXZuUkvy8j"
+ },
+ "outputs": [],
+ "source": [
+ "import gc\n",
+ "import time\n",
+ "\n",
+ "def clear_memory():\n",
+ " # Delete variables if they exist in the current global scope\n",
+ " if 'inputs' in globals(): del globals()['inputs']\n",
+ " if 'model' in globals(): del globals()['model']\n",
+ " if 'processor' in globals(): del globals()['processor']\n",
+ " if 'trainer' in globals(): del globals()['trainer']\n",
+ " if 'peft_model' in globals(): del globals()['peft_model']\n",
+ " if 'bnb_config' in globals(): del globals()['bnb_config']\n",
+ " time.sleep(2)\n",
+ "\n",
+ " # Garbage collection and clearing CUDA memory\n",
+ " gc.collect()\n",
+ " time.sleep(2)\n",
+ " torch.cuda.empty_cache()\n",
+ " torch.cuda.synchronize()\n",
+ " time.sleep(2)\n",
+ " gc.collect()\n",
+ " time.sleep(2)\n",
+ "\n",
+ " print(f\"GPU allocated memory: {torch.cuda.memory_allocated() / 1024**3:.2f} GB\")\n",
+ " print(f\"GPU reserved memory: {torch.cuda.memory_reserved() / 1024**3:.2f} GB\")\n",
+ "\n",
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YIZOIVEzQqNg"
+ },
+ "source": [
+ "## 4. Fine-Tune the Model using TRL\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yIrR9gP2z90z"
+ },
+ "source": [
+ "### 4.1 Load the Quantized Model for Training ⚙️\n",
+ "\n",
+ "Next, we’ll load the quantized model using [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index). If you want to learn more about quantization, check out [this blog post](https://huggingface.co/blog/merve/quantization) or [this one](https://www.maartengrootendorst.com/blog/quantization/).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "b4e7dc3a0b6643a1b2c4124a11f1a932",
+ "1065fc621b1f4ae38e021e464ba28340",
+ "d66b6961a23f4c99872521d8b12b3c93",
+ "67c2b6c826b4437ca9f6f354af959e9d",
+ "dd37b7d9cd2a456986ac59ced8a35205",
+ "1bf49b9b487442df874672b30e0af8da",
+ "18e8ee9a09ab46c2833646651409b8b0",
+ "dd477ac59fa6470ca0ef1f59c39f43cb",
+ "df149f2746544b4a8c3cac6a2365e6db",
+ "1b60256c2fa440ccbb085ada32702b1e",
+ "7531040fc6c743e8bb85dcf07eb73560"
+ ]
+ },
+ "id": "zm_bJRrXsESg",
+ "outputId": "5a3ccdc6-9d40-43c8-df9e-9222a0656c2b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b4e7dc3a0b6643a1b2c4124a11f1a932",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from transformers import BitsAndBytesConfig\n",
+ "\n",
+ "# BitsAndBytesConfig int-4 config\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_use_double_quant=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=torch.bfloat16\n",
+ ")\n",
+ "\n",
+ "# Load model and tokenizer\n",
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ " quantization_config=bnb_config\n",
+ ")\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "65wfO29isQlX"
+ },
+ "source": [
+ "### 4.2 Set Up QLoRA and SFTConfig 🚀\n",
+ "\n",
+ "Next, we will configure [QLoRA](https://github.com/artidoro/qlora) for our training setup. QLoRA enables efficient fine-tuning of large language models while significantly reducing the memory footprint compared to traditional methods. Unlike standard LoRA, which reduces memory usage by applying a low-rank approximation, QLoRA takes it a step further by quantizing the weights of the LoRA adapters. This leads to even lower memory requirements and improved training efficiency, making it an excellent choice for optimizing our model's performance without sacrificing quality.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ITmkRHWCKYjf",
+ "outputId": "3ca824c9-4aca-4d5b-e942-7a1705939e08"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "trainable params: 2,523,136 || all params: 8,293,898,752 || trainable%: 0.0304\n"
+ ]
+ }
+ ],
+ "source": [
+ "from peft import LoraConfig, get_peft_model\n",
+ "\n",
+ "# Configure LoRA\n",
+ "peft_config = LoraConfig(\n",
+ " lora_alpha=16,\n",
+ " lora_dropout=0.05,\n",
+ " r=8,\n",
+ " bias=\"none\",\n",
+ " target_modules=[\"q_proj\", \"v_proj\"],\n",
+ " task_type=\"CAUSAL_LM\",\n",
+ ")\n",
+ "\n",
+ "# Apply PEFT model adaptation\n",
+ "peft_model = get_peft_model(model, peft_config)\n",
+ "\n",
+ "# Print trainable parameters\n",
+ "peft_model.print_trainable_parameters()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K5zzHM2GVtxD"
+ },
+ "source": [
+ "We will use Supervised Fine-Tuning (SFT) to refine our model’s performance on the task at hand. To do this, we'll define the training arguments using the [SFTConfig](https://huggingface.co/docs/trl/sft_trainer) class from the [TRL library](https://huggingface.co/docs/trl/index). SFT allows us to provide labeled data, helping the model learn to generate more accurate responses based on the input it receives. This approach ensures that the model is tailored to our specific use case, leading to better performance in understanding and responding to visual queries.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "SbqX1pQUKaSM"
+ },
+ "outputs": [],
+ "source": [
+ "from trl import SFTConfig\n",
+ "\n",
+ "# Configure training arguments\n",
+ "training_args = SFTConfig(\n",
+ " output_dir=\"qwen2-7b-instruct-trl-sft-ChartQA\", # Directory to save the model\n",
+ " num_train_epochs=3, # Number of training epochs\n",
+ " per_device_train_batch_size=4, # Batch size for training\n",
+ " per_device_eval_batch_size=4, # Batch size for evaluation\n",
+ " gradient_accumulation_steps=8, # Steps to accumulate gradients\n",
+ " gradient_checkpointing=True, # Enable gradient checkpointing for memory efficiency\n",
+ " # Optimizer and scheduler settings\n",
+ " optim=\"adamw_torch_fused\", # Optimizer type\n",
+ " learning_rate=2e-4, # Learning rate for training\n",
+ " lr_scheduler_type=\"constant\", # Type of learning rate scheduler\n",
+ " # Logging and evaluation\n",
+ " logging_steps=10, # Steps interval for logging\n",
+ " eval_steps=10, # Steps interval for evaluation\n",
+ " eval_strategy=\"steps\", # Strategy for evaluation\n",
+ " save_strategy=\"steps\", # Strategy for saving the model\n",
+ " save_steps=20, # Steps interval for saving\n",
+ " metric_for_best_model=\"eval_loss\", # Metric to evaluate the best model\n",
+ " greater_is_better=False, # Whether higher metric values are better\n",
+ " load_best_model_at_end=True, # Load the best model after training\n",
+ " # Mixed precision and gradient settings\n",
+ " bf16=True, # Use bfloat16 precision\n",
+ " tf32=True, # Use TensorFloat-32 precision\n",
+ " max_grad_norm=0.3, # Maximum norm for gradient clipping\n",
+ " warmup_ratio=0.03, # Ratio of total steps for warmup\n",
+ " # Hub and reporting\n",
+ " push_to_hub=True, # Whether to push model to Hugging Face Hub\n",
+ " report_to=\"wandb\", # Reporting tool for tracking metrics\n",
+ " # Gradient checkpointing settings\n",
+ " gradient_checkpointing_kwargs={\"use_reentrant\": False}, # Options for gradient checkpointing\n",
+ " # Dataset configuration\n",
+ " dataset_text_field=\"\", # Text field in dataset\n",
+ " dataset_kwargs={\"skip_prepare_dataset\": True}, # Additional dataset options\n",
+ " #max_seq_length=1024 # Maximum sequence length for input\n",
+ ")\n",
+ "\n",
+ "training_args.remove_unused_columns = False # Keep unused columns in dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pOUrD9P-y-Kf"
+ },
+ "source": [
+ "### 4.3 Training the Model 🏃"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wjQGt-iZVyef"
+ },
+ "source": [
+ "We will log our training progress using [Weights & Biases (W&B)](https://wandb.ai/). Let’s connect our notebook to W&B to capture essential information during training.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 228
+ },
+ "id": "ckVfXDWsoF4Y",
+ "outputId": "bb7ce99c-ed2b-481c-a11f-27272ce8da91"
+ },
+ "outputs": [],
+ "source": [
+ "import wandb\n",
+ "\n",
+ "wandb.init(\n",
+ " project=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
+ " name=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
+ " config=training_args,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ucTUbGURV2_-"
+ },
+ "source": [
+ "We need a collator function to properly retrieve and batch the data during the training procedure. This function will handle the formatting of our dataset inputs, ensuring they are correctly structured for the model. Let's define the collator function below.\n",
+ "\n",
+ "👉 Check out the TRL official example [scripts]( https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87) for more details.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "id": "pAzDovzylQeZ"
+ },
+ "outputs": [],
+ "source": [
+ "# Create a data collator to encode text and image pairs\n",
+ "def collate_fn(examples):\n",
+ " # Get the texts and images, and apply the chat template\n",
+ " texts = [processor.apply_chat_template(example, tokenize=False) for example in examples] # Prepare texts for processing\n",
+ " image_inputs = [process_vision_info(example)[0] for example in examples] # Process the images to extract inputs\n",
+ "\n",
+ " # Tokenize the texts and process the images\n",
+ " batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True) # Encode texts and images into tensors\n",
+ "\n",
+ " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
+ " labels = batch[\"input_ids\"].clone() # Clone input IDs for labels\n",
+ " labels[labels == processor.tokenizer.pad_token_id] = -100 # Mask padding tokens in labels\n",
+ "\n",
+ " # Ignore the image token index in the loss computation (model specific)\n",
+ " if isinstance(processor, Qwen2VLProcessor): # Check if the processor is Qwen2VLProcessor\n",
+ " image_tokens = [151652, 151653, 151655] # Specific image token IDs for Qwen2VLProcessor\n",
+ " else:\n",
+ " image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)] # Convert image token to ID\n",
+ "\n",
+ " # Mask image token IDs in the labels\n",
+ " for image_token_id in image_tokens:\n",
+ " labels[labels == image_token_id] = -100 # Mask image token IDs in labels\n",
+ "\n",
+ " batch[\"labels\"] = labels # Add labels to the batch\n",
+ "\n",
+ " return batch # Return the prepared batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "skbpTuJlV8qN"
+ },
+ "source": [
+ "Now, we will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "k_jk-U7ULYtA",
+ "outputId": "0dc465a9-1744-4b9a-d090-22a63f2e48de"
+ },
+ "outputs": [],
+ "source": [
+ "from trl import SFTTrainer\n",
+ "\n",
+ "trainer = SFTTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=eval_dataset,\n",
+ " data_collator=collate_fn,\n",
+ " peft_config=peft_config,\n",
+ " processing_class=processor.tokenizer,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NlDsh4WvWCx0"
+ },
+ "source": [
+ "Time to Train the Model! 🎉"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "p1rgMTBDLboO"
+ },
+ "outputs": [],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w6CykSCtX-Xa"
+ },
+ "source": [
+ "Let's save the results 💾"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "27d3420d65a545e29cbdae604caa32f3",
+ "37925f7df7324bcb8f7d07389730cf82",
+ "174ecb5e6cd54b2989887e08f7b7ac04",
+ "3b50f08adb95437ead16c8303c971470",
+ "8d9cf624237b4480a2e8b430c7a5e006",
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+ "7dece8a5ac7b4ecf91420f01d60f161b",
+ "e525f59c59f74a2c97ab8a72786127ed",
+ "32f97bb4d25d4d32958d35ac3e897ab9"
+ ]
+ },
+ "id": "tE8usZw0lgrL",
+ "outputId": "455a0714-04b7-4078-ca3f-ccba0ad01f13"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "27d3420d65a545e29cbdae604caa32f3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trainer.save_model(training_args.output_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6yx_sGW42dN3"
+ },
+ "source": [
+ "## 5. Testing the Fine-Tuned Model 🔍\n",
+ "\n",
+ "Now that we've successfully fine-tuned our Vision Language Model (VLM), it's time to evaluate its performance! In this section, we will test the model using examples from the ChartQA dataset to see how well it answers questions based on chart images. Let's dive in and explore the results! 🚀\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i0KEPu6qYKqn"
+ },
+ "source": [
+ "Let's clean up the GPU memory to ensure optimal performance 🧹"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ttx6EK8Uy8t0"
+ },
+ "outputs": [],
+ "source": [
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HwCTPHsfujn2"
+ },
+ "source": [
+ "We will reload the base model using the same pipeline as before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "dc179daa0be34359a3c9ec0224537c53",
+ "2b21097352c34b2e8e91f86e20bf834c",
+ "f3c4e0dd2e274dce8e3c74ca58796581",
+ "57e326d7a47242d09c6819ec367d87ed",
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+ "964f75e681d6498592ff634a22665a70"
+ ]
+ },
+ "id": "EFqTNUud2lA7",
+ "outputId": "32f3a882-0fed-4527-ca83-74857afe658a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "dc179daa0be34359a3c9ec0224537c53",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lRAPEYKkYSkB"
+ },
+ "source": [
+ "We will attach the trained adapter to the pretrained model. This adapter contains the fine-tuning adjustments we made during training, allowing the base model to leverage the new knowledge without altering its core parameters. By integrating the adapter, we can enhance the model's capabilities while maintaining its original structure.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "9f91c4c646c14451a8d9013ff7c8b754",
+ "1e3329ab17004575adf04fdfbadcf477",
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+ "ca03b3317b9c4700b9fb36a81a7928b7",
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+ "0fb1f35201994c61a18798a521f0808b"
+ ]
+ },
+ "id": "mQi2xBXk4sHe",
+ "outputId": "1096bbce-04e1-475d-c7ea-090ef2e5bf5b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9f91c4c646c14451a8d9013ff7c8b754",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_config.json: 0%| | 0.00/650 [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1a953c336a9049b3a46f1895bcc03ed4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adapter_path = \"sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA\"\n",
+ "model.load_adapter(adapter_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pqryChyLWRmR"
+ },
+ "source": [
+ "We will utilize the previous sample from the dataset that the model initially struggled to answer correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3X9YGJUezZr6",
+ "outputId": "0598cff6-dae8-4496-bef7-82e2015d12bf"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376
+ },
+ "id": "2hLJrxxTVn6x",
+ "outputId": "ee8fd396-f73b-4b8d-e10f-e4430a6a9b13"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "pdb9vErmzdAf",
+ "outputId": "e5059c15-c1ec-4f9b-d642-820cac72bb59"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Yes'"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "swibyq5AWctZ"
+ },
+ "source": [
+ "Since this sample is drawn from the training set, the model has encountered it during training, which may be seen as a form of cheating. To gain a more comprehensive understanding of the model's performance, we will also evaluate it using an unseen sample.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2fEa9ChjZsJw"
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "czZSBgnoef1E",
+ "outputId": "350d3520-4973-444e-f2bc-12e44d548fe6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'What is the value of Slovenia in the graph?'}]}]"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset[10][:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 617
+ },
+ "id": "ATuQ6ZS6eirO",
+ "outputId": "c3adc0fd-0fdc-4ff4-cc4e-14b4d9039323"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset[10][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "9yHJMKHNWcMc",
+ "outputId": "5cedc6aa-e375-4026-92f2-6be4d0e50d91"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'1'"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output = generate_text_from_sample(model, processor, test_dataset[10])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NUr6jmnAIlh1"
+ },
+ "source": [
+ "The model has successfully learned to respond to the queries as specified in the dataset. We've achieved our goal! 🎉✨"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z_Ns3p0Dhjbr"
+ },
+ "source": [
+ "💻 I’ve developed an example application to test the model, which you can find [here](https://huggingface.co/spaces/sergiopaniego/Qwen2-VL-7B-trl-sft-ChartQA). You can easily compare it with another Space featuring the pre-trained model, available [here](https://huggingface.co/spaces/GanymedeNil/Qwen2-VL-7B)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 822
+ },
+ "id": "dYJJ6ASKhJ5k",
+ "outputId": "f010e580-a4a1-470b-84c4-95ee999774b8"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import IFrame\n",
+ "\n",
+ "IFrame(src=\"https://sergiopaniego-qwen2-vl-7b-trl-sft-chartqa.hf.space\", width=1000, height=800)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "daUMWw5xxhSc"
+ },
+ "source": [
+ "## 6. Compare Fine-Tuned Model vs. Base Model + Prompting 📊\n",
+ "\n",
+ "We have explored how fine-tuning the VLM can be a valuable option for adapting it to our specific needs. Another approach to consider is directly using prompting or implementing a RAG system, which is covered in another [recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms).\n",
+ "\n",
+ "Fine-tuning a VLM requires significant amounts of data and computational resources, which can incur costs. In contrast, we can experiment with prompting to see if we can achieve similar results without the overhead of fine-tuning.\n",
+ "\n",
+ "Let's again clean up the GPU memory to ensure optimal performance 🧹"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ei-OZGGx4lHe",
+ "outputId": "81bee1a4-4860-464a-bc6a-ae2fb1695236"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GPU allocated memory: 0.02 GB\n",
+ "GPU reserved memory: 0.27 GB\n"
+ ]
+ }
+ ],
+ "source": [
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9NApMx5S4-sh"
+ },
+ "source": [
+ "🏗️ First, we will load the baseline model following the same pipeline as before.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
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+ "outputs": [
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+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1102501e7373424a8340e8b874ef616b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1bwatIlf5EDE"
+ },
+ "source": [
+ "📜 In this case, we will again use the previous sample, but this time we will include the system message as follows. This addition helps to contextualize the input for the model, potentially improving its response accuracy.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SNMKKvvZxqR8",
+ "outputId": "fefa3c3a-f666-4c8f-fdef-38b16539c069"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fou6XKGM5Uii"
+ },
+ "source": [
+ "Let's see how it performs!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "eN3NkkpgR4do",
+ "outputId": "5ed3fb26-580a-4c07-d626-cf8c66619b81"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Yes'"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text = processor.apply_chat_template(\n",
+ " train_dataset[0][:2], tokenize=False, add_generation_prompt=True\n",
+ ")\n",
+ "\n",
+ "image_inputs, _ = process_vision_info(train_dataset[0])\n",
+ "\n",
+ "inputs = processor(\n",
+ " text=[text],\n",
+ " images=image_inputs,\n",
+ " return_tensors=\"pt\",\n",
+ ")\n",
+ "\n",
+ "inputs = inputs.to(\"cuda\")\n",
+ "\n",
+ "generated_ids = model.generate(**inputs, max_new_tokens=1024)\n",
+ "generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
+ "\n",
+ "output_text = processor.batch_decode(\n",
+ " generated_ids_trimmed,\n",
+ " skip_special_tokens=True,\n",
+ " clean_up_tokenization_spaces=False\n",
+ ")\n",
+ "\n",
+ "output_text[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "o9Id3dzV5Wwy"
+ },
+ "source": [
+ "💡 As we can see, the model generates the correct answer using the pretrained model along with the additional system message, without any training. This approach may serve as a viable alternative to fine-tuning, depending on the specific use case."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wgv0-sy8TLPE"
+ },
+ "source": [
+ "## 7. Continuing the Learning Journey 🧑🎓️\n",
+ "\n",
+ "To further enhance your understanding and skills in working with multimodal models, check out the following resources:\n",
+ "\n",
+ "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)\n",
+ "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)\n",
+ "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main)\n",
+ "- [Quantize Your Qwen2-VL Model with AutoAWQ](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#quantize-your-own-model-with-autoawq)\n",
+ "- [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm)\n",
+ "- [Hugging Face Llama Recipes: SFT for VLM](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/sft_vlm.py)\n",
+ "- [Hugging Face Llama Recipes: PEFT Fine-Tuning](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/peft_finetuning.py)\n",
+ "- [Hugging Face Blog: IDEFICS2](https://huggingface.co/blog/idefics2)\n",
+ "\n",
+ "These resources will help you deepen your knowledge and skills in multimodal learning.\n",
+ "\n"
+ ]
+ }
+ ],
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From 245fb9e89f090f210fc958f6121c2de943c44ce2 Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:24:26 +0530
Subject: [PATCH 3/8] Delete notebooks/en/fine_tuning_vlm_trl.ipynb
---
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-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "vKadZFQ2IdJb"
- },
- "source": [
- "# Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)\n",
- "\n",
- "\n",
- "\n",
- "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JATmSI8mcyW2"
- },
- "source": [
- "🚨 **WARNING**: This notebook is resource-intensive and requires substantial computational power. If you’re running this in Colab, it will utilize an A100 GPU.\n",
- "\n",
- "In this recipe, we’ll demonstrate how to fine-tune a [Vision Language Model (VLM)](https://huggingface.co/blog/vlms) using the Hugging Face ecosystem, specifically with the [Transformer Reinforcement Learning library (TRL)](https://huggingface.co/docs/trl/index).\n",
- "\n",
- "**🌟 Model & Dataset Overview**\n",
- "\n",
- "We’ll be fine-tuning the [Qwen2-VL-7B](https://qwenlm.github.io/blog/qwen2-vl/) model on the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset. This dataset includes images of various chart types paired with question-answer pairs—ideal for enhancing the model's visual question-answering capabilities.\n",
- "\n",
- "**📖 Additional Resources**\n",
- "\n",
- "If you’re interested in more VLM applications, check out:\n",
- "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms): where I guide you through building a RAG system using Document Retrieval (ColPali) and Vision Language Models (VLMs).\n",
- "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl): an excellent deep dive into fine-tuning multimodal LLMs with TRL.\n",
- "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main): a collection of engaging notebooks on cutting-edge vision and multimodal AI topics.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "QoD6dxPeXDKR"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gSHmDKNFoqjC"
- },
- "source": [
- "## 1. Install Dependencies\n",
- "\n",
- "Let’s start by installing the essential libraries we’ll need for fine-tuning! 🚀\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "GCMhPmFdIGSb",
- "outputId": "016a9f29-9c8d-42c5-9187-813f5bdeb536"
- },
- "outputs": [],
- "source": [
- "!pip install -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
- "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "J4pAvoQaOJ1M"
- },
- "source": [
- "We’ll also need to install an earlier version of *PyTorch*, as the latest version has an issue that currently prevents this notebook from running correctly. You can learn more about the issue [here](https://github.com/pytorch/pytorch/issues/138340) and consider updating to the latest version once it’s resolved."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "D8iRteA4oXVj",
- "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
- },
- "outputs": [],
- "source": [
- "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "V0-2Lso6wkIh"
- },
- "source": [
- "Log in to Hugging Face to upload your fine-tuned model! 🗝️\n",
- "\n",
- "You’ll need to authenticate with your Hugging Face account to save and share your model directly from this notebook.\n"
- ]
- },
- {
- "cell_type": "code",
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- "text/plain": [
- "VBox(children=(HTML(value='
},\n",
- " {'type': 'text',\n",
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- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
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- },
- "source": [
- "## 3. Load Model and Check Performance! 🤔\n",
- "\n",
- "Now that we’ve loaded the dataset, let’s start by loading the model and evaluating its performance using a sample from the dataset. We’ll be using [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), a Vision Language Model (VLM) capable of understanding both visual data and text.\n",
- "\n",
- "If you're exploring alternatives, consider these open-source options:\n",
- "- Meta AI's [Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)\n",
- "- Mistral AI's [Pixtral-12B](https://huggingface.co/mistralai/Pixtral-12B-2409)\n",
- "- Allen AI's [Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924)\n",
- "\n",
- "Additionally, you can check the Leaderboards, such as the [WildVision Arena](https://huggingface.co/spaces/WildVision/vision-arena) or the [OpenVLM Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard), to find the best-performing VLMs.\n",
- "\n",
- "\n"
- ]
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- "outputs": [
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- "name": "stderr",
- "output_type": "stream",
- "text": [
- "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
- ]
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- "source": [
- "import torch\n",
- "from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor\n",
- "\n",
- "model_id = \"Qwen/Qwen2-VL-7B-Instruct\""
- ]
- },
- {
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- "source": [
- "Next, we’ll load the model and the tokenizer to prepare for inference."
- ]
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- {
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- "text": [
- "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
- ]
- },
- {
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- ]
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- },
- {
- "data": {
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- "version_major": 2,
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- "text/plain": [
- "tokenizer.json: 0%| | 0.00/7.03M [00:00, ?B/s]"
- ]
- },
- "metadata": {},
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- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
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- ]
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- ],
- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7JtKcuuXUGUT"
- },
- "source": [
- "To evaluate the model's performance, we’ll use a sample from the dataset. First, let’s take a look at the internal structure of this sample.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "i-eIIdL9lqJJ",
- "outputId": "02eda1d8-f6e8-43e7-85f4-b58e50370da3"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]},\n",
- " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Yes'}]}]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hLaWWJk_RkVU"
- },
- "source": [
- "We’ll use the sample without the system message to assess the VLM's raw understanding. Here’s the input we will use:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "Ytnr1rePOamM",
- "outputId": "b38d536e-bfa2-49e8-eb0f-22c1c1cbcff2"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1:2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "3IK2HOMuRtY_"
- },
- "source": [
- "Now, let’s take a look at the chart corresponding to the sample. Can you answer the query based on the visual information?\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 376
- },
- "id": "QavnLzjJUbxf",
- "outputId": "0b935e4d-3b13-4676-f3cc-1da64bc828ab"
- },
- "outputs": [
- {
- "data": {
- "image/jpeg": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "gpLfsCUtUW6I"
- },
- "source": [
- "Let’s create a method that takes the model, processor, and sample as inputs to generate the model's answer. This will allow us to streamline the inference process and easily evaluate the VLM's performance.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 25,
- "metadata": {
- "id": "_MoRTjFcE8qD"
- },
- "outputs": [],
- "source": [
- "from qwen_vl_utils import process_vision_info\n",
- "\n",
- "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
- " # Prepare the text input by applying the chat template\n",
- " text_input = processor.apply_chat_template(\n",
- " sample[1:2], # Use the sample without the system message\n",
- " tokenize=False,\n",
- " add_generation_prompt=True\n",
- " )\n",
- "\n",
- " # Process the visual input from the sample\n",
- " image_inputs, _ = process_vision_info(sample)\n",
- "\n",
- " # Prepare the inputs for the model\n",
- " model_inputs = processor(\n",
- " text=[text_input],\n",
- " images=image_inputs,\n",
- " return_tensors=\"pt\",\n",
- " ).to(device) # Move inputs to the specified device\n",
- "\n",
- " # Generate text with the model\n",
- " generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)\n",
- "\n",
- " # Trim the generated ids to remove the input ids\n",
- " trimmed_generated_ids = [\n",
- " out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)\n",
- " ]\n",
- "\n",
- " # Decode the output text\n",
- " output_text = processor.batch_decode(\n",
- " trimmed_generated_ids,\n",
- " skip_special_tokens=True,\n",
- " clean_up_tokenization_spaces=False\n",
- " )\n",
- "\n",
- " return output_text[0] # Return the first decoded output text"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "5UeNiMJC_uCk",
- "outputId": "6b7c1260-9980-442f-9d55-a8d7ebedaf94"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
- ]
- },
- "execution_count": 26,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Example of how to call the method with sample:\n",
- "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ysh0e9DRUfF-"
- },
- "source": [
- "While the model successfully retrieves the correct visual information, it struggles to answer the question accurately. This indicates that fine-tuning might be the key to enhancing its performance. Let’s proceed with the fine-tuning process!\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Sw3b76rawti6"
- },
- "source": [
- "**Remove Model and Clean GPU**\n",
- "\n",
- "Before we proceed with training the model in the next section, let's clear the current variables and clean the GPU to free up resources.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "dxkXZuUkvy8j"
- },
- "outputs": [],
- "source": [
- "import gc\n",
- "import time\n",
- "\n",
- "def clear_memory():\n",
- " # Delete variables if they exist in the current global scope\n",
- " if 'inputs' in globals(): del globals()['inputs']\n",
- " if 'model' in globals(): del globals()['model']\n",
- " if 'processor' in globals(): del globals()['processor']\n",
- " if 'trainer' in globals(): del globals()['trainer']\n",
- " if 'peft_model' in globals(): del globals()['peft_model']\n",
- " if 'bnb_config' in globals(): del globals()['bnb_config']\n",
- " time.sleep(2)\n",
- "\n",
- " # Garbage collection and clearing CUDA memory\n",
- " gc.collect()\n",
- " time.sleep(2)\n",
- " torch.cuda.empty_cache()\n",
- " torch.cuda.synchronize()\n",
- " time.sleep(2)\n",
- " gc.collect()\n",
- " time.sleep(2)\n",
- "\n",
- " print(f\"GPU allocated memory: {torch.cuda.memory_allocated() / 1024**3:.2f} GB\")\n",
- " print(f\"GPU reserved memory: {torch.cuda.memory_reserved() / 1024**3:.2f} GB\")\n",
- "\n",
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "YIZOIVEzQqNg"
- },
- "source": [
- "## 4. Fine-Tune the Model using TRL\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "yIrR9gP2z90z"
- },
- "source": [
- "### 4.1 Load the Quantized Model for Training ⚙️\n",
- "\n",
- "Next, we’ll load the quantized model using [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index). If you want to learn more about quantization, check out [this blog post](https://huggingface.co/blog/merve/quantization) or [this one](https://www.maartengrootendorst.com/blog/quantization/).\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 28,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "b4e7dc3a0b6643a1b2c4124a11f1a932",
- "1065fc621b1f4ae38e021e464ba28340",
- "d66b6961a23f4c99872521d8b12b3c93",
- "67c2b6c826b4437ca9f6f354af959e9d",
- "dd37b7d9cd2a456986ac59ced8a35205",
- "1bf49b9b487442df874672b30e0af8da",
- "18e8ee9a09ab46c2833646651409b8b0",
- "dd477ac59fa6470ca0ef1f59c39f43cb",
- "df149f2746544b4a8c3cac6a2365e6db",
- "1b60256c2fa440ccbb085ada32702b1e",
- "7531040fc6c743e8bb85dcf07eb73560"
- ]
- },
- "id": "zm_bJRrXsESg",
- "outputId": "5a3ccdc6-9d40-43c8-df9e-9222a0656c2b"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "b4e7dc3a0b6643a1b2c4124a11f1a932",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from transformers import BitsAndBytesConfig\n",
- "\n",
- "# BitsAndBytesConfig int-4 config\n",
- "bnb_config = BitsAndBytesConfig(\n",
- " load_in_4bit=True,\n",
- " bnb_4bit_use_double_quant=True,\n",
- " bnb_4bit_quant_type=\"nf4\",\n",
- " bnb_4bit_compute_dtype=torch.bfloat16\n",
- ")\n",
- "\n",
- "# Load model and tokenizer\n",
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- " quantization_config=bnb_config\n",
- ")\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "65wfO29isQlX"
- },
- "source": [
- "### 4.2 Set Up QLoRA and SFTConfig 🚀\n",
- "\n",
- "Next, we will configure [QLoRA](https://github.com/artidoro/qlora) for our training setup. QLoRA enables efficient fine-tuning of large language models while significantly reducing the memory footprint compared to traditional methods. Unlike standard LoRA, which reduces memory usage by applying a low-rank approximation, QLoRA takes it a step further by quantizing the weights of the LoRA adapters. This leads to even lower memory requirements and improved training efficiency, making it an excellent choice for optimizing our model's performance without sacrificing quality.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ITmkRHWCKYjf",
- "outputId": "3ca824c9-4aca-4d5b-e942-7a1705939e08"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "trainable params: 2,523,136 || all params: 8,293,898,752 || trainable%: 0.0304\n"
- ]
- }
- ],
- "source": [
- "from peft import LoraConfig, get_peft_model\n",
- "\n",
- "# Configure LoRA\n",
- "peft_config = LoraConfig(\n",
- " lora_alpha=16,\n",
- " lora_dropout=0.05,\n",
- " r=8,\n",
- " bias=\"none\",\n",
- " target_modules=[\"q_proj\", \"v_proj\"],\n",
- " task_type=\"CAUSAL_LM\",\n",
- ")\n",
- "\n",
- "# Apply PEFT model adaptation\n",
- "peft_model = get_peft_model(model, peft_config)\n",
- "\n",
- "# Print trainable parameters\n",
- "peft_model.print_trainable_parameters()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "K5zzHM2GVtxD"
- },
- "source": [
- "We will use Supervised Fine-Tuning (SFT) to refine our model’s performance on the task at hand. To do this, we'll define the training arguments using the [SFTConfig](https://huggingface.co/docs/trl/sft_trainer) class from the [TRL library](https://huggingface.co/docs/trl/index). SFT allows us to provide labeled data, helping the model learn to generate more accurate responses based on the input it receives. This approach ensures that the model is tailored to our specific use case, leading to better performance in understanding and responding to visual queries.\n",
- "\n",
- "\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "id": "SbqX1pQUKaSM"
- },
- "outputs": [],
- "source": [
- "from trl import SFTConfig\n",
- "\n",
- "# Configure training arguments\n",
- "training_args = SFTConfig(\n",
- " output_dir=\"qwen2-7b-instruct-trl-sft-ChartQA\", # Directory to save the model\n",
- " num_train_epochs=3, # Number of training epochs\n",
- " per_device_train_batch_size=4, # Batch size for training\n",
- " per_device_eval_batch_size=4, # Batch size for evaluation\n",
- " gradient_accumulation_steps=8, # Steps to accumulate gradients\n",
- " gradient_checkpointing=True, # Enable gradient checkpointing for memory efficiency\n",
- " # Optimizer and scheduler settings\n",
- " optim=\"adamw_torch_fused\", # Optimizer type\n",
- " learning_rate=2e-4, # Learning rate for training\n",
- " lr_scheduler_type=\"constant\", # Type of learning rate scheduler\n",
- " # Logging and evaluation\n",
- " logging_steps=10, # Steps interval for logging\n",
- " eval_steps=10, # Steps interval for evaluation\n",
- " eval_strategy=\"steps\", # Strategy for evaluation\n",
- " save_strategy=\"steps\", # Strategy for saving the model\n",
- " save_steps=20, # Steps interval for saving\n",
- " metric_for_best_model=\"eval_loss\", # Metric to evaluate the best model\n",
- " greater_is_better=False, # Whether higher metric values are better\n",
- " load_best_model_at_end=True, # Load the best model after training\n",
- " # Mixed precision and gradient settings\n",
- " bf16=True, # Use bfloat16 precision\n",
- " tf32=True, # Use TensorFloat-32 precision\n",
- " max_grad_norm=0.3, # Maximum norm for gradient clipping\n",
- " warmup_ratio=0.03, # Ratio of total steps for warmup\n",
- " # Hub and reporting\n",
- " push_to_hub=True, # Whether to push model to Hugging Face Hub\n",
- " report_to=\"wandb\", # Reporting tool for tracking metrics\n",
- " # Gradient checkpointing settings\n",
- " gradient_checkpointing_kwargs={\"use_reentrant\": False}, # Options for gradient checkpointing\n",
- " # Dataset configuration\n",
- " dataset_text_field=\"\", # Text field in dataset\n",
- " dataset_kwargs={\"skip_prepare_dataset\": True}, # Additional dataset options\n",
- " #max_seq_length=1024 # Maximum sequence length for input\n",
- ")\n",
- "\n",
- "training_args.remove_unused_columns = False # Keep unused columns in dataset"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pOUrD9P-y-Kf"
- },
- "source": [
- "### 4.3 Training the Model 🏃"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wjQGt-iZVyef"
- },
- "source": [
- "We will log our training progress using [Weights & Biases (W&B)](https://wandb.ai/). Let’s connect our notebook to W&B to capture essential information during training.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 228
- },
- "id": "ckVfXDWsoF4Y",
- "outputId": "bb7ce99c-ed2b-481c-a11f-27272ce8da91"
- },
- "outputs": [],
- "source": [
- "import wandb\n",
- "\n",
- "wandb.init(\n",
- " project=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
- " name=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
- " config=training_args,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "ucTUbGURV2_-"
- },
- "source": [
- "We need a collator function to properly retrieve and batch the data during the training procedure. This function will handle the formatting of our dataset inputs, ensuring they are correctly structured for the model. Let's define the collator function below.\n",
- "\n",
- "👉 Check out the TRL official example [scripts]( https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87) for more details.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 65,
- "metadata": {
- "id": "pAzDovzylQeZ"
- },
- "outputs": [],
- "source": [
- "# Create a data collator to encode text and image pairs\n",
- "def collate_fn(examples):\n",
- " # Get the texts and images, and apply the chat template\n",
- " texts = [processor.apply_chat_template(example, tokenize=False) for example in examples] # Prepare texts for processing\n",
- " image_inputs = [process_vision_info(example)[0] for example in examples] # Process the images to extract inputs\n",
- "\n",
- " # Tokenize the texts and process the images\n",
- " batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True) # Encode texts and images into tensors\n",
- "\n",
- " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
- " labels = batch[\"input_ids\"].clone() # Clone input IDs for labels\n",
- " labels[labels == processor.tokenizer.pad_token_id] = -100 # Mask padding tokens in labels\n",
- "\n",
- " # Ignore the image token index in the loss computation (model specific)\n",
- " if isinstance(processor, Qwen2VLProcessor): # Check if the processor is Qwen2VLProcessor\n",
- " image_tokens = [151652, 151653, 151655] # Specific image token IDs for Qwen2VLProcessor\n",
- " else:\n",
- " image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)] # Convert image token to ID\n",
- "\n",
- " # Mask image token IDs in the labels\n",
- " for image_token_id in image_tokens:\n",
- " labels[labels == image_token_id] = -100 # Mask image token IDs in labels\n",
- "\n",
- " batch[\"labels\"] = labels # Add labels to the batch\n",
- "\n",
- " return batch # Return the prepared batch"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "skbpTuJlV8qN"
- },
- "source": [
- "Now, we will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "k_jk-U7ULYtA",
- "outputId": "0dc465a9-1744-4b9a-d090-22a63f2e48de"
- },
- "outputs": [],
- "source": [
- "from trl import SFTTrainer\n",
- "\n",
- "trainer = SFTTrainer(\n",
- " model=model,\n",
- " args=training_args,\n",
- " train_dataset=train_dataset,\n",
- " eval_dataset=eval_dataset,\n",
- " data_collator=collate_fn,\n",
- " peft_config=peft_config,\n",
- " processing_class=processor.tokenizer,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NlDsh4WvWCx0"
- },
- "source": [
- "Time to Train the Model! 🎉"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "p1rgMTBDLboO"
- },
- "outputs": [],
- "source": [
- "trainer.train()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "w6CykSCtX-Xa"
- },
- "source": [
- "Let's save the results 💾"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "27d3420d65a545e29cbdae604caa32f3",
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- },
- "id": "tE8usZw0lgrL",
- "outputId": "455a0714-04b7-4078-ca3f-ccba0ad01f13"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "27d3420d65a545e29cbdae604caa32f3",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "trainer.save_model(training_args.output_dir)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6yx_sGW42dN3"
- },
- "source": [
- "## 5. Testing the Fine-Tuned Model 🔍\n",
- "\n",
- "Now that we've successfully fine-tuned our Vision Language Model (VLM), it's time to evaluate its performance! In this section, we will test the model using examples from the ChartQA dataset to see how well it answers questions based on chart images. Let's dive in and explore the results! 🚀\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "i0KEPu6qYKqn"
- },
- "source": [
- "Let's clean up the GPU memory to ensure optimal performance 🧹"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "Ttx6EK8Uy8t0"
- },
- "outputs": [],
- "source": [
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "HwCTPHsfujn2"
- },
- "source": [
- "We will reload the base model using the same pipeline as before."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 72,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 49,
- "referenced_widgets": [
- "dc179daa0be34359a3c9ec0224537c53",
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- ]
- },
- "id": "EFqTNUud2lA7",
- "outputId": "32f3a882-0fed-4527-ca83-74857afe658a"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "dc179daa0be34359a3c9ec0224537c53",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lRAPEYKkYSkB"
- },
- "source": [
- "We will attach the trained adapter to the pretrained model. This adapter contains the fine-tuning adjustments we made during training, allowing the base model to leverage the new knowledge without altering its core parameters. By integrating the adapter, we can enhance the model's capabilities while maintaining its original structure.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 73,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 81,
- "referenced_widgets": [
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- },
- "id": "mQi2xBXk4sHe",
- "outputId": "1096bbce-04e1-475d-c7ea-090ef2e5bf5b"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "9f91c4c646c14451a8d9013ff7c8b754",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_config.json: 0%| | 0.00/650 [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "1a953c336a9049b3a46f1895bcc03ed4",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "adapter_path = \"sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA\"\n",
- "model.load_adapter(adapter_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "pqryChyLWRmR"
- },
- "source": [
- "We will utilize the previous sample from the dataset that the model initially struggled to answer correctly."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 79,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "3X9YGJUezZr6",
- "outputId": "0598cff6-dae8-4496-bef7-82e2015d12bf"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 79,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][:2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 80,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 376
- },
- "id": "2hLJrxxTVn6x",
- "outputId": "ee8fd396-f73b-4b8d-e10f-e4430a6a9b13"
- },
- "outputs": [
- {
- "data": {
- "image/jpeg": 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",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 80,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 81,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "pdb9vErmzdAf",
- "outputId": "e5059c15-c1ec-4f9b-d642-820cac72bb59"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'Yes'"
- ]
- },
- "execution_count": 81,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "swibyq5AWctZ"
- },
- "source": [
- "Since this sample is drawn from the training set, the model has encountered it during training, which may be seen as a form of cheating. To gain a more comprehensive understanding of the model's performance, we will also evaluate it using an unseen sample.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2fEa9ChjZsJw"
- },
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 82,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "czZSBgnoef1E",
- "outputId": "350d3520-4973-444e-f2bc-12e44d548fe6"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'What is the value of Slovenia in the graph?'}]}]"
- ]
- },
- "execution_count": 82,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test_dataset[10][:2]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 83,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 617
- },
- "id": "ATuQ6ZS6eirO",
- "outputId": "c3adc0fd-0fdc-4ff4-cc4e-14b4d9039323"
- },
- "outputs": [
- {
- "data": {
- "image/png": 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9uuh7zytu/8279v7uLgh8IvGUin0EUbff+NN7T6c+U/y7D91dyuTJGInAi0DpJS/6puKejx9sh7b/nz/0xrIOT7besbfeZ3rWJsK4i9lniju9DAjcheKkwnu7HDjcxWagTRMDIAcmd7TiDnvoFHdyonwEJWxHGV5lSw9I2qt6dzhksIzXDmk7TaEbd//yxCCdD3BxwnjwwQfz4uU2T0pI1OOii7tvpGifV2LyFFPGRpCQ59dtpyc4ykRfqvR+4QtfeISY0GloJkc03GJHlU9RjddnOIGnswwdf/zxLSS2K8Irg6QLL7zwiAq8rkTioiT1vyMK1uxgdkwumsLnoxg+EvKi/fwVirAtxwwnOP7iAix8EnmUg08EZ12Pw9CJZciIffh0tBUXCeTdcccdZZEIhKI8S57m5gk5yGYMOOmkk/az82NtP+NrK3XHVvhEfkFNNW54NL1yRZm2xwGy4I/udccM8jZJcUOJV1XTxE9RkIJBXEDGzSvyVvGjTNtEwABPfClsHnVph6dfJAKcPLX1m7xe03b6anlaLjik/tj2GMrl5Pyi3+lYH7Lx9bx8HKOp3OBYNSbHsfHxj388rVKuh51pIz2PHFHwaztyXdJyMZbFjSbKrnP8cQ6JdgjQeB25qs9p203rmx63TbIjr60vdh3DYlzN5dNunGsJ6CNxk4PxjqCE44rA7Ru/8RuPOLaifLoM3w+fSPPimijdt+k6T5t5PRIb89SVm3o/93M/1/hEf1WbwYmb06bhCbT5JqlNGV7Ve+3/8D8VP7P3ZCte10N7XukjsCFAitf6bv+Nu8snUic9/zllkEUe9XmtkICIJ2E3v/83y+2LLv/Z8glT6ECARf673/uBA3B4CsV+ZPCX5x8ovMbG0WvU2a+SXwTsZyQr6XvGye7ywOdx9aoUJ4eqg5+6DDZclPDonUCIAINBhjs5vCLBiZq7bOgag/d3f/d3F//6X//rciBiMCJI4+kXJ7woU6UXJzOCOlJdv9BlqJS2HwFC2lacjLg7ua2U6rQLJuv2k8CaPxIXT3/yJ3+yfyG/rsy0Xrx+kAZqkR92YpsTQpvjKOqy5G46T7t4RY+TKv7NDQDkhv9G+5zM6hL9/tSnPlVmx8VelEVW+r1V1+Mw5LRdokscO6effnrbavs3GKiAL/KEpe7VqFVCYceFLWMHF/bhHwSsXKwF2yo5XY6DYLkqMKtqp+0+niaT8kA69XvyGTO5aUUiCCfIiwvlcueG/+LJVwQqubgIAuIiL8/ve5vXnxjz44ZDyOfVKBJvAERqewylx3PUXbWMG251XPL6wRHd68bZ/ElqLmPT7RjL0CFS3OBje9PjL2R2XW5y3HZtq6n8OmNYjAWM5/xVpTQAp6+8Hsu4zviM/7S5jkrlVt0wbHMOqvM7zh1VOvDaZRu5qW6r1lfN7reqvvndCDzy+UdXVmhT5q8+9Xe1cghseBJF+nd7T51CHvvj9T7WScwYSDAWKYIynoqRYjvyWfJUjECLp2dDpaM3FRxPhLh45xWJPHGSZoBNP6TkOxxO4G0Sj9S7JA5c5PPqEhdm3PXnj4Odj4AZiPjj/Xi+0eCkQOAVj535TqEqSEGH9K5NXHx00W3Tsmn7m8rqq36q0y6YbNIPbM/Tuy984QvlkyNez+R1mz5SBNx9yMplxHHGExB8nAt//tA/Lv6j/VUns7hAy9vIt7seh3n9VdtchETi+GybuCCOSTK4qcJT2QiG2spIyxGQxk0YOKMLr87UXURE3S7HQVzwdulntNN2GfZvU5473jwhIXCAHZNppBfLbWTUlUm51JXZ5n7edviDP/iDMtjmSTznC8YAeHHHPLVJMFx1DK2jf9w0qLqorZIXHOsuWqvqbGNf38ffujqve9yu215VvXXGsBhX8b0Y16tkp/vwWcZ6xqmon+avuz70tdy6ellvtwRu2Xv97scvu+iIQITghNfvfvZffj2wWUdTXhv8wFt/qqzKa32ReJWPVwIJrN5z8+3lbp5exdMndsT6I59/rMyv+hfBFWUjSKsqt8m+Z21Smbrx6ggn3vwuX8gmMIlX0TgRtA2iqL/OHQjk8xoDH2Iz4PBaCfql7xWjE3dReHeXjzhjEKmaSCH6Ed9hxPa2l7tuv6q/Y9SpSs98HwETf3wQjK9wgbXOneVcbmzjc0MmTrrckeSmQXw/xImVVz1Ibdtva791jsNSkZb/2uqRimO84Zsi7uRynHM8M76kF8Np+Tbr+AAyuIjm6QwXR1ysrPKNLvoPzZJ+trU/QQR3t3n1hskmCMTrbiS14ZeX6cIlrzvUNn7Cnfx49YhvDjkH5BeybRmuo2dX2WPkOMTxtw5L6qx73K7bXlW9dWy0zljAmMQTXMYpbmDnnzFU6dZm39DXcm10sMz4CLzjf7mxeN2555QTRcRU5XyzxIx9vErHa3bxtIfX8EixbNMbnjZ97guP7T1t+nt7r93dvV+F/fF6X+y/+f2/UTD5RQRQvLL3n/YCsZj1b79ytkIAFr8pxRMq9O8zbRxIcScoTgpV77lvqmy8u1/3uhozAJFe8YpXlMv48JQNLgi4MOACAR156kTiApqnZCQGYC6iuZiOC6j0neSy0Nf+IS/6mn+fkpYbaj1tv0rHuCPGq4rbSqlOu2CyTj9hR2BN8MykBkMkPqImVT3xCTvhS11ffUB3gv9I3DSgD9w1J3G3nRTtxytE5c6Kf/HqV9ONEKp1PQ4rmmrclfpRlW9XVWa8IeAhkOTY7Sul3xrSRmw3yU/1X3UcrGLZ1E7bvLb2j9eJCEg3CUDr9Er9q6pMvLbEK3fbSoz/zMLIE3T+4kZK3n5bhnm9Ntshu2p8qKqfcqy7YVlVr8998RpwnF+GOv7W1TmOU24OtD1u122rql46BrQdw2IsYPxtk7A9k44waRBv2HAO4QZam/bi+8g6nxv6Wq5N/1aVCU7pK7ir6pi/GQGe4nzrP7i0DHb4/ain/+LXi/f81A+Xr+Hx3VOkt/5P15cTQTxyz6+Vr+rxml7bRNDEbH35EyP2p6/78Vof5f7qd24p9fiOvYkwLvt/fX2yubr2+JaK2QXRne+suuhWJzPdv3Egxck3ZrvjpByOnjbCevobAnle0zbfMzFYIDcuQKM8gwevSPABIoMYiQsrBtI0oSN3IONDRfKqpl6O/KaDlO9TSDGVZ7nxtX8McukH/2leX+vRfnxMnsqNbxv+/t//++nuwddDp6GZxIl80w6FnPiYt608vsNrm+IJLHe78xSzyfEaVSR8mwAp993IT5fcEMhPnBGQxcky2sdPqi68aAsZHDcElCQuPvLEcYdOXY/DXE6b7fCj+BB/VZ2wR3xns6p823yCMsYcbt7Ar22QFvqvOg5SlnFDZ5Vu8UQsvuVJy1e93sM3nyS+F8vtzzbBBD8fESnkx/aqZVu98a/gl58b0INvuWD97d/+zJ3MVe1W5Xc5dmgTPeJVuSp5sa/tMRTluyxDNuNDbp8qOSlHJijIE/bApn2lKp1iIoXQfajjr6kPVf4f5dc9bqN+H8sYA9qOYTEWMNZUHVO8YZCeE5jplYCR4zW99vrlX/7llX508sknN3YxlTfEtVxj4x0yGS/QNRKMOJ9V8YsyLjcjQIBDwPIN5/xAceil378XWF1y4FslpPNkinz+mNiB4IUnVfylAVeVJsimfJ6q9iM79KBOvLoXk1mEDIKuaJcnVuiM7sgkL/3WKuqsu9w4kKJh7orzihEOzmDOgZ8GPdyh5Qdyq1JaLl5LSstxwPA9E7J5shTluQjktzPYn0+nySCADnEyoCwnz5jeGvlsc/CFPJac1HhFKD1IU11Y564NARcXs/xeRFoffeK7CspGHoN/euGLXvGEjZNR6EmduEOLflGf/ZGQzx14vvsKXtRHF+qQl746md4dz3WIC7D8YjsCjSq94wkgTzpC76GZRHBAf+lD2o/gki5TbsEozY+LNniFLJYRnNLH6Bv1YqITviGBJ2Ujn2Vw5OQZ+wlseIWIQB/bxH70wT/xofRVIoIrfIq8Kp1T/VnH19A/Ev7OsYAtSLSPL9A+ZeMkgx7ow2soEXzxXSB187bRg+Acmesch3W+jH3igii9O5r6UXr80s8ISDlugmUEC+SFzdE5LvBiGYzq9In8dBlBbrzukubVraf6N40NsIwn57/yK7+y74P0gXqR0mOXCyd8Jr1RFOX5xo+UPn3kohL7Ux77Bx+WTHfPxRivtUYQDW8S+bwqhD+Qwm/Kjb1/cbMJX+c4SPOjjfS4oh53zqnHq6hxvFEWvdAPFmkgV2cn6lT5TZdjB/bowrHGd2/xhw6cu9Jjr8sxFH2vGjOrxvpUNq+cR334MJssCVmxn22+XcMu/IYgesZxQB2eqpEfqY5h5K9apq8JowPnSsYSxrQYN7oef9GXnFGqS+idHtPkx81N/IW+0+eQl9Zf57iFY9UYjtzQB59O22O9yhfTMaDNGJaOBXxWkB7z9JM/ZNIeNuDGQzpJDef6+CyB4zrVMeXCejzVDH/M89ne5FqO+ul4znr4KHl1CVvGuYxlOqakdShHiqe5kcfxwPHc9FlGlHUpgSEIHHp6L/UlmIOGu2Uc/OnBykmdi2GeCqUX+Qw0XLzlqWrSAgYIggcGEgZTBg8+Fk5/0R05XBTyl05uwYmTICra5mKFkwC/kJ3K46IpvbjN9Ypt+sndewZ7Ti4kXnfgTl2cZDgpx+AQ9figlMGsaiY1+lz1QXvdR6jIhl9whjEnkegjba6jQ1WdeG2sTm/aGopJyOYkETxz1pSJ1NankMXFAvbDl8L2XESynwsWLq7gSor9rONP+AkXf01MKMvgz0UnAz2JusjM/YwTaPxeWp3Nqc9xwKsd2JqbE2F/mPA7bPFklrKk3E9C9+jXM6WeuWBGz1XHQ9vjsMqX8SNOrvkxjy5coJHCj5hIg+M89OWCJq0XjLALT12iLMc5s/6Fv2BbxgN+8DJPISPfzzb9ZNpeLl7Si/yqsum+0L9pbIjy+AYXzdgIf2OGUYIf2k1TjIdcYMQEOeRTlj/8IY4N9kd51hmL0/E49xNkEszhR6EDYyrfhxH4wJWbSzGJCeW5aOe4ifJVOtB2yjfnQl0uiHKfXcdv2h476ESKYyhl9kzOM/9Tvdmz6hiqGjOR0TTWR3vIzschbtwwI2ek9PjIj7/UBuGnVQzzPoXsfBnjJ35CSset9Bwa9doef6nfRt20X+yr0jud6ANW4ZMc18xWGn4ZMrsetxyDdWN4nT59j2Honp8n4pxEEEWwlesSx3i+H1mRx3qeGAs5dgnekVuXOF67XMvVcUR+asO0vaY6uW9QL87BnCvIj5Qef019j/IuVxPgyc2S0vf9/e8s/s9br127y70GUmtrYUUJSKAkQJDPq2p5oCWe7RLggoyTch7UbFcLW+tCoM2xExdjTC7Bt7Fp4sKOp2QkLtiXmCKQaht4jY2Rx22zRQhGCTyq/L+55m5zCex+8id/slSCGzl5igDaQCons95210CKCRxI8Zpdm1aZyjxNTBqxq7RpIPWsXSluuxKQwEECnAx4IhQ/2Hsw161tEuDpd3wLss12bWs9Am2OHZ6m8fSHpy15EEWrvE3AWxM8hTNNk4DHbbPdeJLDH7+jyfEwlcS3YTxJ4ze0qhJvCPFk3LQbAjf/r1cV/HVJTFzxs+94S/n3np+6opwIgunOY0a+JlnMwBfBW1O5beUZSG2LtO1IoIEAF4J8A5h/L9JQxaweCXCXNiaKwRb81lO8pttjM4oagEDbY6fNBBN8H5m+NjSAuorskYDHbXeYfLPIN2d8U8QTqjEnjm1eryZATr/PS3Xm1WVeReaVZNNuCDCBA39dE5NB8PeK//ZHipNe8aZyunOmVV+V+KHeMQVSR69S2HwJSGB4ArySwOt8XsQNz7qqhbig4M4m3wsxvbBpGgTaHjtcPPLtCd/98B0ST37juyLuzvNd7ec///nym7pp9LxfLXl9Ko4DjgF+UiT/5rLfFjeXFvp63LZnybdR3DSCHa9y8v3pWF8l/4mf+Iny29G6b1V5HZef/KBc0zdf7elYch0CEdTwat9P700v/p6bbt/7sd7Xl0+XmGp81e880WbMDPhXv3Nr8Y4femPx7vd+oPw9qngFMOSwfc5/+dLyjxkeeCWQ9vlxYFKUKze29M9vpLYE2mYkIIHxEohvQ2JyirFfQI6X5Lg1I1jA1tzBjola0JhJTZgNjQvKJV6Qhf/n1hv7t1Kht8dtbjm3JbA+ga7fSPHbTCSmFOe3mm75tb0f6SXA+eb/ouBHc7/jgh894vspylW18+5/ekXxim97afmE6/Ybf7p4900fKAMyfogXOTyNIlijDQK3m2nr//hf99r+30sdbvoXVxUvec2lR7RXZtb82/QbKZ9I1YB1twQksBwCXECP9a7scqwwfE8JkpjpLZ/tbfiWx93CVP1/qnqP2xvUTgKbESiDm73fjyJd9H3n7QVGp7cObOJHeQmSeOUv0kXfe24ph2CNQCptg9+IikSgRZs80dpWWlwgxeNspj3nN4GYoSle7dgWcNuRgAQkIAEJSEACEpDA3AkQGL1i71W823/jrlZd5TW9Rz7/WFmW9f9uL4DiyRav89XJycvxI8DbTM/aZmO7bIvfGuE3FJi16WUve5lB1C6NYdsSkIAEJCABCUhAAhL4GgFm7CNwuv037yqfKvFq3yf+5rPFe26+vbjn//qLSk48fWpTrrJyTzsX8USKp1DM7sP0mPkP+PbEUTESkIAEJCABCUhAAhKQQEcCBFB8a/Wffv9Pi5vfz3dP/6J8isWTLAKsF+89lcoT+3nljzJN5fJ6fW/PPpCKIIrfDrnkkq+/R9k3SOVJQAISkIAEJCABCUhAAqsJMOFEJJ44EQzxDRTp3Xsz/zHxBLP0fe4Lj5Wz80VZZgX8wA0/tbf/8b1JKa7b+w2rq/fLMWvftlNvs/Yxew5BS8yExCxITOX8ute9rnIWJH4fgN8GuOeee8ofWqPj1OHj0bopoJlxiWlO03aYref888+vbIc2mBaTxG/0LHE2prLz/pOABCQgAQlIQAISkMAKAlWz6a2oMunsUczax28SkN7ylreUP2JJwMMvUd98881l0JPP8c/3Su95z3vKOj/+4z9evPzlLy+iTryClz89Iij6+Z//+TLo+pEf+ZH93w3gx9h+6Zd+qbIdAjV+pf7Nb36zQVRJ238SkIAEJCABCUhAAhKQQB8ENp5sgkCGp1D8BscZZ5xR6sSTHwIhnhaR9/u///v7uhIwEUQR4BBgEUSRog5PowiAeMKVpve+971lEMXvWrz2ta/dD4xYZx/t/OIv/mJapfid3/mdcvucc84p5THZxMUXX1y87W1vK2699dYyeDtQwQ0JSEACEpCABCQgAQlIQAItCGwcSPGr0qS//Mu/PKK5F77wheW+KMMGQRVBFK/xReCVVvz+7//+cvMDH/j6HPD86jyv85F4VTBPsY8ylCWxpB3Shz70ofJX2q+//vrilltuKX8pm2CN6c8J7EwSkIAEJCABCUhAAhKQgAS6ENh4sonXvOY1ZZBDYFSXXvCCF+xnRVDFE6yqdPrpp5e7CYIeeOCBMtj64z/+43IfbVR958Q+8ngqRdlTTz21ePzxx/fFX3HFFfvr8eTrs5/9bKk331z5Q5z7eFyRgAQkIAEJSEACElgogY+dcN+iev70k8/dqL8bP5HiVbzbbrvtiGCEJz3xFOm8887bV/LTn/50uV4VEJGR7ue7KNKDDz5YLp/znOeUy6p/kRdl77uv2RFe+cpXlmLSJ19Vct0nAQlIQAISkIAEJCABCcyPwHNPfPZGndr4iVRV6wRAzJL3/Oc/v/weqeoVvqp6fe77lm/5lkZxL3rRi8r8eP2vsbCZEpCABCQgAQlIQAISkIAEEgK9BlI8heJVuQ9/+MMFEzz82I/9WHH48OGkuaLgu6lPfvKTtd8mpd8sRd1TTjmllPHoo48ekJVuRF6UPf7449Ns1yUgAQlIQAISkIAEJCCBBgIv/J9/piF3flnHfNOpG3Wqt0CKKc3f//73l78B9XM/93MHXtFLNeSVOl75+9M//dPiB37gB9Kscv3P//zPyyVPs+JJ1ite8YpyH99AEWilr/+RwT7ySFGWusjgiRO6xeyAZaG9f/HqX9O3XVHWpQQkIAEJSEACEpCABOZO4PD/cMncu9hr/zb+Roog5oYbbij+6I/+qJzOnIkb8kAn1fi7v/u7ywCHwIfJJPL067/+zC8dv/GNb9zPYvKI+JFennjlKfZRhrKRQsbv/u7vxq79JU/NSG9605v297kiAQlIQAISkIAEJCABCUigDYGNn0gxnfhv/dZvlW0xpXhduvbaa8snTARZ/I4TP7zL9OP8lhRPjwjI+BFfnlYREOUz6f3gD/5gce+995Y/8nvCCSeUvyVFW/yOFT/8y29W/dAP/dCB5pGBPPTjmym2ox1eL+SHevMnVQcEuCEBCUhAAhKQgAQkIAEJSKCCwKGn91LF/ta7CIhidr6mShFIRRkmpCDw4kdzY8IHAiimU4+nT1E2lgRB8Q0WgRCJV/Moz29J1T0J48d90TFe//ue7/me4vWvf/3+q4Mh36UEJCABCUhAAhKQgAQkIIE2BDYOpNo0YhkJSEACEpCABCQgAQlIQAJzIrDxN1JzgmFfJCABCUhAAhKQgAQkIAEJtCFgINWGkmUkIAEJSEACEpCABCQgAQkkBAykEhiuSkACEpCABCQgAQlIQAISaEPAQKoNJctIQAISkIAEJCABCUhAAhJICBhIJTBclYAEJCABCUhAAhKQgAQk0IaAgVQbSpaRgAQkIAEJSEACEpCABCSQEDCQSmC4KgEJSEACEpCABCQgAQlIoA0BA6k2lCwjAQlIQAISkIAEJCABCUggIWAglcBwVQISkIAEJCABCUhAAhKQQBsCBlJtKFlGAhKQgAQkIAEJSEACEpBAQmDjQOqaa64pLr/88iP+2N8l3X333UVa58477yze9a53dRHRqewtt9yyr/NDDz10RN1V+UdU6HHH/fffX+rWo8hFi8I/YbqLhE/j232mXOYm/UO3OH771rPPPg8hK46zquN/iPYYU/gbS2J8ZZw1bU5g6PNVk4b4L8dwn36cy4xjpUmPprxdnk+b9NpGHr6RXttso81oIz/G83NHlIvlJueSkNF1ualvpe110X/T8Tj0rjvucvapnn2s09c4Z3cZf5oY5b7aVHbTPuT8h+a1qb5N9Y9uymybd9lllxXnnnvugeIcsIC69NJLD+xvu3HBBRe0LbpWubvuuquo0juErcqPci4lsGsCN95449oqMBCfd955ax+nazdsRQlIYDIEzjzzzGKTccbz6TRMvYmNp9HDeWrZ5Xq5i427lJ0n2Xa96iWQqmrqrW99a3HdddcduEAj4rz33nv3i5999tnFlVdeWUbVN998c7mfCPjaa68t9913331lPhlc8EUZtq+++uqCwb0uNZWnDRLyKIcOaarK565DelfpwgsvLMJ5uTNBX9l3xx13lKK4OOWP/aSTTz657Fe5sfePyD/Kxr46p21qO+qmy7TvMCadddZZpb7oetNNNxWnnHJKaYvox6o2ctulujblpXrFOhzjLk74QOTBPuXI/rQt8uHKiTnqrtI9ZLPM5YddwuaUwf/YH2ldfaMefoYvV91UqOpP+FO0zzL8vUomMiKfslGG9WDEep5Su8EzODfVr9I3l5v7dqobZZvkr8rn5gy6Rgqd2aY/+Hl6XOVtp33Gj/KU+gF5cXywTl38Itonj3X8JRJ9J8XYEPtTvRkDo066n7Jpf6JuLKv6hxxkxLia3xxq6k/IjWU6brAvZxflYpnqHsds1IEDx2Wwiv2r2kj1zX23KS90imXTmBDHV+hMnbStujFyle7RdpX8VeeDdfVN63Fc5fZHp7r+pMcC5YJBlUzOF5zLwj/TMtRNjxO20xR2S8+3TfXr9E1lsp7rH7qR1yS/TX7TGEV/Ut9BXto229Fn1vNxJvyDvEhxfLBN3fQcxz7GnfT8Qd/RoekaiHppij6tOh+FLrmeTTamnabjI7dV+FqqX6w32Q42oR/lGWfyMTjksEztwHb0IR27uozHaT9yuyK/KqU6xLVFld51546QSX6c2/K2yYvrZdrLxwH0jmvAnGGqXy43LRsyQgf0Sm3BdvgY63GM5McGeXX8yUv1yeWn9ShbJZv9W09Pb5je+c53Pr3nyEdIYd/b3/72/f17B+/T119//f72ntHL/KjLElmR9oy1Xz5kPfjgg2V2vh11Ypnn59uUQzf216U0n3bZRudI6IqOpOgLfUy30/6k5UOfsvDX/iE/r0/WqrZTGayHLqErOiI71zW2qRNtBI98m7Kp7VJbNuUhO08pB/JSWWyja8qNdoNL5Ke65Lrm21EneOTy2eaPeqRcn031pX5wLRvI/tF2VX9CX4qTnzLJZSIjypOX8sq3s+ZL2U3l8/q5vrk89KBMpNzXkZf6XhXvXJ8oz37qR8q34UTbYUvqpeXJT1mTl5bPdaN+mh/yo/3wtWDPfmRE+1Euluib9i3XP9+OerGM9kN+bId/rWJd1Z9gm9et6lvowTJkxb7QJVhEfmxTLtoI/fNtZIQ+lE/t0ZRH2TRV6Z7KCh9NbYGd0YcU+akuua75NmXRMa0f8kMeOkRK9dlU36gfXKONWEb7aX/QLfSlXJQJBrnMyKds5OVlY5syeUr5rqofbaX65vJS3uSl/Qn5yIlUxTv0zctTNmxH/XybvrAvEhzT8uSH7iE7ysd2qhv1Ix+Z1E9tg57si4SMtHzsr1oiJ3Qhn3rR76rytBO6petVeqf1Q0fKkWKb9dQ2bId9Q4/YJi/aibzYrtKJ8vQtZZHqzP6076yTHzqiV2q3XM+0D7QFy9QuyE/lUSZNlE31ifYpk/eLfcgL3dhOU1qX/eiK7OBEfuiW9yPaCtmpTqxTlxTl6nhGf0IO9dKy5IcOyEM2f3UJPfmLtEp+3q/cPiFnF8uNv5Gqi/z2IB+4E8PdlPTJT5c7KdzpILqNpwS8RsgdjYjgcx26ls/r59vII1JPdUafPUMeKBp3oaMcZSKhL3daSOifR9LkV6W2bUdddEp1RafgFmVYpq9i0gbtxz7Kx90EyoberJNSWzblPVP66/+5w0UKTrHOHaFUTsqNuyhpHnVCT9ZX6U6ZPKXy6Te8ghHt7Q0UZZW+9M3bz7fT/qAHvhE+RFl0apPQF1bpnUueDOMTOcMqeW3rp/rmcoJd7Kds+PoqnlXtcwcv2qMf9CcS/aRf+ECk1JbpMUc5/Cxlk/oB9Wkr9c2qYxL5kbAVZeJpEG1w5z58KcrVLdv0J6+b9g+/oK3gE34Stm7Tn5APQ+5iRkIubaFjVWJ/Wj7lGuWRkfoxbcA8+KA3/GIcz30n1b8pL9qLJe2ge9o27eZ9yW0d3EJOcGV7le5RJ12G/NAj9bfUN/vSN227aj3tD/ba5HyM/iEPe9I3zvltEv1tUz/kV8nMbZX2ZxXPqvYZow4fPlw+vUN26s9VY2hqS4670AfZ8Ajbs56PGW3G97TvsR7j515QcUBmFZ9N90V/Qg79yPWOPJb0GyaUI6Ez5YNlW19rsk0puMO/dPygGj7XlBgfUrvTB/qDTvRj1fmjSnY6DuETIY8l+rQ9d+TjbfhXXZshl/xgGraJOuxnX8hiPfXVKJcuyQ856B9+UsUnPT+kMprW6+RTp8k+TTK3kdfLq317kWLBX5oAkjpl5KWPJ2PfqiUn0RhMoiyDFwNKVepavkpGuo92cMz8RJyWYT0cLN9ft83AGK/+UabKidu2HW3g0HFBFfu4uMtTqit10m3KcpDEiZEDjUe28ciVASrKN+XlbcKQtkJOmv/www/vy8z1zS+i0vxVuqdtxHpaP/ZVLfvSt0p2uq9On5RT8E7r5etwysvFRVzKN68X223r1+mLHI5T/CZ0ZzCNY3cVz6r2kUmfsDOJi500pYM5+/P+s4+69J+U5ufHSVlg71/6+kbsi2Van330jRMSiWWdzLJA8q9tf5Iq5Wrefp5ftd3UnyiPbfjLx3H4ViX0T/2gSq80HxnYN3whZKbjOBditI//II9xJlJTXpSJZdsxM9c5bBJy0vxVukeddJnWT/fn633pm8vNt6v0Wed8DKdcVnq+yNvNt9vWz9tI5TSdd1bxrGof2bRH3bzdqjG0yreRgew8Dx/n2MpTjJHsz9vMZXBtgAx0YZxJA7lcbh/b6EObcX1Sdz0XbVUdH+Sl/Wrja022iba6LtuMfxGkcp2TJ3Tqcv5I6+fnA+yKPFKXc0fuV3BN2aZt4iPk4Se0gU/nelA+l8m+Ol8lj1TVZh2f3IefkdD8f5X8Ovs0Sx0+t5dAKr1YioMlotzoQuxnmzsVpHQgKXeM+B8DV96nddVNA6h4B5SDvS712XZdG037ce6wGY7MH/sioKrLq5LJCTe9O1VVZkz7dqFvHBdhd46dVUH8mJjFBTDvM3NhzF/sa+LJyXiXKU646IhPp8dpnV6ceOgfJxNsdNVVV9UV3fr+rv2JsWjrin6tQU76/MGSMYbjIC7gmvKq9I1jpypvjPu2re/Uz8dN5yTsu22eXXxqnfGdcYYbDBwHjJMR3HVpt2tZboTzxwU5YxzjW3qt11bernyt6/hHf+JaJu9bBFr5/k22hzx3cA6LG3sE4FUPNTbRfVd16+yzK32i3d5f7SPY4OSXR45xEK4DIo3iQ/GqO0eR17V81KtbMmjTXl8JFjCCxaoBsWvblOdCJE2rLlCr6nDwsT9PXBDzRxt5O015yEFe1Z25vI0u21107yKXskPou0oHBj/axTe6Bu5Vfh8ngPxJTpUem9bPZTJ40w/6E8drk/2r2g+ZyCDFncHYX+enkR/L6H/wYH8qC19GFj7cJdBHrzhpITP0ZL0pRblUB8q37U+TbPK69gd9mmyTt0f5dFzJx4K8PNtV9g2/SMsjG7/hoi3XqSkvZFCmz/G6i+6hQ5flEPquan+T8zH65vbu4reb1s/7lp93VvGsaj9kVvlojBkxhkTZqiWyc59NWa07vscNBuoz3mwz0TbHI9csdcdVFbfQsYuvNdkm5LVZdh3/wrZh67yNqvx87M7rsJ3anm3GTPpIYtn23LHKr0qByT9shR+Gv0WbSZGy/SZfTcuuWg8+aX/T88Oq+qvyQ36dfVbVHzq/90AKhbmAwnD5U5YUcp7X1FEOZO7GRH2cAweou9DsWr6pbfJoh/a4sxKJu+15sBh5bZbRF8oiN3fokNG1bQ4gBq5wOGSnbYXcdAkv2ocrifLw5q4eCVul9orBNGxcl1dWTv7RTtSJ3bTJHbpVOkb5fLlK97x8l+0h9G3TfsoCO2KLNike6eObkZihEZ+oGkijTCw3rY8c/C3uuLJNX/jjRLuKZ7SfH2fhX/SD/kSin/QLuasS5aifskxlRf305BivtURe3TLuFiO/S9qkP23badsfjnXYxLiBfMa31JfSNtGdu9SR6spFPkvsRBvh3/k4jt+ktmecwW9ITXllgeRf1zEzqVq7ukr32ootMobQt0Wz+3agbBxjberBoul8sUrGpvVD31Tn9Jy0ime0n/s6vhdjUOrPXcZQZDPWhB/j6+mYg+7h/6x3Gd+56EZW13GGdrom9OKYSxlh87rzCP1O+0kf0/ppn1O75Xo12YaytM/1TaS0zdiXLtuOf3F+SMf86APjVOSn7VWdP9K2Wad89B2fYJ0+Rmp77ojxOeql/hn70mX4Me3X+UsbX01lNq3DB/9M9UrPD0112+QF/zr7tJExZJleXu2rUjCmP+eg4Q4vr7xwYg5H5G4jiQEQg/IHeA4+7jClKRwvDVwoA9yq1LV8lYx0H+3QXqo/TpPrmdZpWifQRBZ9JeHoHCjpABH1u7bNAQTbcDj05K8ppW2E8yMjOGK/VF9kRd+b8qraDI7Rd8pwt2vdtEr3deVGvU31jQtO/LzN43WYUzb40D9eucKeDMJsN8nM9aVsm3br+tu1PhcxcQIKmfgSfknK9WNfav/Ij3EC340nRPSDgTplQ/m2ifqMR1GfvsUxB9f0uEFm6BJjVF072Ax943ipK0d72JELEmRv2p+6dtjftT+he4wbyGBMwp5VKbczZelXU4o26sbx4B22pw9h36a8vM2o19d4jfxVuuc6dNneVF/qc5zQX3w4dG3SYdX5OJcZAS0yU32rzhdN7fZRHxlN551Uv/Al+IQvpfmha+rr4WvpONF1DKVutM1xH8cGtlk1vodO+ZK63OWPsZR8xjMuxuuO01wGumCzVeej/DoCOdStayd8LphRPl4VXuVrlI20yjYhK8Zt/D04hwyWyCEvHc/CrjGeV43H1K3qA/ubzh/kVyX8Kh3v0nMd5eGG/sGvSkaUS8+r6L4q4fNwapINkzpfXSU/z8+PSfpeZZuol/OP/XXLOAbr7FNXbxv7DzFV4DYaso3dEeBAxqmbDqjdaWfLEpg+AY6xuFCbfm+694CTfDDgIsYkAQn0S4CnQ1wYxwVlv9KVtisCMW7uqv2h2uVJHoHUEs6Lg7zaN5RhlLuaAHfsOTAjMfhykcNdK5MEJNA/AU4Ybe4Q9t/y7iQyxqSvccTrLwZRu7OJLc+bAEHU0saZeVv0mZle52DTeFrGeSAS6zwVW0LyidQMrcxFDo4diTsCXuAEDZcS6I9AHGv5Kxv9tTBOSfEEKrRjfFnCncfor0sJbItAHGtclMZrzttq23aGIzC3cweBU7zqCzUCxKU8PTWQGu44UbIEJCABCUhAAhKQgAQkMFMCvto3U8PaLQlIQAISkIAEJCABCUhgOAIGUsOxVbIEJCABCUhAAhKQgAQkMFMCBlIzNazdkoAEJCABCUhAAhKQgASGI7BxIMUHZszrzl86Y8dwKh+UTLvpj8YdzB1uix9Xa/phuU1bjt/LoX/pxBHrysU2fNwYqW/9N5UHy5gFDHvS7z4Sfe7bL/v0uVy/PmX3wS+XgZ2w9bbTpv61Sl+Pt36ONzjGcbyK+Sb5HDddxgj8h/LpXz4usJ3mp+uprrs6BlIdmtbRu+6c2Kd9kLVqLNiUFX0JO/U5BuTjbhPPMeSlNt3EhpvaY5O2t80RXeMY7uMaqov+fV7DdGl3nbKh67YYbeqD6/Rx6Dob/yAvg9ySZucY2iAhn6lO+UG5qfz2U90P9UV/uiz5McClzYLWhY9l+yfg8bb+j2L3b41miREkMFMgF9erxh5O3PyQaTqmxExoLNP6VbMP0gYX3s5K+HW7cJHKMcPvEzalPmeZS+3U1OYc81LfnWP/hujT1Mb0IRgoczsENgqkOEHFL3bjtHGwc9KJ6DafspM7BOkvHseJi/2R0um6OWmmv05NmfjF7CifLmOAj32hU2zHkpMjOqcnx2gr6qT9o17el5CV12M/delbTP8YJ+6oA4O6E0OwYCpJAlVORk31af+mm24q+PV57JHLRkZMS4nstM9pH5vqoXcTd3jyi+HoGjxSO+fsogxy4YTukSIv7MD+Op+KslGXZegZdeh7+kvusZ+yuV7w4cYAvpHnUT4SnMMvw4fJy+0e+2CR/iI9+0OPXL9UNuVSDmk91pt0JB99+A2x9BfGsT/HSRy7acDexBN5efJ4W9bxhv1jfApfiHEj9QV8K8aZdD91Un+O44XjLc8rd1T8i2OT4471unGUqox9BFGhS4ijLuMEx3BTfcqTz/HDsZEfwyEvX8axzf78GM3Hco6/9Icr4RtjGPXzc1XXYxQZpNQObe3zTM2v/4+24Ue/VqUYf2AY5wjqxNgTvhNyKBNjVf77OlE/grOcYyor9Ay5LINp2CYfd2M/ZXObpXqlsljPU/Q5+kE+/gezSHXHEPlVx0T4BP4exwoM41yV+je6kpr8Om0/1y31E+TE8Zrup20YYYOwR1xnRHnq0pc49zXZi7JN+WFPZAXX3EbIiBT9w8Ztr6EYC1L5sOWv6jxPO7lPsC/tO9tp6su/kBn9C/mp71f5X/h+lKdMHIP5cRZlYlklD5/BH0JGeg1BvSb9Qm4sw29iO9c1ldVk86i/k+XTG6brr7/+6T1n3Zfyzne+8+k9R9/fJo8ykd7+9rc/TZlIbPO3d7Ird6Xl2Ufe3kVwFC9l5fUjP61Lhb0Bp6y/XzlZqZJNff5IuSzaQBdkkuhj9Cvyyoyv/Uu5VLWVc0rrsp62FfWj7Xw72k+55/Kom3KjbNpGyEA2KWcXbVKuKlXxCJaUr2orZEVbUT50iXbQO/LYF+yqdIJ72k/Wg1vUzWWl2+gZdo328yVl+ItE+agTfYk8dEz1if2xzPVDbloeual+5KV2Ji/aDpnpkjxkhl1jO5ik+rbhSf1oP287lZXqwHqVbOpH33JZ4QOhZ5V/pW2gV8iqaivnltZlHUbRVtSv2w7dgkMui23qpnakbNpGyKCtKE9+pNCBclWpikf0n/JVbYUsdCM/yocu0Q56Rx77UnbpOnnRr+gH9dK6rKcc8u3wR2S1ScGFZaxHv6rqp/5alZ/uy22W5sEr2lklM2dEn6kTCVnhO8E+ZZS2RR3KRn5Vn5Ed+ZTP67MvErrwFym3R74d5WKJvm05UCdlFb4Sx1X0nT6RIr/c2PuHLvQlypMfHIND5MU2MtP1kJUzglfUpQzbyI9E29FW6Bl51EOvukS91AZ5v/K2Ij84RP1UfioP3fgjVfUV+SErlcF6yA4bRttRLu03+/K+pm3nXMhL9UQHdCHV6RnMV+VHW9FvZNJWakP2pSnN7yo/2gv9kZvaLedCPu2FflGf/aS0LtuUW9e/cllhQ/pIChvHNvlpP8iPtilPHrpHefalKZcX28E+Z9FGv7B7Xje3E21FWXTKZad67nJ942+k0uiPuwak9E4I60St3G2IRPQciQiTiDju1nD3fA9mmc0+Ivz0LiD5dWnPKPtPgCjDa3HIIOLNE/tpOyJq8qkf0TlPkuJOC3mpDmx3SbSP3FQGDGivTaI+usZrfuie3jkJGZEf26uWyIk6odvDDz9cVqNN7jJEoix9aKsz9VI/QP/wgeAcbYadoq10iU9RL57skcfdkKjTxT+qZL31rW8t+xS6IT+YsF6XUjboFj4edeNY2BtQ932qTla+Pz0+8PfQLWSmXKuOr1xefnyldo/jiTbY34UndkztEjbxeMst8Mx2yj18f0rHG73g2Ev9j+O6KeEjHGOR8Bd8LfWRGHOjTNMyxkJY8kf7TWMSx+UqHZvaI487r7QVNmsqv+oYRX9kBUNkduk/dbsco026ktfGPqkM9G3DIa2TrqN/jJEhJ44BdEnH1WCU1o/18IOQFVwOHz7ceRxbZbO4Hom2aRMbNCVsGv2jH+gXPt/mGGrrE8jFv+M6hmOLtzvYX5eadMMGbcf06F/wQwdkhy6c++LYo+9pu+iWXgOtyo++pD6B7Dg3Rn7dsqv86Ft6Lk7bq/KB6GuuQ/BJdWcdTujf1b/a+k/4QKo37dFuauO0j7nusY3tQh7XDKzHsZdeQ1C+jX4hF7ukxzxyaSvG9JxNLjvk7Hq50at9ufLhGOmjuCjDYBmGSF/jivxVy1RmyEnrhLPy+DRPdQcbjrAX7ZYntagfB1DIqHp8G3ltlwwosAnnaFsvyqF/3mcODnRPU14mzatar7IDjgsD9OVv787Jgap1g8WBQl/byPUJO7CMgy/q1clFn1wOdfJ9q/yDOlWywt5d/TPVP3QJGXEyQTYDRZuBCv0i5XaJwaTt8RVyYhn6xXab5Sqecbx4vLWh+UyZ3K7snerxlr4aUkcgjncubtOUntjZ38U/GUPT44kxnDEqvTBI26paT32b/PRVEnTO89GPE3ibtOoYRX7uB4wl1OuaUj27MIx22tonyvexzPuOzDgGcjb0qa5flK3Ky/e1YbTKZnGdELK48IuLyDom6fmBMvQ7eEedpmMo70fUqVqiC+cZEsu87bxOnh+6rTOmcyzDL45xzn1xXZLeRFx1DbQqP/qQc8mZRrl8ua78XE6+DbN49Y88+p+nIfyLNrr6D6zipkXKMfeHXH+20/JV+VX7mvSL8rDhr+46k7GePHwKHdqOwyF/W8teAymU5sBKn+Rs2pEYvABKFE9g0xSQrLpTlOqDA+FcHAzITA+CNIAKmaFLKqPteujftvwYyqUXGGPQp0qHsEnwXeUfVTL63odfceBzgouLhL7a6Pv4yvXqyjOOjVxO1bbHWxWVr++bwvEWJ0f8ENvnFxJf703/a7TFeM2Jlb80cdynd3wjj5NvfgymPhv+npbf9GTddIxy0bBpCp3HNOZt2qe+63dl1GQzdAuf4Olk+B/7NrnA7OsYYlxFJ44NrmOuuuqqjXCmx8cqQbRNkAIHArK4CUs9dElvcIS/1slclV9Xr+3+PuWn416M24yNdalP/9rlGFzXv3R/V/2CXyoj1rmG4g/f5qYtxzXX6alfRdldLnt9tY+DqY8TRQDh7goyObCrTpJRjmXcEcHB2yZk4+DozEHPoBCJbe48dRlUom6+pB0Gm3UT9XGkNKEz+4dKyO7TlqmeVTzy/kX5uFsW2+myi39Qr0pW+Ev4Tyq/aT3u7FAmdA8ZceCjH/7VVxrSJujYhWf0Nfi16SP6e7xVkxrStn0db/g5YwIXkG1ultEuKT1W2F537GJM5iTKmJz+sY+8qkRe3CGvyu973yo7hv+n7cb4ke6rW+9yjNbJiP192yfkrrusYlfHhrJ1eV0ZVbVb1wcu4PA96jSd03PdCOZD5y7HUJ0e6X7k4lf0m8R2U6rTbZ0xnXMd/YFFXD+hCzc2OPYireK1Kj/krLvsW346FsVbLXW60TaM2qRV/oXtNvGfKhvn43MbPevKdNWvLRvKcdxxTd6WZZ2OQ+zvNZDioKLDaWTOwU0UmR+8bTuT1uOire6kSLscuOljVurSdgwwVW2ic8hkPU1p22mf0jKsh3NGO+iZGjveh2VwicSdrarXoiI/XcZgFfLRC525wzJUQjZtpBfK6Ivem6bgEbLpV8orlc8ghW1zdmGP1EZN/oHMkJX2gdkO8Rva6JKoFwl5nDxSGWzDLz2ZRPl1l0McX7kubXl6vOXkNtueyvFGL9MTbzreVhHA//NjBd/Jx9qquuk+/DIuXtL9rNMG+TGepPmMNdxAiScUaV7b8Tets2p91TEaY1CMZ+gd55+QDZ80MMzz2x6jIa9p2Zd9mtpomxfHQJRPx+nYF8s4J6Y2x54p1yi76rywymbITP0H/vzhV3UJm4WdqM966vNdjqG6NtL9BDFtzzd1uq0zplMn/JVzHil0icCKfXHOD/uwD/vGMbgqn/KbpCHkh33Ri37VXcMM4V/r+k/YOB1T0vF5E8Zp3bb6xTGfH8dx7HPcpT5DwN503KU6bHO991f7uFvJwZEOPESS6yQcEHAhCyfgMSAnb5yY7TTF474oT17TY0Py42DPL3h5PE4/wuGIhEnokw6I7EMP8uORPwNKKo/84BLyKMO+NimtTxsk2sv1KDNq/lGWurBp027ITi+UcHoGpE0T/Qk7IovtGISrZFexizviq/wDO9BvyuEfISt8hPzwm6q26/ZRL2Sge+gT5eGXv1IUeeky1y/Nq1rP9afMusdXLh+dV/FM6wS34ECex9szhOZ6vDHupGNC+GOMi/gz+VxUkIePcFIMH4mxLPWjdJ2TJrLy44mbLdStuvsbwQlja14P2eyjfugQ7a177Ef9umUwSdtLj9HIj3MBeqQXYXHuiWAK5lG26zGa67ipfXJ5fW7TN87rwQ1d61L4UVyIUy49P60ax5DddF5AXtiMc16qF3nYpMoXySMhP9UtZKE3dZuOoWck1P/PbUhJ2OEjLFclOFXpRr1VY3pV25z/8NXgwXauS2qv8GXKcSyQVuWXhTb417d8OMEw9VW4xjGbqxrHfJQnP3yii3/14T/ozo3o0AWb1umd92PVdlf9wl/T4yE9joNb+EzYMfSgD6uuOaLskMtDTBk4ZAPKlsASCXCHhcEpTkxLZGCfJSCB1QQIHhkr4qJydQ1LjJkAF6ncoO3jpmOXfnJhrw91IWZZCfRDoNdX+/pRSSkSmD4BLoy402OSgAQkIAEJDEmAJ66eb4YkrGwJ1BPo/dW++qbMkcD8CfAKCHcGeW0hXnWYf6/toQQkIAEJ7IIA5xvOO/Gq2C50sE0JLJmAr/Yt2fr2XQISkIAEJCABCUhAAhJYi4Cv9q2FzUoSkIAEJCABCUhAAhKQwJIJGEgt2fr2XQISkIAEJCABCUhAAhJYi4CB1FrYrCQBCUhAAhKQgAQkIAEJLJnAxoEUU32mP5g1d5hMax3z74+tr0PaAhsjf9uJD2mZkSgS7NMfb4v921iiy6q2N7FB+BYfDpM2kZXyiN9ACblp3hDr+AqsIu3SZilD/CjVK/Rrs9zU/+O3lGCxLTu06VddmW3qG35fp8uY9oeuY7Dhpj45Jq7ossnxWdcX/Ji/SOl4EPu2tUQPbNaUNtWP8SXOl336R34eburDpnn5MbYpk030SRlu4p/Rp3V1oW1sm9p3XVnbqDc1fTdl4qx9mxK0/iIIcCJpc/FU9YOg6wLqU9a6OvRRb5ezSfXFcNPfhGE6fH6IM36AsA+uQ8qYmr5DslC2BDYlQBDFMcWPjTalvsYr2th0zGrSc5t5fTLpqndfDJnBd5PzIIEJ09tP5Xcpp6ZvV7/Iy/ceSBExM1jELxHTYJMDcXGa3iWmbv7L9ulFLNNKpwdWm/ZW1Y9fdg7Z3IVI9V/Vhxxqus2diPRXm8mr+yVm7rzwy83pwYIuKQ/6m6b0V6DT/Xk98qibXszh7Py6e6Q6vSI/XVbZLR106Mu99967XyX1gZxvXbtht/QX6BGI3JRpKnsV7zb+sq/03kroGj6S5lWt0+/4McZYT30p72vKKf8dkKgfXJvsFXqmOsEltRM8w/7pfurkfpTqRX7KmO08pX6Z94O86Hdun7Td6G/Kix+Y5JiIlOsVxyz5MKBfXLCQaBNZ2AMZ4evo87a3va341V/91fIHLEM+ulE+HV9KQXv/kB3HYfQB3UPXVI+oE8tgQ/vYEPl19qIO5cNOISPYhC/EsUF+3jb1w1/zvJDXZP8qfaMey6r+0x5/cVzCNP1x0Kb+prJjval/uazwraibL6M/sX+Vz+XyUp/LfTtkpsum9tJy9ANfTTkF2zje0rapW2fPvB5lqYsd4nzSZHPK5ykdb2iXFGMb7d10003FKaecUo7HwTStQ/kmluRHf9J68IvjPt2fy4NffrynPzkRwQv1OGeknFMb5TqmedSNvrEex2Ec9+zL67MvUtgFOwTDyKtahnyOc/rHmEOKc2mqC/vTYyH3zagf41mT/UNPZEaKfsWxmJ+HYz/lw45RN9WLfSEr8vNl6ud5P1Im1Evtk7Yb/aVM8KoaRyOPcnn93J8oiw24AbbJ+SP4xnFNH7Bl6keRh15pStkwXlAu5KXlgjHl0+OeMsEmfCE9NshP2476cR5N8ygbqc7+VfpGndkun94wXX/99U/vOcO+lLe//e1Pv/Od79zfJn/PAfe38xXKR/09h32abepEQlbksw9Zaf6q9trUT+XtOU+pQ7TPkjaiD6Fjml+3/uCDD5Z1qROJtlI+sZ9lVduUZT8p7wtc0I12SMgOVizTfpFP2ZCVt1WlK3UipfKqyqa6pWWpn9os55frEe3FMu0/+5rsXaVXzrupfrSZLoNntJ3aMi0X66kNWE/tgyz6E4l8/iKRl5ZPZQWnsHVsUzddD1nICZ8NLlE3ttO+tLVfyE+XtBWcQnbaT/KjrXQ9ykZe8IptZFI+UupH7KMc+fSfFOWjPvtyhqleaZ8pi/yQxXaakB22inaDL+VSPdJ6sZ7mr7JX3s/gxJKE3mnb+TZtha7RfroMedHXfJuyqb5pXdbz/sd2HdtV/Y360U6VXaI/edkq2SGHZS4rfCRYIpe+xjb5aT/Ij7ZDXlo+bSvykREpby/2swzu9CkSdg3b5n4QfQ+7ITt0i7yQw5K8kFXVVs4mrRvyQrfoR/Qt8mOburkt8jZX9Yf6KftV8kKn0DHVP9ZpMxiwDyap/ZCRtpkziTbCP1bVj3ZjiW6hH3VTXlEmXaZlou2wdzAPXSI/6tPP9LglH3mksEXIim1kpushi3o5l6hLmZxTatvQM2Tldoz9saSt0JN9yE5tRF5wS9ejbOQFj9gOPViSUh3ZjvzoV9SP8pRhX+hGuZxJtEVZ5IcsttMUbcU++pfKog3q16U0f5W9qnjTVuhGO2nb+TZtoV9Ton6qb76d6tskZy55G38jVRVhEmlH4g4WUX5V4m4TkXPcZeVuUno3gqibFPmxzl2CVGZde23rp6/bsJ5H4G3uJJWKZv/oG7LSu2TwqEuhR+jNkn7Gfu6opSzW1Yv2Yc/dmkjoCvu9gy121S6pS9m0X9gg6qa2QQh3RONOyN4gcEBuFe8DBSo26uzdlndd/YqmDvCuyl+1D07oRcJewYYlfhx3i8lP9WI7TTAnP2QFt/CPLj67if1SnVhHFjqFX7KeHsNp+eh77KNsfnykfoVMytAGKfUjtlP/Y5tE+ar9z+Qe/E9beye4/Z3YI461/Z0NK9FniqS2bahSZoXt0nLpsYxe6BKJ/pNP32JMSP3mrW99a3nspXyb+hHyogxy8a29i4JostUy+h+8U/9Neazqb9pYjH0hmzzWY8zvOn60GTPhDQNSqnfXY5T6bdqjHIk2aS+1NWMo+pDa+HtZsMU/bI7csBVVsFeM2bmI0CPKY4NglJYNH2IfbTSdU7r2Z5U82kSn0JHtNqnO3tRtY7+m+nn76NZVv1QG/QvGIefhhx8ui2CjlHd6zKQyWK865hl7Dx8+XDLMx+Gm65Q+j9Gux1h+/Of2gldwgBe2Ch9v439d/AnZm5w/0vGy6To5tyU6Ntkr/CXsFOeM2A8PzhmR4IId8JFI9K0uhTzqRao6B0XeEpa9v9oHNB73pyl3/sjDeHlZnIT9pDh5po9yoy6DCWVJuYxob936yMRZ4jUVtpsci/xVKe1D6F1VJw58BgH050Sbp/TRaZ7XdhvZ/O3dVThQpaq9AwX2Nhg8qBsDVJ7PQMZj3+gzg130mYOZC7bI40QQB3gup267zt5p+ZDPvmg78tvUj7KbLvO2kYd/x8kwzW86eeHTVZzS+m19dhP75TyqjmH6gX/kCV3x7ziuWE8HY8rnDLBVjAchL39tJPazzG2b5uXr4Yvsh12XutRJ2bOd68m+plRnL459ZHNiQ0fsFVzwg7zd9AIr8pr6gp5RLvTjuO8aSOUyQlbdsq6/aXn8Bv3S4zfyOWbCZpHfdvxoGjOr+oEOXY/R0JNlU3tpuegPYyZ8SGHPKNfk71Fm1XLVMZ/Xp//hc5FX5VMpO2zH36pzStv+tJFXpVPoW7dMdY4y+THRZL829UPupsuq/jEG4CPonOajV5Vu6JD3L/TKy8dxRX6eF3X6PEa7HmMEH/gXYxX6cW2Rpvz6hTL4fpqa/C/lmdapWo9jl7x1zh95W3HdWtVW3b46e9VdR+IHJALoNMEt8thfZ3vy2p6DKLuUNEgg1Sc8DBxPM9aR27V+erJP3zldp23qhKMzAHCy5CCuC0Aoz8mLgYILTMqldy1icKdP3JFIdaVu1xT961qP8tGfqrochOhHIqDiLx30YvDjPV36yl8abFXJbLuvK++2csdcLvWDsCm+0pQ2sV+T3FV5+DV/BAnYHR9vezGcngDDv8Leq9qtyscn+UMXTracGLeR2tiLYxy9GA+4cMkDzm3o2VcbbfqbtrVqzO4yfvQ9ZqZ6Vq13bQ/7cgEDI44FLoAi9e3vTcd8tLnpMsafKjnr9KdJXlUbm+7rar9N2xtL/RhHw0dWXaf0eYx2YcAYzR/HTNysrbohVyVzHf+rkhP7dnX+oP1V9mq6jgz9XfZHYJBX+9qqhyPmUXgeFXMRsW5Cftf6cTLLH52uowMXQuiArHjcvEpOXMxx0MOCA4LEOn3hImKTwDLaX4dNWje/yxN5+RJ9+UP/1LaU4+IQNujSVl4uP91eh3daf5vrcUeIC6hIcXcuttMld69yfpHf1We78G6yH+1X+VGdnqEvS/wc23MSTG2f103vfkXQRb2+UrTPsRXHWl+y6+S0sRd6oVP4NJxJVX4QPhQ+Vddu7EdWzpm2oo0o19eyTX+jrSp/irx8uWr8oI+bjJnBM/jSftMxuk579JeLUvSEU+qDffo77aTHWc4y36Z87iP5ubqqDv2oS13708UX6trssn8d+3WR33fZKj65zaLNKntGXowxba9TqtoNWfly1THa9RgL+eiAvtyES30u7z/blCV19b9oq2m5i/NHG3vVXUcGi3wcg2HkNfWXvD7OQavamFr+TgOpuLNA0ECKu3IBkXyMm95dx4mIxvMDJuqky3Xrp7LRLT1QU/lt1lNZ9K/N6zMcnJTjBJs7d3oAxCtSVXpQD72j/WAcZbnzRBvpRQJ3eHhKtCoRFCI7lUk96pOwV2qzOIGjE3Xibgpl0Y+//DE3eeuk6C912/Jep51N68Ai7ByymAWrLuHLqe/QTziG/dJ+r/LZTeyX6xfHWPgCeqR6puXRNdWZvHwAp270BZms00akyGM79bHI77rkGOMEi//lx1pXWV3Kp/2osle83gcP/CRS7E+PU/yGMm31hyfcGUtJ6EI7jAlDpVX9jXbDn1LbpmM+rLqOH23HzNAhll2P0ajXtT36HMdM6uvIS7mlTKKtWMYFadiUYw0bR1p1zEe5WOJPHBcxvsSxGPlVyzbnlLb9QX4beVV6bLqvq/02bW/d+sEn6qdjQuyLZRzzYU/2c77GrqTULpQJfywzs399HqNdjzGO/dAZtbi2SK8d0nGNfuDD6fiZ9rPpeMq6XLs5hvNHnb3oN3bMryPZn15r4DfYIR976jrdxzmoTvZU9+/81T7ueHNAx4HL4BAX3kCN/PTk2eWOdNf63EFBn2gPp0MnDsiqxMHIXcSqJ044Jn0JWTgrryoQAHFAs12VkEd7qWNTlrsvafAUfaONtCwyo+0IbuhD2l6UT+VRpqofuY7IibbDbhys7CPxxCxlyL7IQz59Dybk0S8OzqoEf14Bo4/YpilFn0M2erbh3SRzyDz6g/+EvvS1zs/CXlEWveK1F9ilvHOfhQP2oQyskbWu/ap4IAu9whdoP72IizroSfupz1E29Tm2w2eplx7rV111VZkX7SCLVOX/ZUb2j37jS+iKznCJk0LwzaoMstl2jImTdK5b2C58AWarjo20I+nxCw8SLPN2yowe/rXtbzSV94/94Qddxg/6Sb9SfwvZbX2myzG6bnvxFAo7pqmLv0fb2JM/fCeVl9o8jp90zE7bZT0/VinLX1MK/0l5p+eUVf2hPrrH8blKXpMukQcD9GE8wvZNKRim+nf1lyb5fefBJz2XpvbO20rtH3mpbTgeYjyhbH7eRDa2ifNwcIk6yFznGKVel2Ms2g0fjn4hh4SPkoeupDhHsr7K/yjTlHL/pO1dnD/QY5W96EfVdST74U3wFLbLGVJmVQo7hAz8A7lLTYeYfnBMnY+7KlMxCncDGKTTi8Ex8VQXCUyFQNNNiSH7QLuMN5xQTBKQQDUBbnBw8R0BTnUp90pgNwR4UkWA0cenD1164PmjC615lt3pq33pKxuBlzvycYcu9o15mT85GrOu6iYBCRwkEK96GEQd5OLWsglwQzN9MswNQ46VKZ2bl21Be78NAp4/tkF5/G3s9NW+eESZDthDvmIyhDmm8uRsiL4rUwJTJsDFIjdCeP3DJAEJfJ0A5zXOy/HqDjm8zuMNh68zcm3ZBDx/LNv+ae9H92pfqpzrEpCABCQgAQlIQAISkIAExkhgp6/2jRGIOklAAhKQgAQkIAEJSEACElhFwEBqFSHzJSABCUhAAhKQgAQkIAEJZAQMpDIgbkpAAhKQgAQkIAEJSEACElhFwEBqFSHzJSABCUhAAhKQgAQkIAEJZAQ2DqRiCnNm94lfVc/aGHSTdpma1SQBCUhAAhKQgAQkIAEJSGBbBDae/pzgaem/arwtY9mOBCQgAQlIQAISkIAEJDAOAhsFUvyi87333lv2hN9jufHGG8t1fn8ifqjs7LPPPvBL0zxB4tfR77jjjrIsv0vB71PU/V4FT5uuu+66A7T43ZczzzzzwL7YiLn9Yzt0YjuXhR4XXHBBFHUpAQlIQAISkIAEJCABCUigFYGNf0eKYIpgKH6YliCKJ1QRoBDYEFRdeeWVpUIETBE8sSMCqPixv7Q89ZCXBk609+CDD5bBV9SP/LQueTwtu/nmm/cDPNqKslWyqWOSgAQkIAEJSEACEpCABCSwisDG30ilDcS3ShFEkcc6T63iCRX7eBIUiSdWBF7xi+lnnXVWGSiRzz6eKKVPn8ivSzwVi4COMueee24pg4AqbZ+8KtnsN0lAAhKQgAQkIAEJSEACElhFYKNX+3LhETDFU6Y0/+GHH94Plk455ZQ0q9V6KjOCrrRiBHE8wcoTQRR1CNjiNUG/68opuS0BCUhAAhKQgAQkIAEJtCXQayBFo/k3UW0VqSsXAVR8z3TnnXcWPHmqS+k3UXkZnlbxF6/8Ieeyyy4rn1zlZd2WgAQkIAEJSEACEpCABCRQR6DXV/t46hOTT9Q12GU/AU+8gpe+Llgl4/Dhw+XueDJVVSb28cofARdPpe67777Y7VICEpCABCQgAQlIQAISkEArAr0GUvFNEhNCRCIY4qlS/o1S5K9apvUIkmK2v7xe/uoe+dSlbXSgLutpoEXQRz2TBCQgAQlIQAISkIAEJCCBLgR6f7WP2ff4TileyUOZptftmpQlMOOJUcgi6GHWPb5ziu+e0vox0USUJy9m6WOd1/jiGym2eSIVT7oIsMhbV1fkmSQgAQlIQAISkIAEJCCBZRDYePrzZWCylxKQgAQkIAEJSEACEpCABL5OoNdX+74u1jUJSEACEpCABCQgAQlIQALzJWAgNV/b2jMJSEACEpCABCQgAQlIYCACBlIDgVWsBCQgAQlIQAISkIAEJDBfAgZS87WtPZOABCQgAQlIQAISkIAEBiJgIDUQWMVKQAISkIAEJCABCUhAAvMlYCA1X9vaMwlIQAISkIAEJCABCUhgIAIGUgOBVawEJCABCUhAAhKQgAQkMF8CBlLzta09k4AEJCABCUhAAhKQgAQGImAgNRBYxUpAAhKQgAQkIAEJSEAC8yVgIDVf29ozCUhAAhKQgAQkIAEJSGAgAgZSA4FVrAQkIAEJSEACEpCABCQwXwIGUvO1rT2TgAQkIAEJSEACEpCABAYiYCA1EFjFSkACEpCABCQgAQlIQALzJWAgNV/b2jMJSEACEpCABCQgAQlIYCACRw8kV7E1BL74xS8Wf/fLHy6Of/SpmhLuXjKBJ598svjSl75UnHjiiUvGYN9rCDz99NPFY489VjznOc+pKeHupRP4/Oc/Xzzvec9bOgb7X0OA8eP4448vjjrqqJoS7l4igef/t99enPBd37rErm/cZwOpjRF2E/DlL3+5eOy37y2+9Nef61bR0osg8NRTTxVPPPFE8dVjj11Ef+1kNwIEUl/96leLJ/SPbuAWVJpzzFPHHbegHtvVLgS+8pWvFF855pji0KFDXapZduYEnv1fnWogtaaNfbVvTXBWk4AEJCABCUhAAhKQgASWS8BAarm2t+cjJPCsZz2rOPpoHxSP0DSjUIm7yPrHKEwxWiWO2XvaYJJAHQHGD59G1dFxvwS6EzCQ6s7MGhIYlADBlEkCdQT0jzoy7oeA/qEfNBHQP5romCeB7gS8YuvOzBoSGIwA38DwjZRJAlUE9I8qKu5LCfANnUkCdQQ4vzCOmCQggX4IGEj1w1EpEuiFACc4JpwwSaCOgP5RR8b9ENA/9IMmAvpHEx3zJNCdgIFUd2bWkIAEJCABCUhAAhKQgAQWTsBAauEOYPfHRYD3151MYFw2GZM2TjYxJmuMUxcnmxinXcailZNNjMUS6jEXAgZSc7Gk/ZgNAT8Gno0pB+mI/jEI1tkI1T9mY8pBOqJ/DIJVoQsmYCC1YOPb9fERcDKB8dlkTBrpH2Oyxjh1cbKJcdplLFo52cRYLKEecyFgIDUXS9qPWRBwsolZmHHQTvix+KB4Jy9c/5i8CQftgP4xKF6FL5CAgdQCjW6XJSABCUhAAhKQgAQkIIHNCBhIbcbP2hLolQDvrzvZRK9IZyXMySZmZc5BOuNkE4NgnY1QJ5uYjSntyEgIGEiNxBCqIYEg4MfAQcJlFQH9o4qK+4KA/hEkXFYR0D+qqLhPAusTMJBan501JdA7AScT6B3prATqH7My5yCdcbKJQbDORqiTTczGlHZkJAQMpEZiCNWQAAScbEI/WEXAj8VXEVp2vv6xbPuv6r3+sYqQ+RLoRsBAqhsvS0tAAhKQgAQkIAEJSEACEigMpHQCCYyIAO+vO9nEiAwyMlWcbGJkBhmhOk42MUKjjEglJ5sYkTFUZRYEDKRmYUY7MScCfgw8J2v23xf9o3+mc5Kof8zJmv33Rf/on6kSl03AQGrZ9rf3IyPgZAIjM8jI1NE/RmaQEarjZBMjNMqIVHKyiREZQ1VmQcBAahZmtBNzIeBkE3Ox5HD98GPx4djOQbL+MQcrDtcH/WM4tkpeJgEDqWXa3V5LQAISkIAEJCABCUhAAhsQMJDaAJ5VJdA3Ad5fd7KJvqnOR56TTczHlkP1xMkmhiI7D7lONjEPO9qL8RAwkBqPLdREAiUBPwbWEZoI6B9NdMzTP/SBJgL6RxMd8yTQnYCBVHdm1pDAYAScTGAwtLMQrH/MwoyDdsLJJgbFO3nhTjYxeRPagZERMJAamUFUZ9kEnGxi2fZv03s/Fm9Dabll9I/l2r5Nz/WPNpQsI4H2BAyk2rOypAQkIAEJSEACEpCABCQggZKAgZSOIIEREeD9dSebGJFBRqaKk02MzCAjVMfJJkZolBGp5GQTIzKGqsyCgIHULMxoJ+ZEwI+B52TN/vuif/TPdE4S9Y85WbP/vugf/TNV4rIJGEgt2/72fmQEnExgZAYZmTr6x8gMMkJ1nGxihEYZkUpONjEiY6jKLAgYSM3CjHZiLgScbGIulhyuH34sPhzbOUjWP+ZgxeH6oH8Mx1bJyyRgILVMu9trCUhAAhKQgAQkIAEJSGADAkdvULf3qg8//HBxxRVX7Ms97bTTiuuvv77cfuCBB4prrrmmeNWrXlVcffXV+2V2tXLxxReXTd922227UsF2Z0iA99edbGKGhu2pS0420RPIGYtxsokZG7eHrjnZRA8QFyri7rvvLvi78sorVxK4/PLLD5Q5+eSTi2uvvXZ/3y233FLcdddd+9uxcvbZZ+/Lv/POO4v77rtvfzvKjG05qkAqDaIA9clPfrK47rrrRhE45YYzgMqJuN0XAT8G7ovkPOXoH/O0a1+90j/6IjlPOfrHPO06dK8IoG6++eaCQKcp3X///eV1+2WXXVace+65+0UJnAiubrzxxv195513XnHppZfub7Pyrne9q6Bsvv9AoZFtjCaQ+uhHP1qiueCCC4pLLrmkXOepT+yv4pY/wUqfVlU9MbrqqqvK4CyCIIK0kF/39OsFL3hBQVRMwjHe8IY3lOu5/FtvvXW/HAVSXcoK/pNACwJ8I/Xkk0/6VKoFqyUW0T+WaPVufWayCZ9KdWO2pNJMNnHUUUcVPN02SaANgXi6RODz0EMPNVa56aabymvlNIiiAoERdbme5jq/Ll144YVlIDalQGp030gBmdf4SAQ8EfTk0CmTP8EiKCJYIoWhQhZLnnAR4JAIfCKIYjuefrEeifwIothHNE7wlqe8HPns++AHP5gXdVsCjQScbKIRj5l7BPxYXDdoIqB/NNExT//QB7oSILjhSRKv5zUlnkYRLOVBVNThlcC4No99c1iO5okUAQ5PhQho+BYqUl0gdfvtt5dF0qdE8cSJIIbImSCIdzDPOOOM8j1LKrzmNa8pgyHy0qdQ8UQpAq9S+N6/G264oTh8+HAZoKEbgRTbaUL3VE/a52nXgw8+mBZzXQISkIAEJCABCUhAApMh0Db4uffee1e++req0zzR4vp9Smk0gRTQmFgifd2OfbxClwYp7CN9+tOfLpfxqh0bb37zm/cDmAjMCJh4VfDDH/5wWZ79ESwRGMUremXm3r80UKJsBE3nnHNOGeRVPZGKushNg8DY71ICbQnw/rqTTbSltbxyTjaxPJt37bGv9XUltqzyTjaxLHvvurdcg99xxx37aqSTSfCgI59wouq7qf3KI10ZVSAFo5iRj4AlXt3jCU8ENF04nn/++eXreLxiR9DUJqrmKVLXtuIJFPIJ+mK7i66WlUAQ8GPgIOGyioD+UUXFfUFA/wgSLqsI6B9VVNzXBwFe/cvfxOK6OK69CaqYhS/SFIOm0D1djuYbKV6t4+lQfFdEMBPwU4Vj/YUvfGG5GuXZeN/73lfuO+WUU8rlWWedVS75tokUjwsjUIpX8gh+4i99wlVWavHvIx/5SFkq5H/84x9vUcsiEjiSAN9I8TGwSQJVBPSPKiruSwkw2YRJAnUEOL8wjpgk0DcBvo3iGylm+FtSGs0TKYIQolWCngh8whB845S/UnfRRReVT37y8gRH/JGoF99dsWSbRCBFGZ4c5a/28U3UusnX+tYlZ70g4GQTQcJlHQE/Fq8j434I6B/6QRMB/INZ+0wSGIIAb5XxiQ4BVfowhOCKV/xWTZ8+hE5DyxzNEymCnPTHuqLjMdlDbMeS8nnQQ3AUrwZGOV7vI/GNU5ooFwFX7K9rK/Lrlnmb8U3XPffcU1fF/RKQgAQkIAEJSEACEpgNgTPPPLOc4Y9vn5g2Pf546ME1fpsf850ajEN7d8B9xrtFqz3yyCPFpy75/xRH/fXnttiqTU2JAHcMfY99Shbbrq76x3Z5T601/WNqFtuuvvrHdnlPpbUX/tN/WHzDD7xyKuqOSs/RPJEaFRWVkcAOCRhE7RD+BJrWPyZgpB2qqH/sEP4EmtY/JmAkVZwUAQOpSZlLZedOgAfETjYxdyuv3z/9Y312S6npZBNLsfR6/XSyifW4WUsCdQQMpOrIuF8COyDAhTKvXpgkUEdA/6gj434I6B/6QRMB/aOJjnkS6E7AQKo7M2tIQAISkIAEJCABCUhAAgsnYCC1cAew++MiwPvr/PK8SQJVBA4dOqR/VIFx3z6BY445Zn/dFQnkBDi/MI6YJCCBfggYSPXDUSkS6I2AHwP3hnKWgvSPWZq1t07pH72hnKUg/WOWZrVTOyRgILVD+DYtgZyAkwnkRNxOCegfKQ3Xqwg42UQVFfcFASebCBIuJdAPAQOpfjgqRQK9EHCyiV4wzlqIH4vP2rwbd07/2BjhrAXoH7M2r53bAQEDqR1At0kJSEACEpCABCQgAQlIYNoEDKSmbT+1nxkB3l93somZGbXH7jjZRI8wZyrKySZmatieuuVkEz2BVIwEvkbAQEpXkMDICPgx8MgMMjJ19I+RGWRk6ugfIzPIyNTRP0ZmENWZPAEDqcmb0A7MiYCTCczJmv33Rf/on+ncJDrZxNws2m9/nGyiX55Kk4CBlD4ggRERcLKJERljpKr4sfhIDTMStfSPkRhipGroHyM1jGpNloCB1GRNp+ISkIAEJCABCUhAAhKQwK4IGEjtirztSqCCAO+vO9lEBRh3lQScbEJHWEXAySZWEVp2vpNNLNv+9r5/AgZS/TNVogQ2IuDHwBvhm31l/WP2Jt6og/rHRvhmX1n/mL2J7eCWCRhIbRm4zUmgiYCTCTTRMU//0AdWEXCyiVWElp3vZBPLtr+975+AgVT/TJUogbUJONnE2ugWU9GPxRdj6rU6qn+shW0xlfSPxZjajm6JgIHUlkDbjAQkIAEJSEACEpCABCQwHwIGUvOxpT2ZAQHeX3eyiRkYcqAuONnEQGBnJNbJJmZkzAG64mQTA0BV5KIJGEgt2vx2fowE/Bh4jFYZj076x3hsMUZN9I8xWmU8Oukf47GFmsyDgIHUPOxoL2ZCwMkEZmLIgbqhfwwEdkZinWxiRsYcoCtONjEAVEUumoCB1KLNb+fHRsDJJsZmkfHp48fi47PJmDTSP8ZkjfHpon+MzyZqNG0CBlLTtp/aS0ACEpCABCQgAQlIQAI7IGAgtQPoNimBOgK8v+5kE3V03O9kE/rAKgJONrGK0LLznWxi2fa39/0TMJDqn6kSJbARAT8G3gjf7CvrH7M38UYd1D82wjf7yvrH7E1sB7dMwEBqy8BtTgJNBJxMoImOefqHPrCKgJNNrCK07Hwnm1i2/e19/wQMpPpnqkQJrE3AySbWRreYin4svhhTr9VR/WMtbIuppH8sxtR2dEsEDKS2BNpmJCABCUhAAhKQgAQkIIH5EDCQmo8t7ckMCPD+upNNzMCQA3XBySYGAjsjsU42MSNjDtAVJ5sYAKoiF03AQGrR5rfzYyTgx8BjtMp4dNI/xmOLMWqif4zRKuPRSf8Yjy3UZB4EDKTmYUd7MRMCTiYwE0MO1A39YyCwMxLrZBMzMuYAXXGyiQGgKnLRBAykFm1+Oz82Ak42MTaLjE8fPxYfn03GpJH+MSZrjE8X/WN8NlGjaRMwkJq2/dReAhKQgAQkIAEJSEACEtgBgaN30KZNHnWoOHSUMayOcCSBZz3rUHHMnm8c2pt0wiSBnMChvR3H7PmI/pGTcTsIHHv8cfpHwHB5BIFjjju2YNIa/kwS2CegO+yj6LpyaO9Voqe7VrL8+gQeeeSR4qGHHipOP/309YVYUwISkIAEJCABCUhAAhLYKQFve+8Uv41L4CCBr3zlK8WnP/3pgzvdksDXCDz55JPFpz71KXlIoJbAJz7xido8MyTA+cUJSfQDCfRHwECqP5ZKksDGBLhQfuyxxzaWo4B5EuAFgkcffXSenbNXvRD4whe+0IschcyTAOcXzjMmCUigHwIGUv1wVIoEJCABCUhAAhKQgAQksCACBlILMrZdHT+B4447rjj11FPHr6ga7oTAUUcdVZx22mk7adtGp0HgxS9+8TQUVcudEOD8cuyxx+6kbRuVwBwJOGvfHK1qnyZLgF+dP+GEEyarv4oPS4CZtk488cRhG1H6pAk897nPnbT+Kj8sAc8vw/JV+vII+ERqeTa3xyMm4GQTIzbOCFRzsokRGGHkKjjZxMgNtGP1nGxixwaw+dkRMJCanUnt0JQJONnElK03vO5ONjE846m34GQTU7fgsPo72cSwfJW+PAIGUsuzuT2WgAQkIAEJSEACEpCABDYkYCC1IUCrS6BPAk420SfN+clyson52bTvHjnZRN9E5yXPySbmZU97s3sCTjaxexuogQT2CTjZxD4KVyoIONlEBRR3HSDgZBMHcLiREXCyiQyImxLYkIBPpDYEaHUJ9EnAySb6pDk/WU42MT+b9t0jJ5vom+i85DnZxLzsaW92T8BAavc2UAMJ7BNwsol9FK5UEHCyiQoo7jpAwMkmDuBwIyPgZBMZEDclsCEBA6kNAVpdAhKQgAQkIAEJSEACElgeAQOp5dncHo+YgJNNjNg4I1DNySZGYISRq+BkEyM30I7Vc7KJHRvA5mdHwMkmZmdSOzRlAk42MWXrDa+7k00Mz3jqLTjZxNQtOKz+TjYxLF+lL4+AT6SWZ3N7PGICTjYxYuOMQDUnmxiBEUaugpNNjNxAO1bPySZ2bACbnx0BA6nZmdQOTZmAk01M2XrD6+5kE8MznnoLTjYxdQsOq7+TTQzLV+nLI2AgtTyb22MJSEACEpCABCQgAQlIYEMCh/bucD69oQyrdyDwyCOPFPf8f+8rvuG5J3eoZdGlEOBw5PU+Jp0wSaCKwJe//GX9owqM+0oCX/ziF4tnP/vZ0pBAJQHGj2OPPbbge0vTtAg87/AJxUte/sJpKb0AbZ1sYgdGvudDf1V8/v/++A5atkkJSEACEpCABCQggakROPM7XmQgNUKj+WrfCI2iSsslwBOpJ554YrkA7HkjAf2jEY+ZewS++tWvykECtQQ4v/giUi0eMyTQmYCBVGdkVpDAcAQ4wT311FPDNaDkyRPQPyZvwkE7oH8MinfywvWPyZvQDoyMgIHUyAyiOhKQgAQkIAEJSEACEpDA+AkYSI3fRmq4IAL8IO/RR/vp4oJM3qmrfCCuf3RCtrjCxxxzzOL6bIfbE2D8cKKJ9rwsKYFVBAykVhEyXwJbJkAwZZJAHQH9o46M+yGgf+gHTQT0jyY65kmgOwGv2Lozs4YEBiPgZAKDoZ2FYP1jFmYctBNONjEo3skLd7KJyZvQDoyMgIHUyAyiOssm4GQTy7Z/m977sXgbSssto38s1/Zteq5/tKFkGQm0J2Ag1Z6VJSUgAQlIQAISkIAEJCABCZQEDKR0BAmMiADvrzuZwIgMMjJVnGxiZAYZoTpONjFCo4xIJSebGJExVGUWBAykZmFGOzEnAn4MPCdr9t8X/aN/pnOSqH/MyZr990X/6J+pEpdNwEBq2fa39yMj4GQCIzPIyNTRP0ZmkBGq42QTIzTKiFRysokRGUNVZkHAQGoWZrQTcyHgZBNzseRw/fBj8eHYzkGy/jEHKw7XB/1jOLZKXiYBA6ll2t1eS0ACEpCABCQgAQlIQAIbEDCQ2gCeVSXQNwHeX3eyib6pzkeek03Mx5ZD9cTJJoYiOw+5TjYxDzvai/EQMJAajy3URAIlAT8G1hGaCOgfTXTM0z/0gSYC+kcTHfMk0J2AgVR3ZtaQwGAEnExgMLSzEKx/zMKMg3bCySYGxTt54U42MXkT2oGRETCQGplBVGfZBJxsYtn2b9N7PxZvQ2m5ZfSP5dq+Tc/1jzaULCOB9gQMpNqzsqQEJCABCUhAAhKQgAQkIIGSgIGUjiCBERHg/XUnmxiRQUamipNNjMwgI1THySZGaJQRqeRkEyMyhqrMgoCB1CzMaCfmRMCPgedkzf77on/0z3ROEvWPOVmz/77oH/0zVeKyCRhILdv+9n5kBJxMYGQGGZk6+sfIDDJCdZxsYoRGGZFKTjYxImOoyiwIGEjNwox2Yi4EnGxiLpYcrh9+LD4c2zlI1j/mYMXh+qB/DMdWycskYCC1TLvbawlIQAISkIAEJCABCUhgAwIGUhvAs6oE+ibA++tONtE31fnIc7KJ+dhyqJ442cRQZOch18km5mFHezEeAgZS47GFmkigJODHwDpCEwH9o4mOefqHPtBEQP9oomOeBLoTMJDqzswaEhiMgJMJDIZ2FoL1j1mYcdBOONnEoHgnL9zJJiZvQjswMgIGUiMziOosm4CTTSzb/m1678fibSgtt4z+sVzbt+m5/tGGkmUk0J6AgVR7VpaUgAQkIAEJSEACEpCABCRQEjCQ0hEkMCICvL/uZBMjMsjIVHGyiZEZZITqONnECI0yIpWcbGJExlCVWRAwkJqFGe3EnAj4MfCcrNl/X/SP/pnOSaL+MSdr9t8X/aN/pkpcNgEDqWXb396PjICTCYzMICNTR/8YmUFGqI6TTYzQKCNSyckmRmQMVZkFgaOn1IuLL764VPdVr3pVcfXVVx9Q/brrris++tGPFtdee21xxhlnFFH2tttuO1Cur42h5felp3KmRcDJJqZlr11o68fiu6A+nTb1j+nYahea4h9HHXXULpq2zYEJXH755fstXHbZZcW55567v52vpGUvvPDC4oILLtgvcv/99xdcU0fK82N/ukzlsf/kk08ur8ejzC233FLcddddsbm/PPvss4srr7yy3L7zzjuL++67b397v9DIVyYTSBEkRWL94YcfLg4fPhy7tr4cKkDbekdsUAISkIAEJCABCUhgsgQIZCJ4euihh4prrrmmOOWUU4ozzzzziD5R9rzzzisuvfTSMu9d73pXQRBDMBVBVMiiAOUJjKoCs6ry1CFwot6NN97IZpnSNmMfbVM2dIn9U1pO5tW+j3zkIyXXiJp/7/d+rxXnD37wg+XTKZ4g3Xrrrft1CMTYl/498MADZT5L9hORUyfKICtS7IvttFzUjTyXEmhLgPfXnWyiLa3llXOyieXZvGuPnWyiK7FllXeyifnZ++677z4Q6BD0ELRUPQGiLCkNXFi/4447yv0sqZsGTQRD6XZZ8Gv/brrppv0ALt2PTJ42EaA1JZ52VenZVGdseZMIpAh6eAp12mmn7T9+/PCHP9yK5c0337xfDoNGMPTzP//z+/tjhQg+TbSZOgGy0CVPeTny2Rdt5eXdlkATAT8GbqJjnv6hDzQR0D+a6Jinf8zPB3gCxdOnNJ111lnFvffem+7aXyfQqkrIefDBBwvqtkk8jaJOXZDFK3vx8KONvKmWmUQgFU+fzj///PJ1Pr6R+uQnP1nEE6Qm+HwzxWt48U1VBGDXX399uZ88/gjSSHmgdMMNNzTmUwd9Qk7aFg5pkkAXAk4m0IXW8srqH8uzedceO9lEV2LLKu9kE/OzN8FMXXCU95agh/LpQ4J0nTyCMl7Li794ipXLIlDjqdMmiSdaPAGbcprEN1IR/Lz61a8uWb/mNa8pn/jwOJCJJeoSwVHkE+yQCMDSFJNUpPtinTrxHdY555xT1s0DrSjLksAuf6qV5rsugVUEnGxiFSHznUxAH2gioH800THPySb0AR4wcK0ar/Pxel2auC6Ob5sIrOK6tu7JU1qXoCzksj+dTIJr9vw1vqrvplJ5U1gffSBFcBLBzxVXXHGA6T333HNgu8vGVVddVcrliRNPq2K7i4woy2t8OB6PMHkiFduR71ICEpCABCQgAQlIQAJ9E+BpFLPdtU2Uj0CJOryilwY/8QYXeZQl2EF+HkiRl795xXVwvM5HUJXqNYegCSZ5Gv2rfXn0mnZg1et95BPUkGLJU6oIzuKJE0+ZIlhL5bddj4kw4vHkxz/+8bZVLSeBAwR4f93JJg4gcSMh4GQTCQxXKwk42UQlFnd+jYCTTczPFaoCGgKYtq/d8YoeMvhrWweK8Zpg3at/8yNd3aPRB1Lx7mZ8qxTfIjE1I6kp0CKfJ0XpLHp8ZxWJ4Iq8/ElX5Hdd8vgTeaFz1/qWlwAE/BhYP2gioH800TFP/9AHmgjoH010ppkXAU1ce/I6HtfGcXM/7RV5fPvEUygS2zyNitf7kMV3S5EoVyeLMjy9YiK2aDvqEVylT7li/xyXo361L54ixZOj1AB8LxXGu+SSS9KsA+sEXJQj8bjxDW94Q7me7o+gjHI8rYrvosqCLf7hSARQkQj22N7k1cOQ5XJZBPhG6sknn/Sp1LLM3rq3+kdrVIstyGQTPpVarPlXdpzJJvhBXp5um+ZDIP/uieva+A0pghyeUDGLHk+duGblIUMkysZre7Ek2IqUyop9saQNXhNMv7mKPHSivbmnQ3sn5qfn3skx9e+RRx4pbv3Z3yg+/39/aUxqqctICPAhMCe6Y489diQaqcaYCDBcc6Gsf4zJKuPS5ctf/nJx3HHHjUsptRkNga985StloG0gNRqTtFbkzO94UfHmq76ndXkLbofA6F/t2w4GW5GABCQgAQlIQAISkIAEJNCegIFUe1aWlMDgBHh/3ckmBsc82QacbGKyptua4r7WtzXUk2zIySYmaTaVHjEBA6kRG0fVlknAj4GXafe2vdY/2pJaZjn9Y5l2b9tr/aMtKctJoB0BA6l2nCwlga0Q4BsYvpEySaCKgP5RRcV9KQG+oTNJoI4A5xc/ja+j434JdCdgINWdmTUkMBgBTnBMOGGSQB0B/aOOjPshoH/oB00E9I8mOuZJoDsBA6nuzKwhAQlIQAISkIAEJCABCSycgIHUwh3A7o+LAO+vO9nEuGwyJm2cbGJM1hinLk42MU67jEUrJ5sYiyXUYy4EDKTmYkn7MRsCfgw8G1MO0hH9YxCssxGqf8zGlIN0RP8YBKtCF0zAQGrBxrfr4yPgZALjs8mYNNI/xmSNceriZBPjtMtYtHKyibFYQj3mQsBAai6WtB+zIOBkE7Mw46Cd8GPxQfFOXrj+MXkTDtoB/WNQvApfIAEDqQUa3S5LQAISkIAEJCABCUhAApsRMJDajJ+1JdArAd5fd7KJXpHOSpiTTczKnIN0xskmBsE6G6FONjEbU9qRkRAwkBqJIVRDAkHAj4GDhMsqAvpHFRX3BQH9I0i4rCKgf1RRcZ8E1idgILU+O2tKoHcCTibQO9JZCdQ/ZmXOQTrjZBODYJ2NUCebmI0p7chICBhIjcQQqiEBCDjZhH6wioAfi68itOx8/WPZ9l/Ve/1jFSHzJdCNgIFUN16WloAEJCABCUhAAhKQgAQkUBhI6QQSGBEB3l93sokRGWRkqjjZxMgMMkJ1nGxihEYZkUpONjEiY6jKLAgYSM3CjHZiTgT8GHhO1uy/L/pH/0znJFH/mJM1+++L/tE/UyUum4CB1LLtb+9HRsDJBEZmkJGpo3+MzCAjVMfJJkZolBGp5GQTIzKGqsyCgIHULMxoJ+ZCwMkm5mLJ4frhx+LDsZ2DZP1jDlYcrg/6x3BslbxMAgZSy7S7vZaABCQgAQlIQAISkIAENiBgILUBPKtKoG8CvL/uZBN9U52PPCebmI8th+qJk00MRXYecp1sYh52tBfjIWAgNR5bqIkESgJ+DKwjNBHQP5romKd/6ANNBPSPJjrmSaA7AQOp7sysIYHBCDiZwGBoZyFY/5iFGQfthJNNDIp38sKdbGLyJrQDIyNgIDUyg6jOsgk42cSy7d+m934s3obScsvoH8u1fZue6x9tKFlGAu0JGEi1Z2VJCUhAAhKQgAQkIAEJSEACJQEDKR1BAiMiwPvrTjYxIoOMTBUnmxiZQUaojpNNjNAoI1LJySZGZAxVmQUBA6lZmNFOzImAHwPPyZr990X/6J/pnCTqH3OyZv990T/6Z6rEZRMwkFq2/e39yAg4mcDIDDIydfSPkRlkhOo42cQIjTIilZxsYkTGUJVZEDCQmoUZ7cRcCDjZxFwsOVw//Fh8OLZzkKx/zMGKw/VB/xiOrZKXScBAapl2t9cSkIAEJCABCUhAAhKQwAYEDKQ2gGdVCfRNgPfXnWyib6rzkedkE/Ox5VA9cbKJocjOQ66TTczDjvZiPAQMpMZjCzWRQEnAj4F1hCYC+kcTHfP0D32giYD+0UTHPAl0J2Ag1Z2ZNSQwGAEnExgM7SwE6x+zMOOgnXCyiUHxTl64k01M3oR2YGQEDKRGZhDVWTYBJ5tYtv3b9N6PxdtQWm4Z/WO5tm/Tc/2jDSXLSKA9gaPbF7VkXwT+63/0suLUb/rmvsQpZ0YEvvzlLxef+cxnim/5lm+ZUa/sSl8EnnzyyeKv//qvi5e85CV9iVTOzAg88MADxRlnnDGzXtmdvggwfnzTN31Tceyxx/YlUjlbInDcCcdsqSWb6ULg0N4d8Ke7VLDsZgQeeeSR4qGHHipOP/30zQRZe5YEuFv4pS99qTjhhBNm2T87tRkBhuvHH3+8OPHEEzcTZO3ZEvjCF75QPPe5z51t/+zYZgQYP44//vjCb6U242htCQQBn0gFCZcSGAEBTm4GUSMwxEhVYNY+g6iRGmckahlEjcQQI1XD88tIDaNakyXgN1KTNZ2Kz5HAV77yleLTn/70HLtmn3ogwKt9n/rUp3qQpIi5EvjEJz4x167Zrx4IcH5xQpIeQCpCAl8jYCClK0hgRAS4UH7sscdGpJGqjIkAr/Y9+uijY1JJXUZGgFf7TBKoI8D5hfOMSQIS6IeAgVQ/HJUiAQlIQAISkIAEJCABCSyIgIHUgoxtV8dP4LjjjitOPfXU8SuqhjshcNRRRxWnnXbaTtq20WkQePGLXzwNRdVyJwQ4vzhj307Q2+hMCTjZxEwNa7emScDJJqZpt21p7WQT2yI93XacbGK6ttuG5k42sQ3KtrEkAj6RWpK17evoCTjZxOhNtFMFnWxip/gn0biTTUzCTDtT0skmdobehmdKwEBqpoa1W9Mk4GQT07TbtrR2soltkZ5uO042MV3bbUNzJ5vYBmXbWBIBA6klWdu+SkACEpCABCQgAQlIQAK9EDCQ6gWjQiTQDwEnm+iH41ylONnEXC3bX7+cbKI/lnOU5GQTc7SqfdolASeb2CV925ZARsDJJjIgbh4g4GQTB3C4UUHAySYqoLhrn4CTTeyjcEUCvRDwiVQvGBUigX4IONlEPxznKsXJJuZq2f765WQT/bGcoyQnm5ijVe3TLgkYSO2Svm1LICPgZBMZEDcPEHCyiQM43Kgg4GQTFVDctU/AySb2UbgigV4IGEj1glEhEpCABCQgAQlIQAISkMCSCBhILcna9nX0BJxsYvQm2qmCTjaxU/yTaNzJJiZhpp0p6WQTO0NvwzMl4GQTMzWs3ZomASebmKbdtqW1k01si/R023GyienabhuaO9nENijbxpII+ERqSda2r6Mn4GQTozfRThV0somd4p9E4042MQkz7UxJJ5vYGXobnikBn0jtwLCfefyx4qhHHtlByzY5dgJf+tKXis989rPFl5/97LGrqn47IEAg9Vef+UzxxHOes4PWbXIKBO7b84+nn//8KaiqjgMSOOGYY4oXnHjiES0w2cRJJ51UHLOXb5KABDYncGhvFqinNxejhLYEHtkLoP77f/drxV/tBVMmCeQEnnrqqeKJJ54ojj322DzLbQkUDNdf/epX9Q99oZbAl7/85YJvLU3LJvC6b31J8cv/zYVHQHjggQeKF73oRcXxxx9/RJ47JCCB7gR8ta87M2tIYDACfCN19NE+KB4M8MQF842U/jFxIw6svk8aBgY8cfFONjFxA6r+6Ah4xTY6k6jQ0gkQTJkkUEdA/6gj434I6B/6QRMBJ5toomOeBLoT8IqtOzNrSGAwAry6xat9JglUEdA/qqi4LyXAq58mCdQRcLKJOjLul8B6BAyk1uNmLQkMQoALZb6TMkmgjoD+UUfG/RDQP/SDJgJMNsGkNSYJSKAfAgZS/XBUigQkIAEJSEACEpCABCSwIAIGUgsytl0dPwEnmxi/jXapoZNN7JL+NNp2solp2GlXWjrZxK7I2+5cCTjZxFwta78mS8CPxSdruq0orn9sBfNkG9E/Jmu6rSjuZBNbwWwjCyLgE6kFGduujp+AkwmM30a71FD/2CX9abTtZBPTsNOutHSyiV2Rt925EjCQmqtl7dckCTjZxCTNtlWlnUxgq7gn15j+MTmTbVVhJ5vYKm4bWwABA6kFGNkuSkACEpCABCQgAQlIQAL9EjCQ6pen0iSwEQEnm9gI3+wrO9nE7E28cQedbGJjhLMW4GQTszavndsBASeb2AF0m5RAEwE/Fm+iY57+oQ80EdA/muiY52QT+oAE+iXgE6l+eSpNAhsRcDKBjfDNvrL+MXsTb9xBJ5vYGOGsBTjZxKzNa+d2QMBAagfQbVICdQScbKKOjPuDgJMJBAmXVQT0jyoq7gsCTjYRJFxKoB8CBlL9cFSKBCQgAQlIQAISkIAEJLAgAgZSCzK2XR0/ASebGL+Ndqmhk03skv402nayiWnYaVdaOtnErsjb7lwJONnEXC1rvyZLwI/FJ2u6rSiuf2wF82Qb0T8ma7qtKO5kE1vBbCMLIuATqQUZ266On4CTCYzfRrvUUP/YJf1ptO1kE9Ow0660dLKJXZG33bkSMJCaq2Xt1yQJONnEJM22VaWdTGCruCfXmP4xOZNtVWEnm9gqbhtbAAEDqQUY2S5KQAISkIAEJCABCUhAAv0SMJDql6fSJLARASeb2Ajf7Cs72cTsTbxxB51sYmOEsxbgZBOzNq+d2wEBJ5vYAXSblEATAT8Wb6Jjnv6hDzQR0D+a6JjnZBP6gAT6JeATqX55Kk0CGxFwMoGN8M2+sv4xexNv3EEnm9gY4awFONnErM1r53ZAwEBqB9BtUgJ1BJxsoo6M+4OAkwkECZdVBPSPKiruCwJONhEkXEqgHwIGUv1wVIoEJCABCUhAAhKQgAQksCACBlILMrZdHT8BJ5sYv412qaGTTeyS/jTadrKJadhpV1o62cSuyNvuXAk42cRcLWu/JkvAj8Una7qtKK5/bAXzZBvRPyZruq0o7mQTW8FsIwsi4BOpBRnbro6fgJMJjN9Gu9RQ/9gl/Wm07WQT07DTrrR0soldkbfduRIwkJqrZe3XJAk42cQkzbZVpZ1MYKu4J9eY/jE5k21VYSeb2CpuG1sAAQOpBRjZLkpAAhKQgAQkIAEJSEAC/RIwkOqXp9IksBEBJ5vYCN/sKzvZxOxNvHEHnWxiY4SzFuBkE7M2r53bAQEnm9gBdJuUQBMBPxZvomOe/qEPNBHQP5romOdkE/qABPolMHgg9cEPfrC4+eabD2h92223Hdh2QwISeIYA30g9+eSTxdFHD35oinyCBPSPCRptyyoz2YRPpbYMfULNMdnEKaecUvzjf/yP97W+7LLLinPPPXd/O1buvvvuI67fIu/qq68uzjzzzOLOO+8s7rjjjthd3HjjjfvrdSuXX375gayTTz65uPbaa/f33XLLLcVdd921vx0rZ599dnHllVeWm7R733337W9HGZcS2DaBQa/WrrvuuuKjH/3oEX26+OKLixtuuKE4fPjwEXnukMCSCTjZxJKt367vTibQjtNSS+kfS7V8u34z2cTP/MzPFBE8PfTQQ8U111xTBlcERmkiuMoDrHe9610FgU8aRBEEse/+++8vCJLqginyuS6MtqMtAqe83nnnnVdceumlUaRc0jZl8/0HCrkhgS0TGCyQIoCKICp9AnXVVVcVn/zkJ4v3vve9BXc0Hn744eKKK6440G0OyjPOOKOIp1kcdOlTLeRFHhWR86pXvaqU8cADD5SDQghM8yKwoyy6nXbaacX1119/hA7kUy9NoXfsS/tEYEiiDm2QqmSUGf6TgAQkIAEJSEACOyDwsY99rLyJHQESARBBC0+A8kAqV4+nQA8++OD+UyDqXHjhhWUQRVnqI4tyF1xwQV69uOmmm44IoihEYERAV1cvBNEW11gGUkHE5RgIDDbZxEc+8pGyf+njWnb88A//cBnARKDy8z//80dw4O5ImtIgiv0ENek+DiwCMv7yuuQRXKUpArxzzjmnzMsDOfJpIxKBEsFfmthHe2mKIIp9yIh20jKuS6CJgJNNNNExz8km9IFVBHytbxWhZefz1sM3fuM3HoBw1llnFffee++BfVUbvMJHMJMmArE8ERTliadR7I8ALs/nlb2q4Csv57YExkZgsECK93BJ+et7PGniKVAk1nm6E388JSKlQQr7yI+gjKCGdfal5bmbQSJII4/XB0m33357uYx/HKzkX3LJJft5PPViX8ikDQIhnnyReMIU+XGwR3shN9qNfO7cmCTQlYAfi3cltqzy+sey7N21t/pHV2LLKv/5z39+/wlSl55zvUPQlAZCPH1Kb2oTKFV920Q7BGp847RJ4okWbZokMCYCg73a17WTPM2pe4Jz/vnnl+IiKCN4IiAjvfCFL9x/WvTZz3623Jc+GWJHBHVl5t6/9ECMvDe84Q2RXbz5zW8uHx8TCEUwdNFFF+3nEygxqER7kUGwRXrZy15W5kfdyHcpgVUEnExgFaFl5+sfy7Z/m9472UQbSsst8/jjjxfHH398ZwAESOm1EwK4FiJ4iskj4jXBtsK5jkonqkgnk6C9PCijfV/ra0vXctsiMFggFQEOT5YiAKJTbPMqHU93SPHtEU+PeKIT22VmT//y1/J6EqsYCfROwMkmekc6O4FOJjA7k/baIf2jV5yzE3biiScecRN4VSebXssjsEmDm5iMIpdJkJXfXCYQizd4CKqYhS+SQVOQcDl2AoO92vea17ym7Hv+zVJ8ExXfLhHk8CSHYIsga5Og5wUveEHZZrz2F6/iRdBWZQwCPlK8wsf6+973PhblLDZME0pKXw/kgCdFe+WG/yQgAQlIQAISkMCICTzvec8rnyKlKhLANL12x2t5BEL8rUqU5ZurPPFKIE+vmFLdJIE5ERjsiRTBEX+8rhez2qXg0lfl6sqk5duscweDICcP3rjjwfdQVQk9aJ/3fNN3fUN/6rC/Ssc6mVXtuE8CbQg42UQbSsst42QTy7V925472URbUsss933f933Ff/gP/2F/hrz4rikmAKuiQpmqQIupyMmL33Zim2Ar/Y4qlUcb3ESnTjyJIp/gilf8qtpI67sugTESGOyJFJ3loGEShzzxhIhvnPhL81mP7XymvVxG1TbyYkKKyG8KoihDnZiUIuoQRKWDCvrGpBZRpukpV5RxKYF1CPix+DrUllNH/1iOrdfpqf6xDrXl1DnhhBPK6yQCF75t4sYz110x9Tk3o3k9L00EPlVPo+KVPuTwx9Oo/BoslUMb/MYU3z5FHZbcrKZeBGRpHdclMHYCh/a+yXh67ErOSb9HHnmk+O//3a8Vf/X4Y3Pqln3piYCTCfQEcqZi9I+ZGrbHbjnZRI8wJyzqdd/6kuKX/5uDU5XTHSbY4pMFn1xO2LiqPioCgz6RGlVPVUYCEyDAhbIfi0/AUDtUUf/YIfwJNK1/TMBIO1TxscceK5588skdamDTEpgXAQOpednT3khAAhKQgAQkIAEJSEACWyBgILUFyDYhgbYEnGyiLalllnOyiWXavUuvfWWrC63llT311FOLY489dnkdt8cSGIjAYLP2DaSvYiUwewJ+LD57E2/UQf1jI3yzr6x/zN7EG3WQySZMEpBAfwR8ItUfSyVJYGMCfCP1xBNPbCxHAfMkoH/M06599orJJkwSqCPAZBP6SB0d90ugOwEDqe7MrCGBwQg42cRgaGcj2MkEZmPKQTqifwyCdTZCnWxiNqa0IyMhYCA1EkOohgQkIAEJSEACEpCABCQwHQIGUtOxlZougICTTSzAyBt00ckmNoC3kKpONrEQQ6/ZTSebWBOc1SRQQ8DJJmrAuFsCuyLgx+K7Ij+NdvWPadhpV1rqH7siP412nWxiGnZSy+kQ8InUdGylpgsg4GQCCzDyBl3UPzaAt5CqTiSwEEOv2U0nm1gTnNUkUEPAQKoGjLslsAsCTjaxC+rTatPJBKZlr21rq39sm/i02nOyiWnZS23HT8BAavw2UkMJSEACEpCABCQgAQlIYGQEDKRGZhDVWTYBJ5tYtv1X9d7JJlYRMt/JJvSBJgJONtFExzwJdCfgZBPdmVlDAoMS8GPxQfFOXrj+MXkTDtoB/WNQvJMX7mQTkzehHRgZAZ9IjcwgqrNsAk4msGz7r+q9/rGKkPlONqEPNBFwsokmOuZJoDsBA6nuzKwhgcEIONnEYGhnI9jJBGZjykE6on8MgnU2Qp1sYjamtCMjIWAgNRJDqIYEJCABCUhAAhKQgAQkMB0CBlLTsZWaLoCAk00swMgbdNHJJjaAt5CqTjaxEEOv2U0nm1gTnNUkUEPAySZqwLhbArsi4MfiuyI/jXb1j2nYaVda6h+7Ij+Ndp1sYhp2UsvpEPCJ1HRspaYLIOBkAgsw8gZd1D82gLeQqk42sRBDr9lNJ5tYE5zVJFBDwECqBoy7JbALAk42sQvq02rTyQSmZa9ta6t/bJv4tNpzsolp2Uttx0/AQGr8NlJDCUhAAhKQgAQkIAEJSGBkBAykRmYQ1Vk2ASebWLb9V/XeySZWETLfySb0gSYCTjbRRMc8CXQn4GQT3ZlZQwKDEvBj8UHxTl64/jF5Ew7aAf1jULyTF+5kE5M3oR0YGQGfSI3MIKqzbAJOJrBs+6/qvf6xipD5TjahDzQRcLKJJjrmSaA7AQOp7sysIYHBCDjZxGBoZyPYyQRmY8pBOqJ/DIJ1NkKdbGI2prQjIyFgIDUSQ6iGBCQgAQlIQAISkIAEJDAdAgZS07GVmi6AgJNNLMDIG3TRySY2gLeQqk42sRBDr9lNJ5tYE5zVJFBDwMkmasC4WwK7IuDH4rsiP4129Y9p2GlXWuofuyI/jXadbGIadlLL6RDwidR0bKWmCyDgZAILMPIGXdQ/NoC3kKpONrEQQ6/ZTSebWBOc1SRQQ8BAqgaMuyWwCwJONrEL6tNq08kEpmWvbWurf2yb+LTac7KJadlLbcdPwEBq/DZSQwlIQAISkIAEJCABCUhgZAQMpEZmENVZNgEnm1i2/Vf13skmVhEy38km9IEmAk420UTHPAl0J+BkE92ZWUMCgxLwY/FB8U5euP4xeRMO2gH9Y1C8kxfuZBOTN6EdGBkBA6kdGORNLzm9OO6kb9hByzY5dgJPPvlk8YUvfKH4hm/QP8Zuq13ox/cvn/vc54qTTjppF83b5gQIPPTQQ8XJJ588AU1VcUgCpz3v+ZXimWzilFNOKXxyWYnHnRLoTMBAqjOyzSu87ptPK04//fTNBSlhdgS++MUvFn/zN39TnHHGGbPrmx3anMATTzxRPPDAA8XLXvayzYUpYZYEPvaxjxUvf/nLZ9k3O7U5ASab4EaMgdTmLJUgAQj4jZR+IAEJSEACEpCABCQgAQlIoCMBA6mOwCwugSEJHHfccQUfA5skUEXgqKOOKk477bSqLPdJoCTw4he/WBISqCXgZBO1aMyQwFoEfLVvLWxWksAwBPhQ3I+Bh2E7B6nM2nfiiSfOoSv2YSACz33ucweSrNg5EPD8Mgcr2ocxEfCJ1JisoS6LJ/CVr3yl4GNgkwSqCDAZyac+9amqLPdJoCTwiU98QhISqCXA+eWrX/1qbb4ZEpBANwIGUt14WVoCgxLgQpmPgU0SqCLw9NNPF48++mhVlvskUBJg1k+TBOoIcH7hPGOSgAT6IWAg1Q9HpUhAAhKQgAQkIAEJSEACCyJgILUgY9vV8RNwsonx22iXGjrZxC7pT6NtJ5uYhp12paWTTeyKvO3OlYCTTczVsvZrkgScbGKSZtua0k42sTXUk23IySYma7qtKO5kE1vBbCMLIuATqQUZ266On4CTTYzfRrvU0Mkmdkl/Gm072cQ07LQrLZ1sYlfkbXeuBAyk5mpZ+zVJAk42MUmzbU1pJ5vYGurJNuRkE5M13VYUd7KJrWC2kQURMJBakLHtqgQkIAEJSEACEpCABCTQDwEDqX44KkUCvRBwsoleMM5WiJNNzNa0vXXMySZ6QzlLQU42MUuz2qkdEnCyiR3Ct2kJ5AScbCIn4nZKwMkmUhquVxFwsokqKu4LAk42ESRcSqAfAj6R6oejUiTQCwEnm+gF42yFONnEbE3bW8ecbKI3lLMU5GQTszSrndohAQOpHcK3aQnkBJxsIifidkrAySZSGq5XEXCyiSoq7gsCTjYRJFxKoB8CBlL9cFSKBCQgAQlIQAISkIAEJLAgAgZSCzK2XR0/ASebGL+Ndqmhk03skv402nayiWnYaVdaOtnErsjb7lwJONnEXC1rvyZJwMkmJmm2rSntZBNbQz3ZhpxsYrKm24riTjaxFcw2siACBlI7MPbf/NI7i7996G930LJNjp3AU089VTzxxBPFscceO3ZV1W8HBPhG6qtf/ar+sQP2U2nyy1/+csGTbVM9gf/qHb9QHH7Ff11fYMY5TDZxyimnFMccc8yMe2nXJLA9AgZS22O939JTX3q8eOrRz+1vuyKBIBCB1CEDqUDiMiEQgZT+kUBx9QCBr+4FUs/6qoHUASjZxlNPfDXbs5xNJps46aSTDKSWY3J7OjABv5EaGLDiJSABCUhAAhKQgAQkIIH5ETCQmp9N7dGECfCN1NFH+6B4wiYcVHW+kdI/BkU8eeG+sjV5Ew7aASebGBSvwhdIwCu2BRrdLo+bAMGUSQJ1BPSPOjLuh4D+oR80EXCyiSY65kmgOwGv2Lozs4YEBiPANzBMNmGSQBUB/aOKivtSAkxGYpJAHQEmm9BH6ui4XwLdCRhIdWdmDQkMRoALZSacMEmgjoD+UUfG/RDQP/SDJgJMNvHkk082FTFPAhLoQMBAqgMsi0pAAhKQgAQkIAEJSEACEoCAgZR+IIEREXCyiREZY4SqONnECI0yMpWcbGJkBhmZOk42MTKDqM7kCTjZxORNaAfmRsCPxedm0X77o3/0y3Nu0vSPuVm03/442US/PJUmAZ9I6QMSGBEBJxMYkTFGqIr+MUKjjEwlJxIYmUFGpo6TTYzMIKozeQIGUpM3oR2YEwEnm5iTNYfpi5MJDMN1LlL1j7lYcph+ONnEMFyVulwCBlLLtb09l4AEJCABCUhAAhKQgATWJGAgtSY4q0lgCAJONjEE1fnIdLKJ+dhyqJ442cRQZOch18km5mFHezEeAk42MR5bqIkESgJ+LK4jNBHQP5romKd/6ANNBJxsoomOeRLoTsAnUt2ZWUMCgxFwMoHB0M5CsP4xCzMO2gknmxgU7+SFO9nE5E1oB0ZGwEBqZAZRnWUTcLKJZdu/Te+dTKANpeWW0T+Wa/s2PXeyiTaULCOB9gQMpNqzsqQEJCABCUhAAhKQgAQkIIGSgIGUjiCBERFwsokRGWOEqjjZxAiNMjKVnGxiZAYZmTpONjEyg6jO5Ak42cTkTWgH5kbAj8XnZtF++6N/9MtzbtL0j7lZtN/+ONlEvzyVJgGfSOkDEhgRAScTGJExRqiK/jFCo4xMJSebGJlBRqaOk02MzCCqM3kCBlKTN6EdmBMBJ5uYkzWH6YuTCQzDdS5S9Y+5WHKYfjjZxDBclbpcAgZSy7W9PZeABCQgAQlIQAISkIAE1iRgILUmOKtJYAgCTjYxBNX5yHSyifnYcqieONnEUGTnIdfJJuZhR3sxHgJONjEeW6iJBEoCfiyuIzQR0D+a6Jinf+gDTQScbKKJjnkS6E7AJ1LdmVlDAoMRcDKBwdDOQrD+MQszDtoJJ5sYFO/khTvZxORNaAdGRsBAamQGUZ1lE3CyiWXbv03vnUygDaXlltE/lmv7Nj13sok2lCwjgfYEDKTas7KkBCQgAQlIQAISkIAEJCCBkoCBlI4ggRERcLKJERljhKo42cQIjTIylZxsYmQGGZk6TjYxMoOozuQJONnE5E1oB+ZGwI/F52bRfvujf/TLc27S9I+5WbTf/jjZRL88lSYBn0jpAxIYEQEnExiRMUaoiv4xQqOMTCUnmxiZQUamjpNNjMwgqjN5AgZSkzehHZgTASebmJM1h+mLkwkMw3UuUvWPuVhymH442cQwXJW6XAIGUsu1vT2XgAQkIAEJSEACEpCABNYk0Okbqeuuu6746Ec/Wlx77bXFGWecsWaT61W7+OKLy4q33XbbegKsJYEJEHCyiQkYaYcqOtnEDuFPpGknm9jMUHfeeWdxxx13lEJOPvnk8nqnSuI111xTPPTQQ0dknX322cWVV155YD9l3/rWtxZnnnnmgf35Rtp25F122WXFueeeW27SHrKq0tVXX70v//LLLy/S7bS8k02kNFyXwOYEOgVSmze3vgQDqPXZWXNaBPxYfFr22ra2+se2iU+rPf1jfXsRyNx1113FjTfeWAq55ZZbine9611HBEZkckM5Tffff3/BzeZLL7003V0GPlUB14FCexu08+CDD+63TX4ETiwvuOCC/Sq0TZAXKdrO90d+unSyiZSG6xLYnEDvr/Y9/PDDBU+P4o+BJRL7rrrqqtgsBx32ffCDHyz3Rd20ThQOeWw/8MADpXzK3XrrrftthZyoQ1tRL22X/Ggr8tM2kRN6RT5LUuSxzdO5SKFTlE/zooxLCawi4GQCqwgtO1//WLb92/TeySbaUKouQxB14YUX7mcSvNx7772VT572C31thWsInh5FgENQxpMhnlCtSnfffXcZROXBGbJ4shRPyOrk8KSLdpCzKjnZxCpC5kugG4FeAymCiSuuuOKABgQUEcS86lWvKj75yU+WQQyFItj4sz/7s7IO9Unf9m3fVi5X/aM+g1Wkm2++eV82bdJWJNYjWFqlZ9RBXpqQme5DHgEZf/njdvKiP6kM1yXQRMDJJpromAcBJxPQD5oI6B9NdJrzePJzyimn7BcikOHvvvvu299XtcJ1COXiFbwow5Ot/AlV5KVLAqDzzjsv3bW/TpAUT8j2d26w4mQTG8CzqgQqCPQaSN1+++1lE9yV4VU8/k477bQyoCHoiQCJACMNMrhDQvr4xz9eLl/96leXyzb/brjhhv12KE9Qg2wCJ9pO9UAH8lfpGe1G/bhLhEzWo1/RXgRz3DkiD51I0U654T8JSEACEpCABEZJIF6/O3z4cGf9eGKUPslCQPoq3iqBPPVq8+SqTg6BGDLyQK6uvPslIIH+CPT6jVQERG94wxv2NXzzm99cPgni3V8CJJ7oEDDFXR8GGwIRAhyWBC9tBzKecEXZc84558DTLhQ4//zz9/W4/vrr99dX6RkFo360gW4xycYLX/jC/Sden/3sZ8sq8cQr6kc7se1SAqsIONnEKkLLzneyiWXbv03vnWyiDaX+ysTrdH0HMbwWmCZu1Ma1SP4GDOXafB9FOSebgIJJAv0R6DWQWqUWgwDByD333FMQiLDO42wCqHiqQ0A0l5S+WjiXPtmP4Qn4sfjwjKfcgv4xZesNr7v+sR5jXs0jcVM31ttIanotr019ytAeN5vTWf3S1/nyoKpt0FTVvpNNVFFxnwTWJ9Drq30ER6R00of3ve995b54AhVPjnjNjvV4whOBVN17wqWQlv/irs2HP/zh/Ro8LYoJItrouV+xxcoLXvCCslS89hevE7I0SaALAScT6EJreWX1j+XZvGuPnWyiK7Gvl4+AJvbwuh9/Z511Vuw6YskrdU35R1So2MF1z6oJJSqqrbWLN2X0kbXQWUkClQTWeiJV9ViZx84XXXRROYEEr+/xF4lX8PgjxRMo1l/2spexKPMIrEgRWJUba/5DBk+7eCIUs+0hin3xOiDt1emZBoJtVIg+5Vx4bfGSSy5pI8IyEigJONmEjrCKgJMJrCK07Hz9Y337cy7nuiBe0+MGL98u1T2hiu+qNg2kuFZgQguePKVPouhJfl2xfu+eqclkEyeddFLhK6CbkrS+BJ4h0OsTKQKYmGghABO4EGRFSgOlWI9JKCLYirKbLPkmisApTfGdVBs903qr1pHH06g0GUSlNFyXgAQkIAEJjJsA522CKQIa/njalP64Lvv4zaZIvAZIqgu0olybJe0wUVe0HUsCOYKr9LW/NvIsIwEJbIfAob074E9vpylbgcAjjzxS/PH//Jbiqc98QiASqCTAHWW/c6hE4849AvqHbtBEQP9oovNM3rf/k/93ccorX7u64AxLPP7448Xxxx/vOWaGtrVLuyGw1qt9u1HVViWwDAIGUcuw87q91D/WJbeMevrHMuy8bi+dbGJdctaTQDWBXl/tq27CvRKQQFsCTibQltQyy+kfy7R7l147kUAXWssr62QTy7O5PR6WgIHUsHyVLoFOBLhQ9mPxTsgWV1j/WJzJO3VY/+iEa3GFmWziySefXFy/7bAEhiJgIDUUWeVKQAISkIAEJCABCUhAArMlYCA1W9PasSkS4PuGo4/208Up2m4bOh86dEj/2AboCbfhtNYTNt4WVD/11FOLY489dgst2YQElkHAK7Zl2NleToiAH4tPyFg7UFX/2AH0CTWpf0zIWDtQ1ckmdgDdJmdNwCdSszavnZsaAScTmJrFtquv/rFd3lNszckmpmi17ensZBPbY21LyyBgILUMO9vLiRBwsomJGGqHajqZwA7hT6Bp/WMCRtqhik42sUP4Nj1LAgZSszSrnZKABCQgAQlIQAISkIAEhiRgIDUkXWVLoCMBJ5voCGxhxZ1sYmEGX6O7TjaxBrQFVXGyiQUZ265uhYCTTWwFs41IoD0BPxZvz2qJJfWPJVq9fZ/1j/aslljSySaWaHX7PCQBn0gNSVfZEuhIwMkEOgJbWHH9Y2EGX6O7TjaxBrQFVXGyiQUZ265uhYCB1FYw24gE2hFwsol2nJZcyskElmz91X3XP1YzWnIJJ5tYsvXt+xAEDKSGoKpMCUhAAhKQgAQkIAEJSGDWBAykZm1eOzc1Ak42MTWLbVdfJ5vYLu8ptuZkE1O02vZ0drKJ7bG2pWUQcLKJZdjZXk6IgB+LT8hYO1BV/9gB9Ak1qX9MyFg7UNXJJnYA3SZnTcAnUrM2r52bGgEnE5iaxbarr/6xXd5TbM3JJqZote3p7GQT22NtS8sgYCC1DDvby4kQcLKJiRhqh2o6mcAO4U+gaf1jAkbaoYpONrFD+DY9SwIGUrM0q52SgAQkIAEJSEACEpCABIYkYCA1JF1lS6AjASeb6AhsYcWdbGJhBl+ju042sQa0BVVxsokFGduuboWAk01sBbONSKA9AT8Wb89qiSX1jyVavX2f9Y/2rJZY0skmlmh1+zwkAZ9IDUlX2RLoSMDJBDoCW1hx/WNhBl+ju042sQa0BVVxsokFGduuboWAgdRWMNuIBNoRcLKJdpyWXMrJBJZs/dV91z9WM1pyCSebWLL17fsQBAykhqCqTAlIQAISkIAEJCABCUhg1gQMpGZtXjs3NQJONjE1i21XXyeb2C7vKbbmZBNTtNr2dHayie2xtqVlEHCyiWXY2V5OiIAfi0/IWDtQVf/YAfQJNal/TMhYO1DVySZ2AN0mZ03AJ1KzNq+dmxoBJxOYmsW2q6/+sV3eU2zNySamaLXt6exkE9tjbUvLIGAgtQw728uJEHCyiYkYaodqOpnADuFPoGn9YwJG2qGKTjaxQ/g2PUsCBlKzNKudkoAEJCABCUhAAhKQgASGJGAgNSRdZUugIwEnm+gIbGHFnWxiYQZfo7tONrEGtAVVcbKJBRnbrm6FgJNNbAWzjUigPQE/Fm/Paokl9Y8lWr19n/WP9qyWWNLJJpZodfs8JAGfSA1JV9kS6EjAyQQ6AltYcf1jYQZfo7tONrEGtAVVcbKJBRnbrm6FgIHUVjDbiATaEXCyiXacllzKyQSWbP3Vfdc/VjNacgknm1iy9e37EAQMpIagqkwJSEACEpCABCQgAQlIYNYE/EZqB+Y94ezvKE4847/cQcs2OXYC8eqWH4yP3VK70U//2A33KbX6la98pTj22GOnpPLWdT32pG/ceptjadDJJsZiCfWYC4FDeyfmp+fSmSn045FHHikeeuih4vTTT5+CuuooAQlIQAISkIAEJCABCVQQ8NW+CijuksCuCHA3mY+BTRKoIvDkk08Wn/rUp6qy3CeBksAnPvEJSUigloCTTdSiMUMCaxEwkFoLm5UkMAwBLpT5GNgkgSoCvEDw6KOPVmW5TwIlgS984QuSkEAtASebqEVjhgTWImAgtRY2K0lAAhKQgAQkIAEJSEACSyZgILVk69v30RE47rjjCj4GNkmgisBRRx1VnHbaaVVZ7pNASeDFL36xJCRQS8DJJmrRmCGBtQg4a99a2KwkgWEIPOtZzyr85flh2M5B6qFDh4oTTzxxDl2xDwMReO5znzuQZMXOgYDnlzlY0T6MiYBPpMZkDXVZPAEnm1i8CzQCcLKJRjxm7hFwsgndoImAk0000TFPAt0JGEh1Z2YNCQxGwMkmBkM7C8FONjELMw7aCSebGBTv5IU72cTkTWgHRkbAQGpkBlEdCUhAAhKQgAQkIAEJSGD8BAykxm8jNVwQASebWJCx1+iqk02sAW1hVZxsYmEG79hdJ5voCMziElhBwMkmVgAyWwLbJOBkE9ukPb22nGxiejbbtsZONrFt4tNqz8kmpmUvtR0/AZ9Ijd9GarggAk42sSBjr9FVJ5tYA9rCqjjZxMIM3rG7TjbREZjFJbCCgIHUCkBmS2CbBJxsYpu0p9eWk01Mz2bb1tjJJrZNfFrtOdnEtOyltuMnYCA1fhupoQQkIAEJSEACEpCABCQwMgIGUiMziOosm4CTTSzb/qt672QTqwiZ72QT+kATASebaKJjngS6E3Cyie7MrCGBwQg42cRgaGch2MkmZmHGQTvhZBOD4p28cCebmLwJ7cDICPhEamQGUZ1lE3CyiWXbf1XvnWxiFSHznWxCH2gi4GQTTXTMk0B3AgZS3ZlZQwKDEXCyicHQzkKwk03MwoyDdsLJJgbFO3nhTjYxeRPagZERMJAamUFURwISkIAEJCABCUhAAhIYP4FDe3c4nx6/mvPR8Iknnih4fcv3lOdj0z578tRTTxVf+tKX9I8+oc5IFsP1448/Xpx44okz6pVd6ZMAT6T8TqpPovOSxfhx/PHHF3yPa5KABDYnYCC1OUMlSEACEpCABCQgAQlIQAILI+AtiYUZ3O5KQAISkIAEJCABCUhAApsTMJDanKESJCABCUhAAhKQgAQkIIGFETCQWpjB7a4EJCABCUhAAhKQgAQksDkBA6nNGSpBAhKQgAQkIAEJSEACElgYAQOphRnc7kpAAhKQgAQkIAEJSEACmxMwkNqcoRIkIAEJSEACEpCABCQggYURMJBamMHtrgQkIAEJSEACEpCABCSwOQEDqc0ZKkECEpCABCQgAQlIQAISWBiBoxfW3426e8011xQPPfRQKePss88urrzyytby0rpR6dprry1OPvnk2Czuv//+4rrrrtvfzlduvPHG/V3ogczLLrusOPfcc/f3uzIsgVtuuaW466679htJbbK/M1vJ65x33nnFpZdempWq3gw7p7n4DL6Tpi6+c/fddxc333xzKSP1v1Se6+sRCLZR++qrry7OPPPM2Fy5DDt2qdf32BJ9yMenlcpbYCWBYBsF29r5Xe96V3HvvfdGtaLNuBOF2/gHZS+//PKocsQy1TPGJM89R2DaeEewDUEXXnhhccEFF8Rm7TL3jy7Hbn5+opEq27b1D+rjc1wjtT3PUcckgckSeNrUisD111//NH+R8u3Yny/3Lrqffvvb3/70HXfccSCLbfaTH+m+++4r97HM096Fb5kX+x988MEj6keey2EIhM1Cer4d+9NlbjfysDv7V6Wom/sDvocMfCBSF98Jn0zrhxyX6xPIbZBvt5H8zne+s7RtbvOqumHHvseWkKt/VFFff1/uD/l2nWR8Ij33xLhQVz72hx3b+Ad1qs5T7A854ZOee6AyTMptkG9XtZr7Q5yXVh2/YcfUt5Affpmfo+p0yf0DGfhsXp/9JgnMkYCv9rUIgblLzN3A9O4K6+wjry5xd2lvMCnv7uR3ldjmbhP5bVLU546maTcE9k5QpS2jdWzCE50777wzdh2x3DvJHKhDgcv2niKyvylhZ8pwZzF/osGTUO72cSexTdJ32lDavAz+wdPGsBdLttnfJuFHjBltkmNLG0rjKrOOfzAOYOv07Yc4DzWdC/r0D954YJxbNWaNi/b0tOH4h3OM1/SAc8Wq8SM/x0T9Jv9A9l4AVY5PqW+xn3GLJ5DIbbq+oSxJ/3iGg/+XS8BAqoXtCZgY4PiLFNvp6xaRF8sYGOtevWM/F1ptLp6i7TZlo32X/RGIE8pZZ511QCgBzd4dvAP70g1ewamzf5Mt46Ir7J7KZB2ZuS55mdgOGU3tRVmX6xNgLMhtwnbTGBGtYRtszs2VNsmxpQ2lcZVZxz+4GOYckaemcYWyffoH8k455ZRW5ynKmtYjwHmE80maYjyJ80+at8l6BOgRdOWyCKbQ5fDhw3lW5bb+UYnFnQshYCDVwtBc5DBQ5GnV4MGJMx8YUxlc4HJ3MS5007x8PS6C25TN67q9OYG91yBKITl/tiOvbSsReOWyoj625i9OorE/XRJI1Z0E03Ks6zs5kf63g3E+TsR25Ne1zNNFLpibxou0rmNLSmP862H/8IfQOLYjP/bHkrGFcYLAiG9U4i/y65Z9+gdthB517bl/cwJVjOMc0XSOYdxI32zBV0h1N/DI4xyE7JDPvjzxpKopPy1fpXua77oE5kzAySZaWLfuJLeqKvXaDkSrZHGhhaymwXGVDPPXJ7CuD+QtIodXJpqePDz88MNltbjIymV03dZ3uhLrXj5s1r1mUXB3mAtfnjK0vfPs2LIO6d3VWdc/sHM8qYwJJphYgIAqtqt61ad/cGGOvLY3bqr0cd9qAjBeJ8XN2JgMguuEJt+gDdrq6/yif6xjNevMiYCB1JatyUVTeveI5vOZeapm7mNwzGdq27LqNtcDAd5Lx5brXJQQEOXfKaSzaaGevtODkbYsgvGAbyE2TY4tmxIcZ32eUqbjBU8KuGjmAjbdv0r7Nv5B0MZfnrrMApfXdXtYAsyQR1AUwRN2xj8YU7rceCW4Qlaa8L30Gyr9I6XjugSeIWAg1cITuPBd525RVT0GtnRwi7tIqRr5xXGa5/puCGDLTVNMQxwnvDp58V46r0vExAWU5c5jfGjOk4uqoEnfqaM67P6wWddW4pW+dExoI8OxpQ2l8ZRZ1z/oQdUrvlzgNp2T1vWP/KbeeAjOX5N1zjEETfjBVVddtQ+IsYRX9wh66sYV2uIpeJrYl56bePKZJ/0jJ+K2BIrCQKqFF1QNOlTjQrfqJBciOdnlg1XkuZwWgXgNgpNWesJjO/KaehQBc3qiqiuPfP44GdadCOvqun83BMIn8uCXbVLk59rFE8ZYRj5Bcn43OPJYOrakNMa/Hvbv6h/YeZ2kf6xDbbd1OI/kwXFs151jIj/8K3qQb8f+WHLdwphD/VVlo45LCUigmoCTTVRzObCXkxIDTgxaZMZ204mO1y4ox10j07QJxJOhmCgiekOg3BRMU44gipNVmyAq5HLnL050sc/luAkwFuT+wXbTGIFPpH88USSxTF+pyXvu2JITGf/2Ov7B2JL7FD1dNe7oH+P3h1xDbJ3feA3bx/knrxNBUHptQhm264Iv8rlBR92YmIJ9JglIYD0CBlItuDGIMejwGk4kvnXhxFg3wFGOOlwQ8Q1EWpe8eI+ZdZ86QGH8KZ8dCZtywmr6TiFej+j6fRs+QTDF64B5IE67PLHAv5r8b/xE56UhNiP4jQkjsBvb2LHv5NjSN9Hh5a3jH4wtXFynF7yMKdi/6byhfwxvz75bwJ6cT8LWrHPt0DR+UAdbcz0SKcadJv+gLOckxqf8uyja5eYffrdKRrTpUgJLJuCrfS2tz6DDgBOvaBFENd0xDrFc6HLHOWZaiv0s/Z4lpTH+9fg+KXwAjfOnTPgIvkFZLqjjDmNaJ3q6yv5cRPFHXU6oacrbTfNc3w2BuOhIv13LbUwQjE90DayreuTYUkVlvPvW9Q+OdcaVmASCC+c2/qN/jNcXqjQLu6a2JohKb9QRJHEuwP6UJ7GeX1/k405Ve+zDtwjc8vNT3m5dffdLQAJFcejpvSQICUhAAhKQgAQkIAEJSEACEmhPwFf72rOypAQkIAEJSEACEpCABCQggZKAgZSOIAEJSEACEpCABCQgAQlIoCMBA6mOwCwuAQlIQAISkIAEJCABCUjAQEofkIAEJCABCUhAAhKQgAQk0JGAgVRHYBaXgAQkIAEJSEACEpCABCRgIKUPSEACEpCABCQgAQlIQAIS6EjAQKojMItLQAISkIAEJCABCUhAAhIwkNIHJCABCUhAAhKQgAQkIAEJdCRgINURmMUlIAEJSEACEpCABCQgAQkYSOkDEpCABCQgAQlIQAISkIAEOhIwkOoIzOISkIAEJCABCUhAAhKQgAQMpPQBCUhAAhKQgAQkIAEJSEACHQkYSHUEZnEJSEACEpCABCQgAQlIQAIGUvqABCQgAQlIQAISkIAEJCCBjgSO7li+svgv/MIvFP/5P//n/byLL764+M7v/M797TGu/Oqv/mpx0kknFW9605v21Xv/+99f/MEf/EHxz//5P9/f95d/+ZfFjTfeWPzET/xEWX4/o2Hlwx/+cCmHOnlqkvdP/sk/yYsXr3zlKw/oeESBFjvoF/Z529ve1qK0RSQgAQlIQAISkIAEJDAMAa6Tf/M3f3Nf+Pd+7/cW559//v42K23KRAWu6f/8z/88Nssl1/hch//RH/1RcdtttxWXX3558ZKXvORAGeo9/PDDZbkDGR02Ng6kUOL000/fv9jngp3AijTmYOq7vuu7ij/8wz88gAojAJ5gJ2DTH/bxt42UB6EEV7SdO9g2dLENCUhAAhKQgAQkIAEJ9EWg6qFFxA1xrdumTK5P/uCB+IQ/HiL8xV/8RRlMpQ84CLC47k8fnuQy22xv/GofShCUROKin8gyD1IifyxLAqU0eo0nahgi1R34BIq7SrQduu1KB9uVgAQkIAEJSEACEpDAJgR4UMGbX2lAgzweIsQTqjZl2ujw0pe+tHzaRFnePuNamqdckWiPdjdNGz+RInD60Ic+dOC1MSLKiCpRMH08R2AQr5jlr5xRLgCTR2KbNoCeyol9ZaG9f+lrcelTHaJcArv86Rj1+YunTyzRjXK/8iu/EmLLYCtAp+3n/aBC6EowlqaIrNmX56XlqtYJ9ngcGSmVxT640I+UV5SteoxJP3lVMY/co45LCUhAAhKQgAQkIAEJ9E0gHk5w3ZomHm7Ek6E2ZdK6devISa+5uZbnFT+u83kadfjw4SNigzpZTfs3DqTe/va3l6/yRSCTv+eIskR9AYjAhov+9NukOgUJTKJeLgcZIQeZERgQcbKNkTBMHvWmbREMAZpyLIlew7jxFIgl+Xn7eT9SXQm4IlEvzeMxY1PCyPxFgiftk5CVPoZEFm0FS9pJA6s8wI0gqirAivZcSkACEpCABCQgAQlIoG8CXFPHdXad7DZlqupyDcxfJK7x04c6BFC8ccbDEtpoig9CRpvlxoEUQCLYiSc28bgslE4jQgIDAoW4+G9SEgiR6HwqJ+oTHAAkYKEP9SgfAUjIyJcETgRQJAKUkEF95IYs8vP2836kulI+Ul7v9a9//YFXCqNcLNOnaewjYKN/9Bee/EXiA7k0oUM4KMv01UXK8iTKICol5roEJCABCUhAAhKQwNQJxAOV6AcPW7iGTgMmrqXZxzV8XC9H+XWXGwdSacMEIvxFQMVFPxfwBCyRNlG8qS5g0pQGXen+dJ1Ai6CPQIUU8iPAYjt0X7cfeb20/TbracCGnmk/64K3KrnUpT9tAsyq+u6TgAQkIAEJSEACEpDAugS4Dk2fGqVy4jq1TZm0Xt068Qht8WAkHqwgmxTLurpd9m802QSvmqUX9tEwCkZwwjuIsU5+uh7l2y6b6hJx8mQs/uKJVZPsAEk/0qAE4LTFXwRS6/Yjr9ekz6o8AlT0jD6G/qvqkU89GIVTtaljGQlIQAISkIAEJCABCfRBgGvquL5O5bGPeIKgp02ZtO6u1zcKpHjiROd5fJam9HU2ZvRLo880j0Agff0sLZfKYz2XQ1ABdIIe5LAdie+10u3YX7UkwIjvoyIfeTxJIkUUm7ef9iPqVS2jHpxIfLfUJfHErOrpGvKaeNW1ER/b1eW7XwISkIAEJCABCUhAAn0T4Jqaa9r8IQzfLbGf/DZl2uhFHMD1fFzHt6mzTpmNX+3j6QhAYrIJlOB1tPjeKIKtyCdwiadF8dgt8qhXFxzkcmiHtkk8aUFG1MUY0T66IZf6VYnIt+qbLfRMU95+2o+0XL4e9cJp0CUNHvPy+WQTaTv0KVjjHOsERehDEBhz6+ftuy0BCUhAAhKQgAQkIIEhCEQMENf+tMF1e+xnO9abylAuTcQAEQfE/ogTYnuI5aGn99IQgpUpAQlIQAISkIAEJCABCUhgrgQ2erVvrlDslwQkIAEJSEACEpCABCQggSYCBlJNdMyTgAQkIAEJSEACEpCABCRQQcBAqgKKuyQgAQlIQAISkIAEJCABCTQRMJBqomOeBCQgAQlIQAISkIAEJCCBCgIGUhVQ3CUBCUhAAhKQgAQkIAEJSKCJgIFUEx3zJCABCUhAAhKQgAQkIAEJVBAwkKqA4i4JSEACEpCABCQgAQlIQAJNBAykmuiYJwEJSEACEpCABCQgAQlIoIKAgVQFFHdJQAISkIAEJCABCUhAAhJoImAg1UTHPAlIQAISkIAEJCABCUhAAhUEDKQqoLhLAhKQgAQkIAEJSEACEpBAEwEDqSY65klAAhKQgAQkIAEJSEACEqggYCBVAcVdEpCABCQgAQlIQAISkIAEmggYSDXRMU8CEpCABCQgAQlIQAISkEAFAQOpCijukoAEJCABCUhAAhKQgAQk0ETAQKqJjnkSkIAEJCABCUhAAhKQgAQqCBhIVUBxlwQkIAEJSEACEpCABCQggSYC/39wN7J861QKQwAAAABJRU5ErkJggg==",
- "text/plain": [
- ""
- ]
- },
- "execution_count": 83,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "test_dataset[10][1]['content'][0]['image']"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 84,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "9yHJMKHNWcMc",
- "outputId": "5cedc6aa-e375-4026-92f2-6be4d0e50d91"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'1'"
- ]
- },
- "execution_count": 84,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "output = generate_text_from_sample(model, processor, test_dataset[10])\n",
- "output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NUr6jmnAIlh1"
- },
- "source": [
- "The model has successfully learned to respond to the queries as specified in the dataset. We've achieved our goal! 🎉✨"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "Z_Ns3p0Dhjbr"
- },
- "source": [
- "💻 I’ve developed an example application to test the model, which you can find [here](https://huggingface.co/spaces/sergiopaniego/Qwen2-VL-7B-trl-sft-ChartQA). You can easily compare it with another Space featuring the pre-trained model, available [here](https://huggingface.co/spaces/GanymedeNil/Qwen2-VL-7B)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 96,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 822
- },
- "id": "dYJJ6ASKhJ5k",
- "outputId": "f010e580-a4a1-470b-84c4-95ee999774b8"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "\n",
- " \n",
- " "
- ],
- "text/plain": [
- ""
- ]
- },
- "execution_count": 96,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "from IPython.display import IFrame\n",
- "\n",
- "IFrame(src=\"https://sergiopaniego-qwen2-vl-7b-trl-sft-chartqa.hf.space\", width=1000, height=800)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "daUMWw5xxhSc"
- },
- "source": [
- "## 6. Compare Fine-Tuned Model vs. Base Model + Prompting 📊\n",
- "\n",
- "We have explored how fine-tuning the VLM can be a valuable option for adapting it to our specific needs. Another approach to consider is directly using prompting or implementing a RAG system, which is covered in another [recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms).\n",
- "\n",
- "Fine-tuning a VLM requires significant amounts of data and computational resources, which can incur costs. In contrast, we can experiment with prompting to see if we can achieve similar results without the overhead of fine-tuning.\n",
- "\n",
- "Let's again clean up the GPU memory to ensure optimal performance 🧹"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 87,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "ei-OZGGx4lHe",
- "outputId": "81bee1a4-4860-464a-bc6a-ae2fb1695236"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GPU allocated memory: 0.02 GB\n",
- "GPU reserved memory: 0.27 GB\n"
- ]
- }
- ],
- "source": [
- "clear_memory()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "9NApMx5S4-sh"
- },
- "source": [
- "🏗️ First, we will load the baseline model following the same pipeline as before.\n"
- ]
- },
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- "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
- ]
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- "metadata": {},
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- "source": [
- "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
- " model_id,\n",
- " device_map=\"auto\",\n",
- " torch_dtype=torch.bfloat16,\n",
- ")\n",
- "\n",
- "processor = Qwen2VLProcessor.from_pretrained(model_id)"
- ]
- },
- {
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- "source": [
- "📜 In this case, we will again use the previous sample, but this time we will include the system message as follows. This addition helps to contextualize the input for the model, potentially improving its response accuracy.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 93,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "SNMKKvvZxqR8",
- "outputId": "fefa3c3a-f666-4c8f-fdef-38b16539c069"
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- "outputs": [
- {
- "data": {
- "text/plain": [
- "[{'role': 'system',\n",
- " 'content': [{'type': 'text',\n",
- " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
- " {'role': 'user',\n",
- " 'content': [{'type': 'image',\n",
- " 'image': },\n",
- " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
- ]
- },
- "execution_count": 93,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_dataset[0][:2]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "fou6XKGM5Uii"
- },
- "source": [
- "Let's see how it performs!"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 94,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 36
- },
- "id": "eN3NkkpgR4do",
- "outputId": "5ed3fb26-580a-4c07-d626-cf8c66619b81"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'Yes'"
- ]
- },
- "execution_count": 94,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "text = processor.apply_chat_template(\n",
- " train_dataset[0][:2], tokenize=False, add_generation_prompt=True\n",
- ")\n",
- "\n",
- "image_inputs, _ = process_vision_info(train_dataset[0])\n",
- "\n",
- "inputs = processor(\n",
- " text=[text],\n",
- " images=image_inputs,\n",
- " return_tensors=\"pt\",\n",
- ")\n",
- "\n",
- "inputs = inputs.to(\"cuda\")\n",
- "\n",
- "generated_ids = model.generate(**inputs, max_new_tokens=1024)\n",
- "generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
- "\n",
- "output_text = processor.batch_decode(\n",
- " generated_ids_trimmed,\n",
- " skip_special_tokens=True,\n",
- " clean_up_tokenization_spaces=False\n",
- ")\n",
- "\n",
- "output_text[0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "o9Id3dzV5Wwy"
- },
- "source": [
- "💡 As we can see, the model generates the correct answer using the pretrained model along with the additional system message, without any training. This approach may serve as a viable alternative to fine-tuning, depending on the specific use case."
- ]
- },
- {
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- "source": [
- "## 7. Continuing the Learning Journey 🧑🎓️\n",
- "\n",
- "To further enhance your understanding and skills in working with multimodal models, check out the following resources:\n",
- "\n",
- "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)\n",
- "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)\n",
- "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main)\n",
- "- [Quantize Your Qwen2-VL Model with AutoAWQ](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#quantize-your-own-model-with-autoawq)\n",
- "- [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm)\n",
- "- [Hugging Face Llama Recipes: SFT for VLM](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/sft_vlm.py)\n",
- "- [Hugging Face Llama Recipes: PEFT Fine-Tuning](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/peft_finetuning.py)\n",
- "- [Hugging Face Blog: IDEFICS2](https://huggingface.co/blog/idefics2)\n",
- "\n",
- "These resources will help you deepen your knowledge and skills in multimodal learning.\n",
- "\n"
- ]
- }
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From 1dba22eda3ac97a8365ac5d82fc6cc23e8f254cb Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:25:58 +0530
Subject: [PATCH 4/8] Fixed Invalid Notebook error
---
notebooks/en/fine_tuning_vlm_trl.ipynb | 2243 ++++++++++++++++++++++++
1 file changed, 2243 insertions(+)
create mode 100644 notebooks/en/fine_tuning_vlm_trl.ipynb
diff --git a/notebooks/en/fine_tuning_vlm_trl.ipynb b/notebooks/en/fine_tuning_vlm_trl.ipynb
new file mode 100644
index 00000000..797cbd6a
--- /dev/null
+++ b/notebooks/en/fine_tuning_vlm_trl.ipynb
@@ -0,0 +1,2243 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "vKadZFQ2IdJb"
+ },
+ "source": [
+ "# Fine-Tuning a Vision Language Model (Qwen2-VL-7B) with the Hugging Face Ecosystem (TRL)\n",
+ "\n",
+ "\n",
+ "\n",
+ "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JATmSI8mcyW2"
+ },
+ "source": [
+ "\ud83d\udea8 **WARNING**: This notebook is resource-intensive and requires substantial computational power. If you\u2019re running this in Colab, it will utilize an A100 GPU.\n",
+ "\n",
+ "In this recipe, we\u2019ll demonstrate how to fine-tune a [Vision Language Model (VLM)](https://huggingface.co/blog/vlms) using the Hugging Face ecosystem, specifically with the [Transformer Reinforcement Learning library (TRL)](https://huggingface.co/docs/trl/index).\n",
+ "\n",
+ "**\ud83c\udf1f Model & Dataset Overview**\n",
+ "\n",
+ "We\u2019ll be fine-tuning the [Qwen2-VL-7B](https://qwenlm.github.io/blog/qwen2-vl/) model on the [ChartQA](https://huggingface.co/datasets/HuggingFaceM4/ChartQA) dataset. This dataset includes images of various chart types paired with question-answer pairs\u2014ideal for enhancing the model's visual question-answering capabilities.\n",
+ "\n",
+ "**\ud83d\udcd6 Additional Resources**\n",
+ "\n",
+ "If you\u2019re interested in more VLM applications, check out:\n",
+ "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms): where I guide you through building a RAG system using Document Retrieval (ColPali) and Vision Language Models (VLMs).\n",
+ "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl): an excellent deep dive into fine-tuning multimodal LLMs with TRL.\n",
+ "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main): a collection of engaging notebooks on cutting-edge vision and multimodal AI topics.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "QoD6dxPeXDKR"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gSHmDKNFoqjC"
+ },
+ "source": [
+ "## 1. Install Dependencies\n",
+ "\n",
+ "Let\u2019s start by installing the essential libraries we\u2019ll need for fine-tuning! \ud83d\ude80\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "GCMhPmFdIGSb",
+ "outputId": "016a9f29-9c8d-42c5-9187-813f5bdeb536"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
+ "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "J4pAvoQaOJ1M"
+ },
+ "source": [
+ "We\u2019ll also need to install an earlier version of *PyTorch*, as the latest version has an issue that currently prevents this notebook from running correctly. You can learn more about the issue [here](https://github.com/pytorch/pytorch/issues/138340) and consider updating to the latest version once it\u2019s resolved."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "D8iRteA4oXVj",
+ "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "V0-2Lso6wkIh"
+ },
+ "source": [
+ "Log in to Hugging Face to upload your fine-tuned model! \ud83d\udddd\ufe0f\n",
+ "\n",
+ "You\u2019ll need to authenticate with your Hugging Face account to save and share your model directly from this notebook.\n"
+ ]
+ },
+ {
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+ "VBox(children=(HTML(value='
},\n",
+ " {'type': 'text',\n",
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+ ]
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+ ]
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+ {
+ "cell_type": "markdown",
+ "metadata": {
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+ },
+ "source": [
+ "## 3. Load Model and Check Performance! \ud83e\udd14\n",
+ "\n",
+ "Now that we\u2019ve loaded the dataset, let\u2019s start by loading the model and evaluating its performance using a sample from the dataset. We\u2019ll be using [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct), a Vision Language Model (VLM) capable of understanding both visual data and text.\n",
+ "\n",
+ "If you're exploring alternatives, consider these open-source options:\n",
+ "- Meta AI's [Llama-3.2-11B-Vision](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct)\n",
+ "- Mistral AI's [Pixtral-12B](https://huggingface.co/mistralai/Pixtral-12B-2409)\n",
+ "- Allen AI's [Molmo-7B-D-0924](https://huggingface.co/allenai/Molmo-7B-D-0924)\n",
+ "\n",
+ "Additionally, you can check the Leaderboards, such as the [WildVision Arena](https://huggingface.co/spaces/WildVision/vision-arena) or the [OpenVLM Leaderboard](https://huggingface.co/spaces/opencompass/open_vlm_leaderboard), to find the best-performing VLMs.\n",
+ "\n",
+ "\n"
+ ]
+ },
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+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+ ]
+ },
+ {
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+ "0it [00:00, ?it/s]"
+ ]
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+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import torch\n",
+ "from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor\n",
+ "\n",
+ "model_id = \"Qwen/Qwen2-VL-7B-Instruct\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2HobU2iPUDWL"
+ },
+ "source": [
+ "Next, we\u2019ll load the model and the tokenizer to prepare for inference."
+ ]
+ },
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+ ]
+ },
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+ },
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+ {
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+ "application/vnd.jupyter.widget-view+json": {
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+ },
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+ {
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+ },
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "62ed3c82cffe40c98db49ef61f69626b",
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+ ]
+ },
+ "metadata": {},
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+ {
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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
+ ]
+ },
+ {
+ "data": {
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+ ]
+ },
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+ ]
+ },
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+ "output_type": "display_data"
+ },
+ {
+ "data": {
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+ ]
+ },
+ "metadata": {},
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+ },
+ {
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+ ]
+ },
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+ },
+ {
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+ ]
+ },
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+ },
+ {
+ "data": {
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
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+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7JtKcuuXUGUT"
+ },
+ "source": [
+ "To evaluate the model's performance, we\u2019ll use a sample from the dataset. First, let\u2019s take a look at the internal structure of this sample.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "i-eIIdL9lqJJ",
+ "outputId": "02eda1d8-f6e8-43e7-85f4-b58e50370da3"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]},\n",
+ " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Yes'}]}]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hLaWWJk_RkVU"
+ },
+ "source": [
+ "We\u2019ll use the sample without the system message to assess the VLM's raw understanding. Here\u2019s the input we will use:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ytnr1rePOamM",
+ "outputId": "b38d536e-bfa2-49e8-eb0f-22c1c1cbcff2"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "3IK2HOMuRtY_"
+ },
+ "source": [
+ "Now, let\u2019s take a look at the chart corresponding to the sample. Can you answer the query based on the visual information?\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376
+ },
+ "id": "QavnLzjJUbxf",
+ "outputId": "0b935e4d-3b13-4676-f3cc-1da64bc828ab"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "gpLfsCUtUW6I"
+ },
+ "source": [
+ "Let\u2019s create a method that takes the model, processor, and sample as inputs to generate the model's answer. This will allow us to streamline the inference process and easily evaluate the VLM's performance.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "_MoRTjFcE8qD"
+ },
+ "outputs": [],
+ "source": [
+ "from qwen_vl_utils import process_vision_info\n",
+ "\n",
+ "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
+ " # Prepare the text input by applying the chat template\n",
+ " text_input = processor.apply_chat_template(\n",
+ " sample[1:2], # Use the sample without the system message\n",
+ " tokenize=False,\n",
+ " add_generation_prompt=True\n",
+ " )\n",
+ "\n",
+ " # Process the visual input from the sample\n",
+ " image_inputs, _ = process_vision_info(sample)\n",
+ "\n",
+ " # Prepare the inputs for the model\n",
+ " model_inputs = processor(\n",
+ " text=[text_input],\n",
+ " images=image_inputs,\n",
+ " return_tensors=\"pt\",\n",
+ " ).to(device) # Move inputs to the specified device\n",
+ "\n",
+ " # Generate text with the model\n",
+ " generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)\n",
+ "\n",
+ " # Trim the generated ids to remove the input ids\n",
+ " trimmed_generated_ids = [\n",
+ " out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)\n",
+ " ]\n",
+ "\n",
+ " # Decode the output text\n",
+ " output_text = processor.batch_decode(\n",
+ " trimmed_generated_ids,\n",
+ " skip_special_tokens=True,\n",
+ " clean_up_tokenization_spaces=False\n",
+ " )\n",
+ "\n",
+ " return output_text[0] # Return the first decoded output text"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "5UeNiMJC_uCk",
+ "outputId": "6b7c1260-9980-442f-9d55-a8d7ebedaf94"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Example of how to call the method with sample:\n",
+ "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ysh0e9DRUfF-"
+ },
+ "source": [
+ "While the model successfully retrieves the correct visual information, it struggles to answer the question accurately. This indicates that fine-tuning might be the key to enhancing its performance. Let\u2019s proceed with the fine-tuning process!\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Sw3b76rawti6"
+ },
+ "source": [
+ "**Remove Model and Clean GPU**\n",
+ "\n",
+ "Before we proceed with training the model in the next section, let's clear the current variables and clean the GPU to free up resources.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dxkXZuUkvy8j"
+ },
+ "outputs": [],
+ "source": [
+ "import gc\n",
+ "import time\n",
+ "\n",
+ "def clear_memory():\n",
+ " # Delete variables if they exist in the current global scope\n",
+ " if 'inputs' in globals(): del globals()['inputs']\n",
+ " if 'model' in globals(): del globals()['model']\n",
+ " if 'processor' in globals(): del globals()['processor']\n",
+ " if 'trainer' in globals(): del globals()['trainer']\n",
+ " if 'peft_model' in globals(): del globals()['peft_model']\n",
+ " if 'bnb_config' in globals(): del globals()['bnb_config']\n",
+ " time.sleep(2)\n",
+ "\n",
+ " # Garbage collection and clearing CUDA memory\n",
+ " gc.collect()\n",
+ " time.sleep(2)\n",
+ " torch.cuda.empty_cache()\n",
+ " torch.cuda.synchronize()\n",
+ " time.sleep(2)\n",
+ " gc.collect()\n",
+ " time.sleep(2)\n",
+ "\n",
+ " print(f\"GPU allocated memory: {torch.cuda.memory_allocated() / 1024**3:.2f} GB\")\n",
+ " print(f\"GPU reserved memory: {torch.cuda.memory_reserved() / 1024**3:.2f} GB\")\n",
+ "\n",
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "YIZOIVEzQqNg"
+ },
+ "source": [
+ "## 4. Fine-Tune the Model using TRL\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yIrR9gP2z90z"
+ },
+ "source": [
+ "### 4.1 Load the Quantized Model for Training \u2699\ufe0f\n",
+ "\n",
+ "Next, we\u2019ll load the quantized model using [bitsandbytes](https://huggingface.co/docs/bitsandbytes/main/en/index). If you want to learn more about quantization, check out [this blog post](https://huggingface.co/blog/merve/quantization) or [this one](https://www.maartengrootendorst.com/blog/quantization/).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "b4e7dc3a0b6643a1b2c4124a11f1a932",
+ "1065fc621b1f4ae38e021e464ba28340",
+ "d66b6961a23f4c99872521d8b12b3c93",
+ "67c2b6c826b4437ca9f6f354af959e9d",
+ "dd37b7d9cd2a456986ac59ced8a35205",
+ "1bf49b9b487442df874672b30e0af8da",
+ "18e8ee9a09ab46c2833646651409b8b0",
+ "dd477ac59fa6470ca0ef1f59c39f43cb",
+ "df149f2746544b4a8c3cac6a2365e6db",
+ "1b60256c2fa440ccbb085ada32702b1e",
+ "7531040fc6c743e8bb85dcf07eb73560"
+ ]
+ },
+ "id": "zm_bJRrXsESg",
+ "outputId": "5a3ccdc6-9d40-43c8-df9e-9222a0656c2b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "b4e7dc3a0b6643a1b2c4124a11f1a932",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from transformers import BitsAndBytesConfig\n",
+ "\n",
+ "# BitsAndBytesConfig int-4 config\n",
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit=True,\n",
+ " bnb_4bit_use_double_quant=True,\n",
+ " bnb_4bit_quant_type=\"nf4\",\n",
+ " bnb_4bit_compute_dtype=torch.bfloat16\n",
+ ")\n",
+ "\n",
+ "# Load model and tokenizer\n",
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ " quantization_config=bnb_config\n",
+ ")\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "65wfO29isQlX"
+ },
+ "source": [
+ "### 4.2 Set Up QLoRA and SFTConfig \ud83d\ude80\n",
+ "\n",
+ "Next, we will configure [QLoRA](https://github.com/artidoro/qlora) for our training setup. QLoRA enables efficient fine-tuning of large language models while significantly reducing the memory footprint compared to traditional methods. Unlike standard LoRA, which reduces memory usage by applying a low-rank approximation, QLoRA takes it a step further by quantizing the weights of the LoRA adapters. This leads to even lower memory requirements and improved training efficiency, making it an excellent choice for optimizing our model's performance without sacrificing quality.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ITmkRHWCKYjf",
+ "outputId": "3ca824c9-4aca-4d5b-e942-7a1705939e08"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "trainable params: 2,523,136 || all params: 8,293,898,752 || trainable%: 0.0304\n"
+ ]
+ }
+ ],
+ "source": [
+ "from peft import LoraConfig, get_peft_model\n",
+ "\n",
+ "# Configure LoRA\n",
+ "peft_config = LoraConfig(\n",
+ " lora_alpha=16,\n",
+ " lora_dropout=0.05,\n",
+ " r=8,\n",
+ " bias=\"none\",\n",
+ " target_modules=[\"q_proj\", \"v_proj\"],\n",
+ " task_type=\"CAUSAL_LM\",\n",
+ ")\n",
+ "\n",
+ "# Apply PEFT model adaptation\n",
+ "peft_model = get_peft_model(model, peft_config)\n",
+ "\n",
+ "# Print trainable parameters\n",
+ "peft_model.print_trainable_parameters()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "K5zzHM2GVtxD"
+ },
+ "source": [
+ "We will use Supervised Fine-Tuning (SFT) to refine our model\u2019s performance on the task at hand. To do this, we'll define the training arguments using the [SFTConfig](https://huggingface.co/docs/trl/sft_trainer) class from the [TRL library](https://huggingface.co/docs/trl/index). SFT allows us to provide labeled data, helping the model learn to generate more accurate responses based on the input it receives. This approach ensures that the model is tailored to our specific use case, leading to better performance in understanding and responding to visual queries.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "SbqX1pQUKaSM"
+ },
+ "outputs": [],
+ "source": [
+ "from trl import SFTConfig\n",
+ "\n",
+ "# Configure training arguments\n",
+ "training_args = SFTConfig(\n",
+ " output_dir=\"qwen2-7b-instruct-trl-sft-ChartQA\", # Directory to save the model\n",
+ " num_train_epochs=3, # Number of training epochs\n",
+ " per_device_train_batch_size=4, # Batch size for training\n",
+ " per_device_eval_batch_size=4, # Batch size for evaluation\n",
+ " gradient_accumulation_steps=8, # Steps to accumulate gradients\n",
+ " gradient_checkpointing=True, # Enable gradient checkpointing for memory efficiency\n",
+ " # Optimizer and scheduler settings\n",
+ " optim=\"adamw_torch_fused\", # Optimizer type\n",
+ " learning_rate=2e-4, # Learning rate for training\n",
+ " lr_scheduler_type=\"constant\", # Type of learning rate scheduler\n",
+ " # Logging and evaluation\n",
+ " logging_steps=10, # Steps interval for logging\n",
+ " eval_steps=10, # Steps interval for evaluation\n",
+ " eval_strategy=\"steps\", # Strategy for evaluation\n",
+ " save_strategy=\"steps\", # Strategy for saving the model\n",
+ " save_steps=20, # Steps interval for saving\n",
+ " metric_for_best_model=\"eval_loss\", # Metric to evaluate the best model\n",
+ " greater_is_better=False, # Whether higher metric values are better\n",
+ " load_best_model_at_end=True, # Load the best model after training\n",
+ " # Mixed precision and gradient settings\n",
+ " bf16=True, # Use bfloat16 precision\n",
+ " tf32=True, # Use TensorFloat-32 precision\n",
+ " max_grad_norm=0.3, # Maximum norm for gradient clipping\n",
+ " warmup_ratio=0.03, # Ratio of total steps for warmup\n",
+ " # Hub and reporting\n",
+ " push_to_hub=True, # Whether to push model to Hugging Face Hub\n",
+ " report_to=\"wandb\", # Reporting tool for tracking metrics\n",
+ " # Gradient checkpointing settings\n",
+ " gradient_checkpointing_kwargs={\"use_reentrant\": False}, # Options for gradient checkpointing\n",
+ " # Dataset configuration\n",
+ " dataset_text_field=\"\", # Text field in dataset\n",
+ " dataset_kwargs={\"skip_prepare_dataset\": True}, # Additional dataset options\n",
+ " #max_seq_length=1024 # Maximum sequence length for input\n",
+ ")\n",
+ "\n",
+ "training_args.remove_unused_columns = False # Keep unused columns in dataset"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pOUrD9P-y-Kf"
+ },
+ "source": [
+ "### 4.3 Training the Model \ud83c\udfc3"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wjQGt-iZVyef"
+ },
+ "source": [
+ "We will log our training progress using [Weights & Biases (W&B)](https://wandb.ai/). Let\u2019s connect our notebook to W&B to capture essential information during training.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 228
+ },
+ "id": "ckVfXDWsoF4Y",
+ "outputId": "bb7ce99c-ed2b-481c-a11f-27272ce8da91"
+ },
+ "outputs": [],
+ "source": [
+ "import wandb\n",
+ "\n",
+ "wandb.init(\n",
+ " project=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
+ " name=\"qwen2-7b-instruct-trl-sft-ChartQA\", # change this\n",
+ " config=training_args,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ucTUbGURV2_-"
+ },
+ "source": [
+ "We need a collator function to properly retrieve and batch the data during the training procedure. This function will handle the formatting of our dataset inputs, ensuring they are correctly structured for the model. Let's define the collator function below.\n",
+ "\n",
+ "\ud83d\udc49 Check out the TRL official example [scripts]( https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87) for more details.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "id": "pAzDovzylQeZ"
+ },
+ "outputs": [],
+ "source": [
+ "# Create a data collator to encode text and image pairs\n",
+ "def collate_fn(examples):\n",
+ " # Get the texts and images, and apply the chat template\n",
+ " texts = [processor.apply_chat_template(example, tokenize=False) for example in examples] # Prepare texts for processing\n",
+ " image_inputs = [process_vision_info(example)[0] for example in examples] # Process the images to extract inputs\n",
+ "\n",
+ " # Tokenize the texts and process the images\n",
+ " batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True) # Encode texts and images into tensors\n",
+ "\n",
+ " # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
+ " labels = batch[\"input_ids\"].clone() # Clone input IDs for labels\n",
+ " labels[labels == processor.tokenizer.pad_token_id] = -100 # Mask padding tokens in labels\n",
+ "\n",
+ " # Ignore the image token index in the loss computation (model specific)\n",
+ " if isinstance(processor, Qwen2VLProcessor): # Check if the processor is Qwen2VLProcessor\n",
+ " image_tokens = [151652, 151653, 151655] # Specific image token IDs for Qwen2VLProcessor\n",
+ " else:\n",
+ " image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)] # Convert image token to ID\n",
+ "\n",
+ " # Mask image token IDs in the labels\n",
+ " for image_token_id in image_tokens:\n",
+ " labels[labels == image_token_id] = -100 # Mask image token IDs in labels\n",
+ "\n",
+ " batch[\"labels\"] = labels # Add labels to the batch\n",
+ "\n",
+ " return batch # Return the prepared batch"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "skbpTuJlV8qN"
+ },
+ "source": [
+ "Now, we will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "k_jk-U7ULYtA",
+ "outputId": "0dc465a9-1744-4b9a-d090-22a63f2e48de"
+ },
+ "outputs": [],
+ "source": [
+ "from trl import SFTTrainer\n",
+ "\n",
+ "trainer = SFTTrainer(\n",
+ " model=model,\n",
+ " args=training_args,\n",
+ " train_dataset=train_dataset,\n",
+ " eval_dataset=eval_dataset,\n",
+ " data_collator=collate_fn,\n",
+ " peft_config=peft_config,\n",
+ " processing_class=processor.tokenizer,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NlDsh4WvWCx0"
+ },
+ "source": [
+ "Time to Train the Model! \ud83c\udf89"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "p1rgMTBDLboO"
+ },
+ "outputs": [],
+ "source": [
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "w6CykSCtX-Xa"
+ },
+ "source": [
+ "Let's save the results \ud83d\udcbe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "27d3420d65a545e29cbdae604caa32f3",
+ "37925f7df7324bcb8f7d07389730cf82",
+ "174ecb5e6cd54b2989887e08f7b7ac04",
+ "3b50f08adb95437ead16c8303c971470",
+ "8d9cf624237b4480a2e8b430c7a5e006",
+ "31be17b18a5e48b498bd009692f4a2d6",
+ "dfa21476037b465b9e2143d027433f40",
+ "d0e8e5e0352448ff9266f06b9f97b092",
+ "7dece8a5ac7b4ecf91420f01d60f161b",
+ "e525f59c59f74a2c97ab8a72786127ed",
+ "32f97bb4d25d4d32958d35ac3e897ab9"
+ ]
+ },
+ "id": "tE8usZw0lgrL",
+ "outputId": "455a0714-04b7-4078-ca3f-ccba0ad01f13"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "27d3420d65a545e29cbdae604caa32f3",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "trainer.save_model(training_args.output_dir)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6yx_sGW42dN3"
+ },
+ "source": [
+ "## 5. Testing the Fine-Tuned Model \ud83d\udd0d\n",
+ "\n",
+ "Now that we've successfully fine-tuned our Vision Language Model (VLM), it's time to evaluate its performance! In this section, we will test the model using examples from the ChartQA dataset to see how well it answers questions based on chart images. Let's dive in and explore the results! \ud83d\ude80\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "i0KEPu6qYKqn"
+ },
+ "source": [
+ "Let's clean up the GPU memory to ensure optimal performance \ud83e\uddf9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Ttx6EK8Uy8t0"
+ },
+ "outputs": [],
+ "source": [
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "HwCTPHsfujn2"
+ },
+ "source": [
+ "We will reload the base model using the same pipeline as before."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "dc179daa0be34359a3c9ec0224537c53",
+ "2b21097352c34b2e8e91f86e20bf834c",
+ "f3c4e0dd2e274dce8e3c74ca58796581",
+ "57e326d7a47242d09c6819ec367d87ed",
+ "8b1ba107530f43818c2c9a159150a6a4",
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+ "7787baba17db494590ccc37d3017002c",
+ "964f75e681d6498592ff634a22665a70"
+ ]
+ },
+ "id": "EFqTNUud2lA7",
+ "outputId": "32f3a882-0fed-4527-ca83-74857afe658a"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "dc179daa0be34359a3c9ec0224537c53",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lRAPEYKkYSkB"
+ },
+ "source": [
+ "We will attach the trained adapter to the pretrained model. This adapter contains the fine-tuning adjustments we made during training, allowing the base model to leverage the new knowledge without altering its core parameters. By integrating the adapter, we can enhance the model's capabilities while maintaining its original structure.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 81,
+ "referenced_widgets": [
+ "9f91c4c646c14451a8d9013ff7c8b754",
+ "1e3329ab17004575adf04fdfbadcf477",
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+ "ca03b3317b9c4700b9fb36a81a7928b7",
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+ "fdda9bb09ce04f32b66c685a4551a62e",
+ "0fb1f35201994c61a18798a521f0808b"
+ ]
+ },
+ "id": "mQi2xBXk4sHe",
+ "outputId": "1096bbce-04e1-475d-c7ea-090ef2e5bf5b"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9f91c4c646c14451a8d9013ff7c8b754",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_config.json: 0%| | 0.00/650 [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1a953c336a9049b3a46f1895bcc03ed4",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "adapter_model.safetensors: 0%| | 0.00/10.1M [00:00, ?B/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adapter_path = \"sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA\"\n",
+ "model.load_adapter(adapter_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pqryChyLWRmR"
+ },
+ "source": [
+ "We will utilize the previous sample from the dataset that the model initially struggled to answer correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3X9YGJUezZr6",
+ "outputId": "0598cff6-dae8-4496-bef7-82e2015d12bf"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 79,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 376
+ },
+ "id": "2hLJrxxTVn6x",
+ "outputId": "ee8fd396-f73b-4b8d-e10f-e4430a6a9b13"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/jpeg": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 80,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "pdb9vErmzdAf",
+ "outputId": "e5059c15-c1ec-4f9b-d642-820cac72bb59"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Yes'"
+ ]
+ },
+ "execution_count": 81,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "swibyq5AWctZ"
+ },
+ "source": [
+ "Since this sample is drawn from the training set, the model has encountered it during training, which may be seen as a form of cheating. To gain a more comprehensive understanding of the model's performance, we will also evaluate it using an unseen sample.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2fEa9ChjZsJw"
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "czZSBgnoef1E",
+ "outputId": "350d3520-4973-444e-f2bc-12e44d548fe6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'What is the value of Slovenia in the graph?'}]}]"
+ ]
+ },
+ "execution_count": 82,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset[10][:2]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 617
+ },
+ "id": "ATuQ6ZS6eirO",
+ "outputId": "c3adc0fd-0fdc-4ff4-cc4e-14b4d9039323"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 83,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test_dataset[10][1]['content'][0]['image']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "9yHJMKHNWcMc",
+ "outputId": "5cedc6aa-e375-4026-92f2-6be4d0e50d91"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'1'"
+ ]
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "output = generate_text_from_sample(model, processor, test_dataset[10])\n",
+ "output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NUr6jmnAIlh1"
+ },
+ "source": [
+ "The model has successfully learned to respond to the queries as specified in the dataset. We've achieved our goal! \ud83c\udf89\u2728"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z_Ns3p0Dhjbr"
+ },
+ "source": [
+ "\ud83d\udcbb I\u2019ve developed an example application to test the model, which you can find [here](https://huggingface.co/spaces/sergiopaniego/Qwen2-VL-7B-trl-sft-ChartQA). You can easily compare it with another Space featuring the pre-trained model, available [here](https://huggingface.co/spaces/GanymedeNil/Qwen2-VL-7B)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 96,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 822
+ },
+ "id": "dYJJ6ASKhJ5k",
+ "outputId": "f010e580-a4a1-470b-84c4-95ee999774b8"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 96,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from IPython.display import IFrame\n",
+ "\n",
+ "IFrame(src=\"https://sergiopaniego-qwen2-vl-7b-trl-sft-chartqa.hf.space\", width=1000, height=800)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "daUMWw5xxhSc"
+ },
+ "source": [
+ "## 6. Compare Fine-Tuned Model vs. Base Model + Prompting \ud83d\udcca\n",
+ "\n",
+ "We have explored how fine-tuning the VLM can be a valuable option for adapting it to our specific needs. Another approach to consider is directly using prompting or implementing a RAG system, which is covered in another [recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms).\n",
+ "\n",
+ "Fine-tuning a VLM requires significant amounts of data and computational resources, which can incur costs. In contrast, we can experiment with prompting to see if we can achieve similar results without the overhead of fine-tuning.\n",
+ "\n",
+ "Let's again clean up the GPU memory to ensure optimal performance \ud83e\uddf9"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ei-OZGGx4lHe",
+ "outputId": "81bee1a4-4860-464a-bc6a-ae2fb1695236"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GPU allocated memory: 0.02 GB\n",
+ "GPU reserved memory: 0.27 GB\n"
+ ]
+ }
+ ],
+ "source": [
+ "clear_memory()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9NApMx5S4-sh"
+ },
+ "source": [
+ "\ud83c\udfd7\ufe0f First, we will load the baseline model following the same pipeline as before.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "1102501e7373424a8340e8b874ef616b",
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+ ]
+ },
+ "id": "VbdSklNAR1q-",
+ "outputId": "a4d7d4e9-d5a2-47d2-88bb-85867f05f8fb"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "1102501e7373424a8340e8b874ef616b",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading checkpoint shards: 0%| | 0/5 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
+ " model_id,\n",
+ " device_map=\"auto\",\n",
+ " torch_dtype=torch.bfloat16,\n",
+ ")\n",
+ "\n",
+ "processor = Qwen2VLProcessor.from_pretrained(model_id)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1bwatIlf5EDE"
+ },
+ "source": [
+ "\ud83d\udcdc In this case, we will again use the previous sample, but this time we will include the system message as follows. This addition helps to contextualize the input for the model, potentially improving its response accuracy.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 93,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SNMKKvvZxqR8",
+ "outputId": "fefa3c3a-f666-4c8f-fdef-38b16539c069"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[{'role': 'system',\n",
+ " 'content': [{'type': 'text',\n",
+ " 'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+ " {'role': 'user',\n",
+ " 'content': [{'type': 'image',\n",
+ " 'image': },\n",
+ " {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
+ ]
+ },
+ "execution_count": 93,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train_dataset[0][:2]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "fou6XKGM5Uii"
+ },
+ "source": [
+ "Let's see how it performs!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 94,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 36
+ },
+ "id": "eN3NkkpgR4do",
+ "outputId": "5ed3fb26-580a-4c07-d626-cf8c66619b81"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'Yes'"
+ ]
+ },
+ "execution_count": 94,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "text = processor.apply_chat_template(\n",
+ " train_dataset[0][:2], tokenize=False, add_generation_prompt=True\n",
+ ")\n",
+ "\n",
+ "image_inputs, _ = process_vision_info(train_dataset[0])\n",
+ "\n",
+ "inputs = processor(\n",
+ " text=[text],\n",
+ " images=image_inputs,\n",
+ " return_tensors=\"pt\",\n",
+ ")\n",
+ "\n",
+ "inputs = inputs.to(\"cuda\")\n",
+ "\n",
+ "generated_ids = model.generate(**inputs, max_new_tokens=1024)\n",
+ "generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]\n",
+ "\n",
+ "output_text = processor.batch_decode(\n",
+ " generated_ids_trimmed,\n",
+ " skip_special_tokens=True,\n",
+ " clean_up_tokenization_spaces=False\n",
+ ")\n",
+ "\n",
+ "output_text[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "o9Id3dzV5Wwy"
+ },
+ "source": [
+ "\ud83d\udca1 As we can see, the model generates the correct answer using the pretrained model along with the additional system message, without any training. This approach may serve as a viable alternative to fine-tuning, depending on the specific use case."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Wgv0-sy8TLPE"
+ },
+ "source": [
+ "## 7. Continuing the Learning Journey \ud83e\uddd1\u200d\ud83c\udf93\ufe0f\n",
+ "\n",
+ "To further enhance your understanding and skills in working with multimodal models, check out the following resources:\n",
+ "\n",
+ "- [Multimodal Retrieval-Augmented Generation (RAG) Recipe](https://huggingface.co/learn/cookbook/multimodal_rag_using_document_retrieval_and_vlms)\n",
+ "- [Phil Schmid's tutorial](https://www.philschmid.de/fine-tune-multimodal-llms-with-trl)\n",
+ "- [Merve Noyan's **smol-vision** repository](https://github.com/merveenoyan/smol-vision/tree/main)\n",
+ "- [Quantize Your Qwen2-VL Model with AutoAWQ](https://github.com/QwenLM/Qwen2-VL?tab=readme-ov-file#quantize-your-own-model-with-autoawq)\n",
+ "- [Preference Optimization for Vision Language Models with TRL](https://huggingface.co/blog/dpo_vlm)\n",
+ "- [Hugging Face Llama Recipes: SFT for VLM](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/sft_vlm.py)\n",
+ "- [Hugging Face Llama Recipes: PEFT Fine-Tuning](https://github.com/huggingface/huggingface-llama-recipes/blob/main/fine_tune/peft_finetuning.py)\n",
+ "- [Hugging Face Blog: IDEFICS2](https://huggingface.co/blog/idefics2)\n",
+ "\n",
+ "These resources will help you deepen your knowledge and skills in multimodal learning.\n",
+ "\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "A100",
+ "machine_shape": "hm",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
From dbcea7ee3cce1c69df72f46483fdd7234c68b93f Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:28:10 +0530
Subject: [PATCH 5/8] Delete notebooks/en/medical_rag_and_reasoning.ipynb
Giving invalid notebook error
---
notebooks/en/medical_rag_and_reasoning.ipynb | 11893 -----------------
1 file changed, 11893 deletions(-)
delete mode 100644 notebooks/en/medical_rag_and_reasoning.ipynb
diff --git a/notebooks/en/medical_rag_and_reasoning.ipynb b/notebooks/en/medical_rag_and_reasoning.ipynb
deleted file mode 100644
index 11588b62..00000000
--- a/notebooks/en/medical_rag_and_reasoning.ipynb
+++ /dev/null
@@ -1,11893 +0,0 @@
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- "# HuatuoGPT-o1 Medical RAG and Reasoning\n",
- "\n",
- "_Authored by: [Alan Ponnachan](https://huggingface.co/AlanPonnachan)_\n",
- "\n",
- "This notebook demonstrates an end-to-end example of using HuatuoGPT-o1 for medical question answering with Retrieval-Augmented Generation (RAG) and reasoning. We'll leverage the HuatuoGPT-o1 model, a medical Large Language Model (LLM) designed for advanced medical reasoning, to provide detailed and well-structured answers to medical queries.\n",
- "\n",
- "## Introduction\n",
- "\n",
- "HuatuoGPT-o1 is a medical LLM that excels at identifying mistakes, exploring alternative strategies, and refining its answers. It utilizes verifiable medical problems and a specialized medical verifier to enhance its reasoning capabilities. This notebook showcases how to use HuatuoGPT-o1 in a RAG setting, where we retrieve relevant information from a medical knowledge base and then use the model to generate a reasoned response."
- ],
- "metadata": {
- "id": "ZVwwGBNRkaIK"
- }
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Notebook Setup\n",
- "\n",
- "\n",
- "**Important:** Before running the code, ensure you are using a GPU runtime for faster performance. Go to **\"Runtime\" -> \"Change runtime type\"** and select **\"GPU\"** under \"Hardware accelerator.\"\n",
- "\n",
- "Let's start by installing the necessary libraries."
- ],
- "metadata": {
- "id": "AVnKxFdgkwRg"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "!pip install transformers datasets sentence-transformers scikit-learn --upgrade -q"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "collapsed": true,
- "id": "xxihylcYksno",
- "outputId": "48a0495f-3204-47e0-815c-55810b4e1142"
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- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m44.4/44.4 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
- "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
- "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
- "\u001b[0m"
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- "## Load the Dataset\n",
- "\n",
- "We'll use the **\"ChatDoctor-HealthCareMagic-100k\"** dataset from the Hugging Face Datasets library. This dataset contains 100,000 real-world patient-doctor interactions, providing a rich knowledge base for our RAG system."
- ],
- "metadata": {
- "id": "tIp5tChLlwQ-"
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- "from datasets import load_dataset\n",
- "\n",
- "dataset = load_dataset(\"lavita/ChatDoctor-HealthCareMagic-100k\")"
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- "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
- "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
- "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
- "You will be able to reuse this secret in all of your notebooks.\n",
- "Please note that authentication is recommended but still optional to access public models or datasets.\n",
- " warnings.warn(\n"
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- "Generating train split: 0%| | 0/112165 [00:00, ? examples/s]"
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- "## Step 3: Initialize the Models\n",
- "\n",
- "We need to initialize two models:\n",
- "\n",
- "1. **HuatuoGPT-o1**: The medical LLM for generating responses.\n",
- "2. **Sentence Transformer**: An embedding model for creating vector representations of text, which we'll use for retrieval."
- ],
- "metadata": {
- "id": "nQkOshW8mD6I"
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- "import torch\n",
- "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
- "from sentence_transformers import SentenceTransformer\n",
- "\n",
- "# Initialize HuatuoGPT-o1\n",
- "model_name = \"FreedomIntelligence/HuatuoGPT-o1-7B\"\n",
- "model = AutoModelForCausalLM.from_pretrained(\n",
- " model_name, torch_dtype=\"auto\", device_map=\"auto\"\n",
- ")\n",
- "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
- "\n",
- "# Initialize Sentence Transformer\n",
- "embed_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
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- "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
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- "version_major": 2,
- "version_minor": 0,
- "model_id": "c95ed4db44af4a56b6c62e63a84f045b"
- }
- },
- "metadata": {}
- },
- {
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- "data": {
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- "version_major": 2,
- "version_minor": 0,
- "model_id": "12dcb66574d84f3398f7aaca54b22ee6"
- }
- },
- "metadata": {}
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Prepare the Knowledge Base\n",
- "\n",
- "We'll create a knowledge base by generating embeddings for the combined question-answer pairs from the dataset."
- ],
- "metadata": {
- "id": "h1JS4S7imWyx"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "import pandas as pd\n",
- "import numpy as np\n",
- "\n",
- "# Convert dataset to DataFrame\n",
- "df = pd.DataFrame(dataset[\"train\"])\n",
- "\n",
- "# Combine question and answer for context\n",
- "df[\"combined\"] = df[\"input\"] + \" \" + df[\"output\"]\n",
- "\n",
- "# Generate embeddings\n",
- "print(\"Generating embeddings for the knowledge base...\")\n",
- "embeddings = embed_model.encode(\n",
- " df[\"combined\"].tolist(), show_progress_bar=True, batch_size=128\n",
- ")\n",
- "print(\"Embeddings generated!\")"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 84,
- "referenced_widgets": [
- "c4cd782a33894b1ea264d2f16278f35d",
- "7146015096f549debb2897cfe37401a5",
- "3bd652817a2a4a91a3588a9a210acf0e",
- "37ba7a33a5024e16ba7d97599b0af894",
- "5ac858b55352476b82671455474be771",
- "a1076492a9ac43f7a884c406f2ee4061",
- "098e1fe90fcb4b82953ad33478456a21",
- "bca3929e8f1b4ce9a1f732bb4c11134c",
- "9cbb77e485c546a88bb77e8672f43e0c",
- "6e04cc7b350148feb8751f27053e08d5",
- "639d8934692d4b0f911cb116dad9cff7"
- ]
- },
- "id": "enY6TakEmZv0",
- "outputId": "6c826f94-d13d-4afb-e572-d8dcd8c9d167"
- },
- "execution_count": 4,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Generating embeddings for the knowledge base...\n"
- ]
- },
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "Batches: 0%| | 0/877 [00:00, ?it/s]"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "c4cd782a33894b1ea264d2f16278f35d"
- }
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Embeddings generated!\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Implement Retrieval\n",
- "\n",
- "This function retrieves the `k` most relevant contexts to a given query using cosine similarity."
- ],
- "metadata": {
- "id": "lCPOlYWHmdXs"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "from sklearn.metrics.pairwise import cosine_similarity\n",
- "\n",
- "def retrieve_relevant_contexts(query: str, k: int = 3) -> list:\n",
- " \"\"\"\n",
- " Retrieves the k most relevant contexts to a given query.\n",
- "\n",
- " Args:\n",
- " query (str): The user's medical query.\n",
- " k (int): The number of relevant contexts to retrieve.\n",
- "\n",
- " Returns:\n",
- " list: A list of dictionaries, each containing a relevant context.\n",
- " \"\"\"\n",
- " # Generate query embedding\n",
- " query_embedding = embed_model.encode([query])[0]\n",
- "\n",
- " # Calculate similarities\n",
- " similarities = cosine_similarity([query_embedding], embeddings)[0]\n",
- "\n",
- " # Get top k similar contexts\n",
- " top_k_indices = np.argsort(similarities)[-k:][::-1]\n",
- "\n",
- " contexts = []\n",
- " for idx in top_k_indices:\n",
- " contexts.append(\n",
- " {\n",
- " \"question\": df.iloc[idx][\"input\"],\n",
- " \"answer\": df.iloc[idx][\"output\"],\n",
- " \"similarity\": similarities[idx],\n",
- " }\n",
- " )\n",
- "\n",
- " return contexts"
- ],
- "metadata": {
- "id": "N8wVjl1QmhyS"
- },
- "execution_count": 5,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Implement Response Generation\n",
- "\n",
- "This function generates a detailed response using the retrieved contexts."
- ],
- "metadata": {
- "id": "sdXnIn94mr2F"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "def generate_structured_response(query: str, contexts: list) -> str:\n",
- " \"\"\"\n",
- " Generates a detailed response using the retrieved contexts.\n",
- "\n",
- " Args:\n",
- " query (str): The user's medical query.\n",
- " contexts (list): A list of relevant contexts.\n",
- "\n",
- " Returns:\n",
- " str: The generated response.\n",
- " \"\"\"\n",
- " # Prepare prompt with retrieved contexts\n",
- " context_prompt = \"\\n\".join(\n",
- " [\n",
- " f\"Reference {i+1}:\"\n",
- " f\"\\nQuestion: {ctx['question']}\"\n",
- " f\"\\nAnswer: {ctx['answer']}\"\n",
- " for i, ctx in enumerate(contexts)\n",
- " ]\n",
- " )\n",
- "\n",
- " prompt = f\"\"\"Based on the following references and your medical knowledge, provide a detailed response:\n",
- "\n",
- "References:\n",
- "{context_prompt}\n",
- "\n",
- "Question: {query}\n",
- "\n",
- "By considering:\n",
- "1. The key medical concepts in the question.\n",
- "2. How the reference cases relate to this question.\n",
- "3. What medical principles should be applied.\n",
- "4. Any potential complications or considerations.\n",
- "\n",
- "Give the final response:\n",
- "\"\"\"\n",
- "\n",
- " # Generate response\n",
- " messages = [{\"role\": \"user\", \"content\": prompt}]\n",
- " inputs = tokenizer(\n",
- " tokenizer.apply_chat_template(\n",
- " messages, tokenize=False, add_generation_prompt=True\n",
- " ),\n",
- " return_tensors=\"pt\",\n",
- " ).to(model.device)\n",
- "\n",
- " outputs = model.generate(\n",
- " **inputs,\n",
- " max_new_tokens=1024,\n",
- " temperature=0.7,\n",
- " num_beams=1,\n",
- " do_sample=True,\n",
- " )\n",
- "\n",
- " response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
- "\n",
- " # Extract the final response portion\n",
- " final_response = response.split(\"Give the final response:\\n\")[-1]\n",
- "\n",
- " return final_response"
- ],
- "metadata": {
- "id": "IPpBjeWmmtrj"
- },
- "execution_count": 6,
- "outputs": []
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Putting It All Together\n",
- "\n",
- "Let's define a function to process a query end-to-end and then use it with an example."
- ],
- "metadata": {
- "id": "e3rCCwcom0bx"
- }
- },
- {
- "cell_type": "code",
- "source": [
- "def process_query(query: str, k: int = 3) -> tuple:\n",
- " \"\"\"\n",
- " Processes a medical query end-to-end.\n",
- "\n",
- " Args:\n",
- " query (str): The user's medical query.\n",
- " k (int): The number of relevant contexts to retrieve.\n",
- "\n",
- " Returns:\n",
- " tuple: The generated response and the retrieved contexts.\n",
- " \"\"\"\n",
- " contexts = retrieve_relevant_contexts(query, k)\n",
- " response = generate_structured_response(query, contexts)\n",
- " return response, contexts\n",
- "\n",
- "# Example query\n",
- "query = \"I've been experiencing persistent headaches and dizziness for the past week. What could be the cause?\"\n",
- "\n",
- "# Process query\n",
- "response, contexts = process_query(query)\n",
- "\n",
- "# Print results\n",
- "print(\"\\nQuery:\", query)\n",
- "print(\"\\nRelevant Contexts:\")\n",
- "for i, ctx in enumerate(contexts, 1):\n",
- " print(f\"\\nReference {i} (Similarity: {ctx['similarity']:.3f}):\")\n",
- " print(f\"Q: {ctx['question']}\")\n",
- " print(f\"A: {ctx['answer']}\")\n",
- "\n",
- "print(\"\\nGenerated Response:\")\n",
- "print(response)"
- ],
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "-WS65VK0m4Nc",
- "outputId": "4e632de4-e468-498b-c635-eadac0411dcb"
- },
- "execution_count": 7,
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "\n",
- "Query: I've been experiencing persistent headaches and dizziness for the past week. What could be the cause?\n",
- "\n",
- "Relevant Contexts:\n",
- "\n",
- "Reference 1 (Similarity: 0.687):\n",
- "Q: Dizziness, sometimes severe, nausea, sometimes severe. Very close to throwing up at times, but not actually doing it. Headache. No pain anywhere, and it comes and goes a couple times in a day. I v had this about a week. I am well hydrated. I v been diagnosed with vertigo years ago, but it went away years ago, and this is nothing like that was. I feel okay between episodes, but tired. I have been laying down and sleeping when it happens, and seem ok when I get back up. It s been hit and miss, meaning not everyday. I haven t changed my diet or products\n",
- "A: Hello! Thank you for asking on Chat Doctor! I carefully read your question and would explain that your symptoms could be related to an inner ear disorder or an inflammatory disorder, causing the headache. Coming to this point, I would recommend consulting with an ENT specialist for a careful physical exam and labyrinthine tests to exclude possible inner ear disorder. Further, tests to be done are\n",
- "\n",
- "Reference 2 (Similarity: 0.673):\n",
- "Q: I have been having dizzy spells , bad headache I collapsed on the train the other day and went to hospital but hey couldnt find anything in my blood or brain scan the headache has been coming and going for about one month but te dizziness only started three days ago\n",
- "A: Hello! Welcome and thank you for asking on Chat Doctor ! Your symptoms could be related to low blood pressure or orthostatic hypotension. An inner ear disorder can not be excluded too, considering the dizzy spells. For this reason, I would recommend first consulting with an ENT specialist for a physical check up and labyrinthine tests. Other tests to consider would be a Head Up Tilt test for orthostatic hypotension, especially if your blood pressure values Chat Doctor. Hope you will find this answer helpful! Best wishes,\n",
- "\n",
- "Reference 3 (Similarity: 0.672):\n",
- "Q: over the past two weeks or so I have had an experience of what I believe is vertigo. The first time I was mowing my lawn on a riding tractor and made a turn in the yard and felt like I was swaying back and forth. It lasted just a few minutes and thankfully I had a good grip on the stearing wheel. The second time was today, I was sitting at my desk at work and all of a sudden it seemed as though my desk was wobbiling back and forth. It wasn t the desk it was me. The first time it happened I do not recall having a headache but today I have had just a slight headache most of the day. Any suggestions?\n",
- "A: Hi, There can be many causes of vertigo. One of the most common causes is diseases associated with ear like labyrinthine (infection of the ear), vestibular neuritis (inflammation of the nerves) or BPPV (benign positional vertigo). It can also be related to diseases of brain (infection or swelling) or heart disorders (arrhythmia-rhythm disturbances) or cervical spondylosis (neck posture related issues). Besides this, there are simpler causes like anemia (low hemoglobin), hypoglycemia (low sugar), prolonged fasting, excessive heat, stress, anxiety or lack of proper sleep. Hence, I feel, first, focus on lifestyle modifications. Have a good balanced diet with lots of fruits and vegetables and less of tea and coffee. Maintain proper posture while working and sleeping, take good sleep for 7-8 hours, do some meditation or go out for a walk. If still the symptoms persist then do go for some investigations like-complete blood count, sugar levels, electrolytes, ECG, X-ray cervical spine and MRI brain. This will help us to make a proper diagnosis. Take care. Hope I have answered your question. Let me know if I can assist you further.\n",
- "\n",
- "Generated Response:\n",
- "\n",
- "assistant\n",
- "## Thinking\n",
- "\n",
- "Alright, let's think about this. So, we're dealing with someone who's been having these bouts of dizziness and headaches for about a week now. That sounds pretty uncomfortable. Dizziness and headaches can come from a bunch of different things, right? Like, maybe it's something to do with the inner ear, or maybe it's a bit more systemic, like a problem with blood pressure or even something neurological.\n",
- "\n",
- "Okay, let's break it down. Inner ear problems, like vertigo, are pretty common culprits here. They can definitely cause dizziness and sometimes headaches, although they usually don't last forever. But since this person says their symptoms are hitting and missing, it might not be exactly the same as their old vertigo.\n",
- "\n",
- "Now, let's consider the possibility of something like orthostatic hypotension. That's where your blood pressure drops when you stand up, and it can make you dizzy. But the thing is, if this were orthostatic hypotension, we'd expect the dizziness to happen every time they stand up, which isn't quite the case here. Plus, the headaches are a bit of a wildcard.\n",
- "\n",
- "Hmm, what else could it be? Maybe anemia or hypoglycemia. Those can cause dizziness and headaches too. But again, without any major changes in diet or lifestyle, it's hard to say if that's really it.\n",
- "\n",
- "Let's see, what else should we think about? Oh, right, the person mentions they've had their blood checked and a brain scan, but nothing showed up. That rules out a lot of serious stuff like infections or brain issues, which is good news. But it also means we have to keep looking at other possibilities.\n",
- "\n",
- "Given all this, it seems like the best course of action is to consult an ENT specialist. They can do some tests specific to inner ear disorders, which might shed some light on what's going on. And if those tests don't reveal anything, maybe we should look into things like lifestyle changes, especially around diet and hydration.\n",
- "\n",
- "So, in summary, it looks like we need to keep an eye on things. The dizziness and headaches could be due to an inner ear issue or something systemic. Consulting a specialist and making some lifestyle adjustments might help figure out what's causing these symptoms.\n",
- "\n",
- "## Final Response\n",
- "\n",
- "The symptoms of dizziness, headaches, and occasional nausea you are experiencing could be related to several underlying conditions. Based on the information provided, it appears that an inner ear disorder, such as benign paroxysmal positional vertigo (BPPV) or vestibular neuritis, is a plausible explanation. These conditions can cause episodes of dizziness and sometimes headaches, although they typically resolve on their own or improve with treatment.\n",
- "\n",
- "Another consideration is orthostatic hypotension, which involves a drop in blood pressure upon standing, potentially causing dizziness. However, given that your symptoms do not consistently occur with changes in position, this is less likely.\n",
- "\n",
- "Systemic factors, such as anemia or hypoglycemia, could also contribute to dizziness and headaches. Since these conditions can be influenced by dietary and lifestyle factors, maintaining a balanced diet, staying hydrated, and ensuring adequate rest may help alleviate symptoms.\n",
- "\n",
- "To better understand the nature of your symptoms, it would be advisable to consult with an ENT specialist for a thorough examination and possibly labyrinthine tests to assess any inner ear issues. Additionally, considering a Head-Up Tilt test for orthostatic hypotension and evaluating other systemic factors through appropriate blood tests and scans could provide further insights. \n",
- "\n",
- "In summary, while the exact cause remains unclear, exploring options like an ENT consultation and adjusting lifestyle factors may aid in managing your symptoms.\n"
- ]
- }
- ]
- },
- {
- "cell_type": "markdown",
- "source": [
- "## Conclusion\n",
- "\n",
- "This notebook demonstrates a practical application of HuatuoGPT-o1 for medical question answering using RAG and reasoning. By combining retrieval from a relevant knowledge base with the advanced reasoning capabilities of HuatuoGPT-o1, we can build a system that provides detailed and well-structured answers to complex medical queries.\n",
- "\n",
- "You can further enhance this system by:\n",
- "\n",
- "* Experimenting with different values of `k` (number of retrieved contexts).\n",
- "* Fine-tuning HuatuoGPT-o1 on a specific medical domain.\n",
- "* Evaluating the system's performance using medical benchmarks.\n",
- "* Adding a user interface for easier interaction.\n",
- "* Improving upon existing code by handling edge cases.\n",
- "\n",
- "Feel free to adapt and expand upon this example to create even more powerful and helpful medical AI applications!"
- ],
- "metadata": {
- "id": "SFYsU7G4m9Ii"
- }
- }
- ]
-}
From 9a1fbc28b1bcd912ccf4fae40e99b24abdef79f4 Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:29:04 +0530
Subject: [PATCH 6/8] Fixed Invalid Notebook error
---
notebooks/en/medical_rag_and_reasoning.ipynb | 1287 ++++++++++++++++++
1 file changed, 1287 insertions(+)
create mode 100644 notebooks/en/medical_rag_and_reasoning.ipynb
diff --git a/notebooks/en/medical_rag_and_reasoning.ipynb b/notebooks/en/medical_rag_and_reasoning.ipynb
new file mode 100644
index 00000000..682aa8fc
--- /dev/null
+++ b/notebooks/en/medical_rag_and_reasoning.ipynb
@@ -0,0 +1,1287 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "gpuType": "T4"
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# HuatuoGPT-o1 Medical RAG and Reasoning\n",
+ "\n",
+ "_Authored by: [Alan Ponnachan](https://huggingface.co/AlanPonnachan)_\n",
+ "\n",
+ "This notebook demonstrates an end-to-end example of using HuatuoGPT-o1 for medical question answering with Retrieval-Augmented Generation (RAG) and reasoning. We'll leverage the HuatuoGPT-o1 model, a medical Large Language Model (LLM) designed for advanced medical reasoning, to provide detailed and well-structured answers to medical queries.\n",
+ "\n",
+ "## Introduction\n",
+ "\n",
+ "HuatuoGPT-o1 is a medical LLM that excels at identifying mistakes, exploring alternative strategies, and refining its answers. It utilizes verifiable medical problems and a specialized medical verifier to enhance its reasoning capabilities. This notebook showcases how to use HuatuoGPT-o1 in a RAG setting, where we retrieve relevant information from a medical knowledge base and then use the model to generate a reasoned response."
+ ],
+ "metadata": {
+ "id": "ZVwwGBNRkaIK"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Notebook Setup\n",
+ "\n",
+ "\n",
+ "**Important:** Before running the code, ensure you are using a GPU runtime for faster performance. Go to **\"Runtime\" -> \"Change runtime type\"** and select **\"GPU\"** under \"Hardware accelerator.\"\n",
+ "\n",
+ "Let's start by installing the necessary libraries."
+ ],
+ "metadata": {
+ "id": "AVnKxFdgkwRg"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "!pip install transformers datasets sentence-transformers scikit-learn --upgrade -q"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "collapsed": true,
+ "id": "xxihylcYksno",
+ "outputId": "48a0495f-3204-47e0-815c-55810b4e1142"
+ },
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
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+ "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m9.7/9.7 MB\u001b[0m \u001b[31m102.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m13.5/13.5 MB\u001b[0m \u001b[31m96.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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+ "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m179.3/179.3 kB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m143.5/143.5 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u001b[0m \u001b[32m194.8/194.8 kB\u001b[0m \u001b[31m17.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+ "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
+ "\u001b[0m"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Load the Dataset\n",
+ "\n",
+ "We'll use the **\"ChatDoctor-HealthCareMagic-100k\"** dataset from the Hugging Face Datasets library. This dataset contains 100,000 real-world patient-doctor interactions, providing a rich knowledge base for our RAG system."
+ ],
+ "metadata": {
+ "id": "tIp5tChLlwQ-"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from datasets import load_dataset\n",
+ "\n",
+ "dataset = load_dataset(\"lavita/ChatDoctor-HealthCareMagic-100k\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 237,
+ "referenced_widgets": [
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+ ]
+ },
+ "id": "04FZeEB0lsRe",
+ "outputId": "76b8a639-5c68-4a12-914e-f36610ffbed5"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "README.md: 0%| | 0.00/542 [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "(\u2026)-00000-of-00001-5e7cb295b9cff0bf.parquet: 0%| | 0.00/70.5M [00:00, ?B/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Generating train split: 0%| | 0/112165 [00:00, ? examples/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "55b0cba3a2b84fe38e9c6a44dc5fcf56"
+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Step 3: Initialize the Models\n",
+ "\n",
+ "We need to initialize two models:\n",
+ "\n",
+ "1. **HuatuoGPT-o1**: The medical LLM for generating responses.\n",
+ "2. **Sentence Transformer**: An embedding model for creating vector representations of text, which we'll use for retrieval."
+ ],
+ "metadata": {
+ "id": "nQkOshW8mD6I"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import torch\n",
+ "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+ "from sentence_transformers import SentenceTransformer\n",
+ "\n",
+ "# Initialize HuatuoGPT-o1\n",
+ "model_name = \"FreedomIntelligence/HuatuoGPT-o1-7B\"\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " model_name, torch_dtype=\"auto\", device_map=\"auto\"\n",
+ ")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "\n",
+ "# Initialize Sentence Transformer\n",
+ "embed_model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 936,
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+ },
+ "execution_count": 3,
+ "outputs": [
+ {
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+ "name": "stderr",
+ "text": [
+ "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
+ ]
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+ ]
+ },
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+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
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+ ],
+ "application/vnd.jupyter.widget-view+json": {
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+ }
+ },
+ "metadata": {}
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Prepare the Knowledge Base\n",
+ "\n",
+ "We'll create a knowledge base by generating embeddings for the combined question-answer pairs from the dataset."
+ ],
+ "metadata": {
+ "id": "h1JS4S7imWyx"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Convert dataset to DataFrame\n",
+ "df = pd.DataFrame(dataset[\"train\"])\n",
+ "\n",
+ "# Combine question and answer for context\n",
+ "df[\"combined\"] = df[\"input\"] + \" \" + df[\"output\"]\n",
+ "\n",
+ "# Generate embeddings\n",
+ "print(\"Generating embeddings for the knowledge base...\")\n",
+ "embeddings = embed_model.encode(\n",
+ " df[\"combined\"].tolist(), show_progress_bar=True, batch_size=128\n",
+ ")\n",
+ "print(\"Embeddings generated!\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 84,
+ "referenced_widgets": [
+ "c4cd782a33894b1ea264d2f16278f35d",
+ "7146015096f549debb2897cfe37401a5",
+ "3bd652817a2a4a91a3588a9a210acf0e",
+ "37ba7a33a5024e16ba7d97599b0af894",
+ "5ac858b55352476b82671455474be771",
+ "a1076492a9ac43f7a884c406f2ee4061",
+ "098e1fe90fcb4b82953ad33478456a21",
+ "bca3929e8f1b4ce9a1f732bb4c11134c",
+ "9cbb77e485c546a88bb77e8672f43e0c",
+ "6e04cc7b350148feb8751f27053e08d5",
+ "639d8934692d4b0f911cb116dad9cff7"
+ ]
+ },
+ "id": "enY6TakEmZv0",
+ "outputId": "6c826f94-d13d-4afb-e572-d8dcd8c9d167"
+ },
+ "execution_count": 4,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Generating embeddings for the knowledge base...\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/plain": [
+ "Batches: 0%| | 0/877 [00:00, ?it/s]"
+ ],
+ "application/vnd.jupyter.widget-view+json": {
+ "version_major": 2,
+ "version_minor": 0,
+ "model_id": "c4cd782a33894b1ea264d2f16278f35d"
+ }
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Embeddings generated!\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Implement Retrieval\n",
+ "\n",
+ "This function retrieves the `k` most relevant contexts to a given query using cosine similarity."
+ ],
+ "metadata": {
+ "id": "lCPOlYWHmdXs"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "from sklearn.metrics.pairwise import cosine_similarity\n",
+ "\n",
+ "def retrieve_relevant_contexts(query: str, k: int = 3) -> list:\n",
+ " \"\"\"\n",
+ " Retrieves the k most relevant contexts to a given query.\n",
+ "\n",
+ " Args:\n",
+ " query (str): The user's medical query.\n",
+ " k (int): The number of relevant contexts to retrieve.\n",
+ "\n",
+ " Returns:\n",
+ " list: A list of dictionaries, each containing a relevant context.\n",
+ " \"\"\"\n",
+ " # Generate query embedding\n",
+ " query_embedding = embed_model.encode([query])[0]\n",
+ "\n",
+ " # Calculate similarities\n",
+ " similarities = cosine_similarity([query_embedding], embeddings)[0]\n",
+ "\n",
+ " # Get top k similar contexts\n",
+ " top_k_indices = np.argsort(similarities)[-k:][::-1]\n",
+ "\n",
+ " contexts = []\n",
+ " for idx in top_k_indices:\n",
+ " contexts.append(\n",
+ " {\n",
+ " \"question\": df.iloc[idx][\"input\"],\n",
+ " \"answer\": df.iloc[idx][\"output\"],\n",
+ " \"similarity\": similarities[idx],\n",
+ " }\n",
+ " )\n",
+ "\n",
+ " return contexts"
+ ],
+ "metadata": {
+ "id": "N8wVjl1QmhyS"
+ },
+ "execution_count": 5,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Implement Response Generation\n",
+ "\n",
+ "This function generates a detailed response using the retrieved contexts."
+ ],
+ "metadata": {
+ "id": "sdXnIn94mr2F"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def generate_structured_response(query: str, contexts: list) -> str:\n",
+ " \"\"\"\n",
+ " Generates a detailed response using the retrieved contexts.\n",
+ "\n",
+ " Args:\n",
+ " query (str): The user's medical query.\n",
+ " contexts (list): A list of relevant contexts.\n",
+ "\n",
+ " Returns:\n",
+ " str: The generated response.\n",
+ " \"\"\"\n",
+ " # Prepare prompt with retrieved contexts\n",
+ " context_prompt = \"\\n\".join(\n",
+ " [\n",
+ " f\"Reference {i+1}:\"\n",
+ " f\"\\nQuestion: {ctx['question']}\"\n",
+ " f\"\\nAnswer: {ctx['answer']}\"\n",
+ " for i, ctx in enumerate(contexts)\n",
+ " ]\n",
+ " )\n",
+ "\n",
+ " prompt = f\"\"\"Based on the following references and your medical knowledge, provide a detailed response:\n",
+ "\n",
+ "References:\n",
+ "{context_prompt}\n",
+ "\n",
+ "Question: {query}\n",
+ "\n",
+ "By considering:\n",
+ "1. The key medical concepts in the question.\n",
+ "2. How the reference cases relate to this question.\n",
+ "3. What medical principles should be applied.\n",
+ "4. Any potential complications or considerations.\n",
+ "\n",
+ "Give the final response:\n",
+ "\"\"\"\n",
+ "\n",
+ " # Generate response\n",
+ " messages = [{\"role\": \"user\", \"content\": prompt}]\n",
+ " inputs = tokenizer(\n",
+ " tokenizer.apply_chat_template(\n",
+ " messages, tokenize=False, add_generation_prompt=True\n",
+ " ),\n",
+ " return_tensors=\"pt\",\n",
+ " ).to(model.device)\n",
+ "\n",
+ " outputs = model.generate(\n",
+ " **inputs,\n",
+ " max_new_tokens=1024,\n",
+ " temperature=0.7,\n",
+ " num_beams=1,\n",
+ " do_sample=True,\n",
+ " )\n",
+ "\n",
+ " response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+ "\n",
+ " # Extract the final response portion\n",
+ " final_response = response.split(\"Give the final response:\\n\")[-1]\n",
+ "\n",
+ " return final_response"
+ ],
+ "metadata": {
+ "id": "IPpBjeWmmtrj"
+ },
+ "execution_count": 6,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Putting It All Together\n",
+ "\n",
+ "Let's define a function to process a query end-to-end and then use it with an example."
+ ],
+ "metadata": {
+ "id": "e3rCCwcom0bx"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def process_query(query: str, k: int = 3) -> tuple:\n",
+ " \"\"\"\n",
+ " Processes a medical query end-to-end.\n",
+ "\n",
+ " Args:\n",
+ " query (str): The user's medical query.\n",
+ " k (int): The number of relevant contexts to retrieve.\n",
+ "\n",
+ " Returns:\n",
+ " tuple: The generated response and the retrieved contexts.\n",
+ " \"\"\"\n",
+ " contexts = retrieve_relevant_contexts(query, k)\n",
+ " response = generate_structured_response(query, contexts)\n",
+ " return response, contexts\n",
+ "\n",
+ "# Example query\n",
+ "query = \"I've been experiencing persistent headaches and dizziness for the past week. What could be the cause?\"\n",
+ "\n",
+ "# Process query\n",
+ "response, contexts = process_query(query)\n",
+ "\n",
+ "# Print results\n",
+ "print(\"\\nQuery:\", query)\n",
+ "print(\"\\nRelevant Contexts:\")\n",
+ "for i, ctx in enumerate(contexts, 1):\n",
+ " print(f\"\\nReference {i} (Similarity: {ctx['similarity']:.3f}):\")\n",
+ " print(f\"Q: {ctx['question']}\")\n",
+ " print(f\"A: {ctx['answer']}\")\n",
+ "\n",
+ "print(\"\\nGenerated Response:\")\n",
+ "print(response)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-WS65VK0m4Nc",
+ "outputId": "4e632de4-e468-498b-c635-eadac0411dcb"
+ },
+ "execution_count": 7,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\n",
+ "Query: I've been experiencing persistent headaches and dizziness for the past week. What could be the cause?\n",
+ "\n",
+ "Relevant Contexts:\n",
+ "\n",
+ "Reference 1 (Similarity: 0.687):\n",
+ "Q: Dizziness, sometimes severe, nausea, sometimes severe. Very close to throwing up at times, but not actually doing it. Headache. No pain anywhere, and it comes and goes a couple times in a day. I v had this about a week. I am well hydrated. I v been diagnosed with vertigo years ago, but it went away years ago, and this is nothing like that was. I feel okay between episodes, but tired. I have been laying down and sleeping when it happens, and seem ok when I get back up. It s been hit and miss, meaning not everyday. I haven t changed my diet or products\n",
+ "A: Hello! Thank you for asking on Chat Doctor! I carefully read your question and would explain that your symptoms could be related to an inner ear disorder or an inflammatory disorder, causing the headache. Coming to this point, I would recommend consulting with an ENT specialist for a careful physical exam and labyrinthine tests to exclude possible inner ear disorder. Further, tests to be done are\n",
+ "\n",
+ "Reference 2 (Similarity: 0.673):\n",
+ "Q: I have been having dizzy spells , bad headache I collapsed on the train the other day and went to hospital but hey couldnt find anything in my blood or brain scan the headache has been coming and going for about one month but te dizziness only started three days ago\n",
+ "A: Hello! Welcome and thank you for asking on Chat Doctor ! Your symptoms could be related to low blood pressure or orthostatic hypotension. An inner ear disorder can not be excluded too, considering the dizzy spells. For this reason, I would recommend first consulting with an ENT specialist for a physical check up and labyrinthine tests. Other tests to consider would be a Head Up Tilt test for orthostatic hypotension, especially if your blood pressure values Chat Doctor. Hope you will find this answer helpful! Best wishes,\n",
+ "\n",
+ "Reference 3 (Similarity: 0.672):\n",
+ "Q: over the past two weeks or so I have had an experience of what I believe is vertigo. The first time I was mowing my lawn on a riding tractor and made a turn in the yard and felt like I was swaying back and forth. It lasted just a few minutes and thankfully I had a good grip on the stearing wheel. The second time was today, I was sitting at my desk at work and all of a sudden it seemed as though my desk was wobbiling back and forth. It wasn t the desk it was me. The first time it happened I do not recall having a headache but today I have had just a slight headache most of the day. Any suggestions?\n",
+ "A: Hi, There can be many causes of vertigo. One of the most common causes is diseases associated with ear like labyrinthine (infection of the ear), vestibular neuritis (inflammation of the nerves) or BPPV (benign positional vertigo). It can also be related to diseases of brain (infection or swelling) or heart disorders (arrhythmia-rhythm disturbances) or cervical spondylosis (neck posture related issues). Besides this, there are simpler causes like anemia (low hemoglobin), hypoglycemia (low sugar), prolonged fasting, excessive heat, stress, anxiety or lack of proper sleep. Hence, I feel, first, focus on lifestyle modifications. Have a good balanced diet with lots of fruits and vegetables and less of tea and coffee. Maintain proper posture while working and sleeping, take good sleep for 7-8 hours, do some meditation or go out for a walk. If still the symptoms persist then do go for some investigations like-complete blood count, sugar levels, electrolytes, ECG, X-ray cervical spine and MRI brain. This will help us to make a proper diagnosis. Take care. Hope I have answered your question. Let me know if I can assist you further.\n",
+ "\n",
+ "Generated Response:\n",
+ "\n",
+ "assistant\n",
+ "## Thinking\n",
+ "\n",
+ "Alright, let's think about this. So, we're dealing with someone who's been having these bouts of dizziness and headaches for about a week now. That sounds pretty uncomfortable. Dizziness and headaches can come from a bunch of different things, right? Like, maybe it's something to do with the inner ear, or maybe it's a bit more systemic, like a problem with blood pressure or even something neurological.\n",
+ "\n",
+ "Okay, let's break it down. Inner ear problems, like vertigo, are pretty common culprits here. They can definitely cause dizziness and sometimes headaches, although they usually don't last forever. But since this person says their symptoms are hitting and missing, it might not be exactly the same as their old vertigo.\n",
+ "\n",
+ "Now, let's consider the possibility of something like orthostatic hypotension. That's where your blood pressure drops when you stand up, and it can make you dizzy. But the thing is, if this were orthostatic hypotension, we'd expect the dizziness to happen every time they stand up, which isn't quite the case here. Plus, the headaches are a bit of a wildcard.\n",
+ "\n",
+ "Hmm, what else could it be? Maybe anemia or hypoglycemia. Those can cause dizziness and headaches too. But again, without any major changes in diet or lifestyle, it's hard to say if that's really it.\n",
+ "\n",
+ "Let's see, what else should we think about? Oh, right, the person mentions they've had their blood checked and a brain scan, but nothing showed up. That rules out a lot of serious stuff like infections or brain issues, which is good news. But it also means we have to keep looking at other possibilities.\n",
+ "\n",
+ "Given all this, it seems like the best course of action is to consult an ENT specialist. They can do some tests specific to inner ear disorders, which might shed some light on what's going on. And if those tests don't reveal anything, maybe we should look into things like lifestyle changes, especially around diet and hydration.\n",
+ "\n",
+ "So, in summary, it looks like we need to keep an eye on things. The dizziness and headaches could be due to an inner ear issue or something systemic. Consulting a specialist and making some lifestyle adjustments might help figure out what's causing these symptoms.\n",
+ "\n",
+ "## Final Response\n",
+ "\n",
+ "The symptoms of dizziness, headaches, and occasional nausea you are experiencing could be related to several underlying conditions. Based on the information provided, it appears that an inner ear disorder, such as benign paroxysmal positional vertigo (BPPV) or vestibular neuritis, is a plausible explanation. These conditions can cause episodes of dizziness and sometimes headaches, although they typically resolve on their own or improve with treatment.\n",
+ "\n",
+ "Another consideration is orthostatic hypotension, which involves a drop in blood pressure upon standing, potentially causing dizziness. However, given that your symptoms do not consistently occur with changes in position, this is less likely.\n",
+ "\n",
+ "Systemic factors, such as anemia or hypoglycemia, could also contribute to dizziness and headaches. Since these conditions can be influenced by dietary and lifestyle factors, maintaining a balanced diet, staying hydrated, and ensuring adequate rest may help alleviate symptoms.\n",
+ "\n",
+ "To better understand the nature of your symptoms, it would be advisable to consult with an ENT specialist for a thorough examination and possibly labyrinthine tests to assess any inner ear issues. Additionally, considering a Head-Up Tilt test for orthostatic hypotension and evaluating other systemic factors through appropriate blood tests and scans could provide further insights. \n",
+ "\n",
+ "In summary, while the exact cause remains unclear, exploring options like an ENT consultation and adjusting lifestyle factors may aid in managing your symptoms.\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## Conclusion\n",
+ "\n",
+ "This notebook demonstrates a practical application of HuatuoGPT-o1 for medical question answering using RAG and reasoning. By combining retrieval from a relevant knowledge base with the advanced reasoning capabilities of HuatuoGPT-o1, we can build a system that provides detailed and well-structured answers to complex medical queries.\n",
+ "\n",
+ "You can further enhance this system by:\n",
+ "\n",
+ "* Experimenting with different values of `k` (number of retrieved contexts).\n",
+ "* Fine-tuning HuatuoGPT-o1 on a specific medical domain.\n",
+ "* Evaluating the system's performance using medical benchmarks.\n",
+ "* Adding a user interface for easier interaction.\n",
+ "* Improving upon existing code by handling edge cases.\n",
+ "\n",
+ "Feel free to adapt and expand upon this example to create even more powerful and helpful medical AI applications!"
+ ],
+ "metadata": {
+ "id": "SFYsU7G4m9Ii"
+ }
+ }
+ ]
+}
\ No newline at end of file
From cda9fe7a229184071e12b0d75e790bf657b25d46 Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:37:53 +0530
Subject: [PATCH 7/8] Delete notebooks/en/search_and_learn.ipynb
Invalid Notebook
There was an error rendering your Notebook: the 'state' key is missing from 'metadata.widgets'. Add 'state' to each, or remove 'metadata.widgets'.
Using nbformat v5.10.4 and nbconvert v7.16.6
---
notebooks/en/search_and_learn.ipynb | 8207 ---------------------------
1 file changed, 8207 deletions(-)
delete mode 100644 notebooks/en/search_and_learn.ipynb
diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb
deleted file mode 100644
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--- a/notebooks/en/search_and_learn.ipynb
+++ /dev/null
@@ -1,8207 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xctusmJ6BZ6_"
- },
- "source": [
- "# Scaling Test-Time Compute for Longer Thinking in LLMs\n",
- "\n",
- "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "JmgoppItAO7B"
- },
- "source": [
- "🚨 **WARNING**: This notebook is **resource-intensive** and requires substantial computational power. If you’re running this in **Colab**, it will utilize an **A100 GPU**.\n",
- "\n",
- "---\n",
- "\n",
- "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute** to solve more challenging problems, such as **complex math problems**. This approach, inspired by [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), demonstrates that **longer reasoning time** during inference can enhance model performance.\n",
- "\n",
- "This technique builds on experiments shared in [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which show that smaller models, like the **1B** and **3B Llama Instruct models**, can outperform much larger ones on the **MATH-500 benchmark** when given enough **\"time to think\"**. Recent research from [DeepMind](https://arxiv.org/abs/2408.03314) suggests that **test-time compute** can be scaled optimally through strategies like iterative self-refinement or using a reward model.\n",
- "\n",
- "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) for running these experiments. In this recipe, we'll focus on building a **small chatbot** that engages in **longer reasoning** to tackle **harder problems** using small open models.\n",
- "\n",
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "twKCzVIg71Xa"
- },
- "source": [
- "## 1. Install Dependencies\n",
- "\n",
- "Let’s start by installing the [search-and-learn](https://github.com/huggingface/search-and-learn) repository! 🚀 \n",
- "This repo is designed to replicate the experimental results and is not a Python pip package. However, we can still use it to generate our system. To do so, we’ll need to install it from source with the following steps:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "t0YDC2_7XTm8",
- "outputId": "d804071f-8a3e-4463-9721-d6100ae1d48b"
- },
- "outputs": [],
- "source": [
- "!git clone https://github.com/huggingface/search-and-learn"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000
- },
- "id": "kT3jH_d_XcEb",
- "outputId": "b50f463e-2c9d-4554-cd20-00887acc2114"
- },
- "outputs": [],
- "source": [
- "%cd search-and-learn\n",
- "!pip install -e '.[dev]'"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VAQHu9T176zh"
- },
- "source": [
- "Log in to Hugging Face to access [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), as it is a gated model! 🗝️ \n",
- "If you haven't previously requested access, you'll need to submit a request before proceeding.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 17,
- "referenced_widgets": [
- "2eb64217adde437ea678f68ed612cdc5",
- "931ce892773c44c6a1e2610d3c620617",
- "2cbb5166d93a45eda909a8b7fd6df23a",
- "0b25f7b04ee34fd0a2b1b6920d958d2d",
- "52d9e4bbb17948ee80f9ad60e21ce32f",
- "7e130cc78a7e435d9bc06b360ee9c895",
- "80afab3a0c0b41c78ede99a63bd3ae89",
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- "85d1f3abd57444608539f45a7532dae3",
- "9fd575fd748f488399f886d1f1d840a3",
- "2d92e9d40c174518bde5a8f27c3ff270",
- "e365dcfc89b2464cb5edd6d66eef6ac5",
- "af7f28d3b88448d2ababbd739be1c431",
- "2950fa6d48fc4220a8350ae264a09452",
- "fd0276e87a70434ab3c0f81f54c9fdcd",
- "e25c2e7311bd455389dd4387bbab4eb1",
- "8b382820d7ee4387ad39070fe09eb214",
- "a771cd3bc92e43db917238c1c23c1a58",
- "cd2a46d086a64dc4a7e53facc3ba4c84",
- "316860b81fec4959be9f7ff9c077c4a4"
- ]
- },
- "id": "pnEaTlFYZF_H",
- "outputId": "aa361d8b-23b9-4c21-aa4c-2c0771ea40b7"
- },
- "outputs": [],
- "source": [
- "from huggingface_hub import notebook_login\n",
- "\n",
- "notebook_login()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "wX07zCTA8MWL"
- },
- "source": [
- "## 2. Setup the Large Language Model (LLM) and the Process Reward Model (PRM) 💬\n",
- "\n",
- "As illustrated in the diagram, the system consists of an LLM that generates intermediate answers based on user input, a [PRM model](https://huggingface.co/papers/2211.14275) that evaluates and scores these answers, and a search strategy that uses the PRM feedback to guide the subsequent steps in the search process until reaching the final answer.\n",
- "\n",
- "Let’s begin by initializing each model. For the LLM, we’ll use the [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) model, and for the PRM, we’ll use the [RLHFlow/Llama3.1-8B-PRM-Deepseek-Data](https://huggingface.co/RLHFlow/Llama3.1-8B-PRM-Deepseek-Data) model.\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "IkJw0x7gDJEY"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 1000,
- "referenced_widgets": [
- "fe0917922ba74b39b30a65f7124dc928",
- "7aa678d10ec94310bae0d91230f29c59",
- "0cd30408a9064af3b4714480ca7e07af",
- "a5166e10eb854c928e7a3c574b064117",
- "e5f328d18abc41c7ac45d2d155f0b7a6",
- "f54b35d34059478398858a0fee55a27a",
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- ]
- },
- "id": "MG1MolfxmZ7M",
- "outputId": "b8271db3-a092-4ef9-aacb-730ef489a89e"
- },
- "outputs": [],
- "source": [
- "import torch\n",
- "from vllm import LLM\n",
- "from sal.models.reward_models import RLHFFlow\n",
- "\n",
- "model_path=\"meta-llama/Llama-3.2-1B-Instruct\"\n",
- "prm_path=\"RLHFlow/Llama3.1-8B-PRM-Deepseek-Data\"\n",
- "\n",
- "llm = LLM(\n",
- " model=model_path,\n",
- " gpu_memory_utilization=0.5, # Utilize 50% of GPU memory\n",
- " enable_prefix_caching=True, # Optimize repeated prefix computations\n",
- " seed=42, # Set seed for reproducibility\n",
- ")\n",
- "\n",
- "prm = RLHFFlow(prm_path)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xYtPn0_V_YRx"
- },
- "source": [
- "### 2.1 Instantiate the Question, Search Strategy, and Call the Pipeline\n",
- "\n",
- "Now that we've set up the LLM and PRM, let's proceed by defining the question, selecting a search strategy to retrieve relevant information, and calling the pipeline to process the question through the models.\n",
- "\n",
- "1. **Instantiate the Question**: In this step, we define the input question that the system will answer, considering the given context.\n",
- "\n",
- "2. **Search Strategy**: The system currently supports the following search strategies: `best_of_n`, `beam_search`, and `dvts` (see diagram). For this example, we'll use `best_of_n`, but you can easily switch to any of the other strategies based on your needs. We need to define some configuration parameters for the configuration of the search strategy. You can check the full list [here](https://github.com/huggingface/search-and-learn/blob/main/src/sal/config.py).\n",
- "\n",
- "3. **Call the Pipeline**: With the question and search strategy in place, we’ll call the inference pipeline, processing the inputs through both the LLM and PRM to generate the final answer."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "xSWINPerJrhm"
- },
- "source": [
- ""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "z69xD6i2L5a6"
- },
- "source": [
- "The first step is to clearly define the question that the system will answer. This ensures that we have a precise task for the model to tackle."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {
- "id": "83puLxhzsOM0"
- },
- "outputs": [],
- "source": [
- "question_text = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n",
- "input_batch = {\"problem\": [question_text]}"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "yGpyzMNkAO7H"
- },
- "source": [
- "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers. In this case, we'll use `best_of_n`.\n",
- "\n",
- "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates are evaluated based on their relevance and quality and the final answer is returned.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {
- "id": "C6s6GS16QZLV"
- },
- "outputs": [],
- "source": [
- "from sal.config import Config\n",
- "from sal.search import beam_search, best_of_n, dvts\n",
- "\n",
- "config = Config()\n",
- "config.n=32 # Number of answers to generate during the search\n",
- "\n",
- "search_result = best_of_n(x=input_batch, config=config, llm=llm, prm=prm)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "lsLHD_6C_15p"
- },
- "source": [
- "### 2.2 Display the Final Result\n",
- "\n",
- "Once the pipeline has processed the question through the LLM and PRM, we can display the final result. This result will be the model's output after considering the intermediate answers and scoring them using the PRM.\n",
- "\n",
- "Here's how to display the final answer:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 40,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 105
- },
- "id": "v8medbURbgdI",
- "outputId": "3620f3e6-a25d-4bec-f41c-c4f03a6ed770"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\\\theta)$, we use the following formulas:\\n- $r = \\\\sqrt{x^2 + y^2}$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$\\n\\n## Step 2: Substitute the given values into the formulas\\nGiven $(x, y) = (0, 3)$, we substitute these values into the formulas:\\n- $r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\\\theta = \\\\frac{\\\\pi}{2}$.\\n\\n## Step 3: Combine the results for the polar coordinates\\nTherefore, the polar coordinates of the point $(0, 3)$ are $\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'"
- ]
- },
- "execution_count": 40,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "search_result['pred'][0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "F-8hIu05AO7J"
- },
- "source": [
- "The model’s output might include special tokens, such as `<|start_header_id|>` or `<|end_header_id|>`. To make the answer more readable, we can safely remove them before displaying it to the end user."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 41,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 105
- },
- "id": "flbIu6-rDapM",
- "outputId": "fcb197d5-0f21-4953-8a21-869c92a1f957"
- },
- "outputs": [
- {
- "data": {
- "application/vnd.google.colaboratory.intrinsic+json": {
- "type": "string"
- },
- "text/plain": [
- "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\\\theta)$, we use the following formulas:\\n- $r = \\\\sqrt{x^2 + y^2}$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$\\n\\n## Step 2: Substitute the given values into the formulas\\nGiven $(x, y) = (0, 3)$, we substitute these values into the formulas:\\n- $r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\\\theta = \\\\frac{\\\\pi}{2}$.\\n\\n## Step 3: Combine the results for the polar coordinates\\nTherefore, the polar coordinates of the point $(0, 3)$ are $\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "formatted_output = search_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n",
- "formatted_output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7ZuLZNirAO7J"
- },
- "source": [
- "After removing any special tokens, we can display the final answer to the user. Since the answer is based on markdown, it can be rendered properly by displaying it as markdown."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 42,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 385
- },
- "id": "P4En0qJRD0cl",
- "outputId": "56400fea-e304-4f16-d255-909f42f636e0"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "## Step 1: Recall the conversion formulas\n",
- "To convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\theta)$, we use the following formulas:\n",
- "- $r = \\sqrt{x^2 + y^2}$\n",
- "- $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$\n",
- "\n",
- "## Step 2: Substitute the given values into the formulas\n",
- "Given $(x, y) = (0, 3)$, we substitute these values into the formulas:\n",
- "- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n",
- "- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\theta = \\frac{\\pi}{2}$.\n",
- "\n",
- "## Step 3: Combine the results for the polar coordinates\n",
- "Therefore, the polar coordinates of the point $(0, 3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n",
- "\n",
- "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "from IPython.display import display, Markdown\n",
- "\n",
- "display(Markdown(formatted_output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "4uCpYzAw_4o9"
- },
- "source": [
- "## 3. Assembling It All! 🧑🏭️\n",
- "\n",
- "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n",
- "\n",
- "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n",
- "\n",
- "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n",
- "\n",
- "Here’s how we can structure the method:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 43,
- "metadata": {
- "id": "YpswbcVi37KR"
- },
- "outputs": [],
- "source": [
- "import time\n",
- "\n",
- "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n",
- " \"\"\"\n",
- " Generate an answer for a given question using the search-and-learn pipeline.\n",
- "\n",
- " Args:\n",
- " - question (str): The input question to generate an answer for.\n",
- " - config (Config): Configuration object containing parameters for search strategy.\n",
- " - llm (LLM): Pretrained large language model used for generating answers.\n",
- " - prm (RLHFFlow): Process reward model used for evaluating answers.\n",
- " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n",
- "\n",
- " Returns:\n",
- " - str: The formatted output after processing the question.\n",
- " \"\"\"\n",
- " batch = {\"problem\": [question]}\n",
- "\n",
- " start_time = time.time()\n",
- " if method == 'best_of_n':\n",
- " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n",
- " elif method == 'beam_search':\n",
- " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n",
- " elif method == 'dvts':\n",
- " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n",
- "\n",
- " elapsed_time = time.time() - start_time\n",
- " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n",
- "\n",
- " tokenizer = llm.get_tokenizer()\n",
- " total_tokens = 0\n",
- " for completion in result['completions']:\n",
- " for comp in completion:\n",
- " output_tokens = tokenizer.encode(comp)\n",
- " total_tokens += len(output_tokens)\n",
- "\n",
- " print(f\"Total tokens in all completions: {total_tokens}\")\n",
- "\n",
- " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n",
- " return formatted_output"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "RWbOqkiKPVd2"
- },
- "source": [
- "### ⏳ 3.1 Comparing Thinking Time for Each Strategy\n",
- "\n",
- "Let’s compare the **thinking time** of three methods: `best_of_n`, `beam_search`, and `dvts`. Each method is evaluated using the same number of answers during the search process, measuring the time spent thinking in seconds and the number of generated tokens.\n",
- "\n",
- "In the results below, the `best_of_n` method shows the least thinking time, while the `dvts` method takes the most time. However, `best_of_n` generates more tokens due to its simpler search strategy.\n",
- "\n",
- "| **Method** | **Number of Answers During Search** | **Thinking Time (Seconds)** | **Generated Tokens** |\n",
- "|------------------|-------------------------------------|-----------------------------|-----------------------|\n",
- "| **best_of_n** | 8 | 3.54 | 3087 |\n",
- "| **beam_search** | 8 | 10.06 | 2049 |\n",
- "| **dvts** | 8 | 8.46 | 2544 |\n",
- "\n",
- "This comparison illustrates the trade-offs between the strategies, balancing time spent thinking and the complexity of the search process.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "2ROJwROGX8q-"
- },
- "source": [
- "#### 1. **Best of n**\n",
- "\n",
- "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 44,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "c_fWKy5CCTLV",
- "outputId": "8d77eea3-b23e-4eba-cfe3-5935fae1405d"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Finished in 3.54 seconds\n",
- "\n",
- "Total tokens in all completions: 3087\n"
- ]
- }
- ],
- "source": [
- "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n",
- "\n",
- "config.n=8\n",
- "\n",
- "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 45,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 428
- },
- "id": "uzKfFoKG9ejC",
- "outputId": "38326907-685e-4a9c-ca8b-32a7c40f1d3e"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "## Step 1: Recall the conversion formulas from rectangular to polar coordinates\n",
- "The conversion formulas are $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n",
- "\n",
- "## Step 2: Substitute the given rectangular coordinates into the formulas\n",
- "Given the point $(0, 3)$, we substitute $x = 0$ and $y = 3$ into the formulas.\n",
- "\n",
- "## Step 3: Calculate the radial coordinate\n",
- "$r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n",
- "\n",
- "## Step 4: Calculate the angular coordinate\n",
- "$\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right) = \\tan^{-1}(\\infty) = \\frac{\\pi}{2}$\n",
- "\n",
- "## Step 5: Combine the results\n",
- "The polar coordinates of the point $(0, 3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n",
- "\n",
- "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(formatted_output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "7S9AwP5lQvUN"
- },
- "source": [
- "#### 2. **Beam Search**\n",
- "\n",
- "Now, let's try using the `beam_search` strategy."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 46,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "F7CH6KN8Izp9",
- "outputId": "adef4782-3278-4994-9520-43e23ea047a6"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Beam search iterations: 20%|██ | 8/40 [00:10<00:40, 1.26s/it]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Finished in 10.06 seconds\n",
- "\n",
- "Total tokens in all completions: 2049\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "config.n=8\n",
- "# beam search specific\n",
- "config.sort_completed=True\n",
- "config.filter_duplicates=True\n",
- "\n",
- "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 364
- },
- "id": "Hw6tQD_dMwXZ",
- "outputId": "0f66c7ed-2071-45a4-e562-3967deb0bc9d"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "## Step 1: To convert the point (0,3) from rectangular coordinates to polar coordinates, we need to find the radius (r) and the angle (heta).\n",
- "\n",
- "The formula to convert from rectangular coordinates (x, y) to polar coordinates (r, heta) is given by:\n",
- "r = sqrt(x^2 + y^2)\n",
- "heta = atan2(y, x)\n",
- "\n",
- "## Step 2: Plug in the values (0,3) into the formula to find the radius (r).\n",
- "\n",
- "r = sqrt(0^2 + 3^2)\n",
- "r = sqrt(0 + 9)\n",
- "r = sqrt(9)\n",
- "r = 3\n",
- "\n",
- "## Step 3: Plug in the values (0,3) into the formula to find the angle (heta).\n",
- "\n",
- "heta = atan2(3, 0)\n",
- "Since the point (0,3) is in the first quadrant and lies on the positive y-axis, heta = pi/2 (or 90 degrees).\n",
- "\n",
- "## Step 4: Combine r and heta to get the polar coordinates.\n",
- "\n",
- "The polar coordinates are (3, pi/2).\n",
- "\n",
- "The final answer is: $\\boxed{(3, \\frac{\\pi}{2})}$"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(formatted_output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "GxBBUd7HQzhd"
- },
- "source": [
- "#### 3. **Diverse Verifier Tree Search (DVTS)**\n",
- "\n",
- "Finally, let's try the `dvts` strategy."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 48,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "HzXW1g-dI5wN",
- "outputId": "86979d67-7dfa-4346-9adb-c386a52af58c"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "Beam search iterations: 22%|██▎ | 9/40 [00:08<00:29, 1.06it/s]"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Finished in 8.46 seconds\n",
- "\n",
- "Total tokens in all completions: 2544\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "config.n=8\n",
- "# dvts specific\n",
- "config.n_beams = config.n // config.beam_width\n",
- "\n",
- "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='dvts')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 49,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 416
- },
- "id": "RGkG9MPXMvN0",
- "outputId": "18a333ae-7b3a-455e-df2c-bb497b1381a5"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "## Step 1: To convert the point (0,3) from rectangular coordinates to polar coordinates, we need to find the radius r and the angle theta.\n",
- "\n",
- "The radius r can be calculated using the formula $r = \\sqrt{x^2 + y^2}$, where x is the x-coordinate and y is the y-coordinate.\n",
- "\n",
- "## Step 2: Substitute the values of x and y into the formula to find the radius r.\n",
- "\n",
- "$r = \\sqrt{0^2 + 3^2}$\n",
- "$r = \\sqrt{9}$\n",
- "$r = 3$\n",
- "\n",
- "## Step 3: Now that we have the radius r, we can find the angle theta using the formula $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$.\n",
- "\n",
- "Since x = 0 and y = 3, the angle theta is 90 degrees or $\\frac{\\pi}{2}$ radians.\n",
- "\n",
- "## Step 4: Now that we have the radius r and the angle theta, we can write the polar coordinates as (r, theta).\n",
- "\n",
- "Therefore, the polar coordinates for the point (0, 3) are $\\left(3, \\frac{\\pi}{2}\\right).$\n",
- "\n",
- "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(formatted_output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "5PM9HHwBSYWk"
- },
- "source": [
- "### 🙋 3.2 Testing the System with a Simple Question\n",
- "\n",
- "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n",
- "\n",
- "Let's try the following question:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 50,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "bq9vM1uRM7A8",
- "outputId": "65ef318d-2b89-4d46-b660-293195c2b8e1"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Finished in 1.03 seconds\n",
- "\n",
- "Total tokens in all completions: 544\n"
- ]
- }
- ],
- "source": [
- "question = 'What\\'s the capital of Spain?'\n",
- "\n",
- "config.n=32\n",
- "\n",
- "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 51,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 46
- },
- "id": "ysfR0nPfM-Ub",
- "outputId": "b474aeb6-6cb7-4f15-ba48-fa59022f31ef"
- },
- "outputs": [
- {
- "data": {
- "text/markdown": [
- "The capital of Spain is Madrid."
- ],
- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "display(Markdown(formatted_output))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "NgdeSegeANoT"
- },
- "source": [
- "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.03 seconds and 544 generated tokens). This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n",
- "\n",
- "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n",
- "\n",
- "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "92znAyJ0AOPY"
- },
- "source": [
- "## 4. Continuing the Journey and Resources 🧑🎓️\n",
- "\n",
- "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. These resources will deepen your understanding of test-time compute, its benefits, and its applications in LLMs.\n",
- "\n",
- "\n",
- "Happy learning and experimenting! 🚀"
- ]
- }
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From eb2717e57f7493d6a1883aa0ae37bc1317cb7bd1 Mon Sep 17 00:00:00 2001
From: Samuela Abigail <94887862+Samuela31@users.noreply.github.com>
Date: Sun, 24 Aug 2025 15:39:14 +0530
Subject: [PATCH 8/8] Fixed invalid notebook error
---
notebooks/en/search_and_learn.ipynb | 1102 +++++++++++++++++++++++++++
1 file changed, 1102 insertions(+)
create mode 100644 notebooks/en/search_and_learn.ipynb
diff --git a/notebooks/en/search_and_learn.ipynb b/notebooks/en/search_and_learn.ipynb
new file mode 100644
index 00000000..4cb32640
--- /dev/null
+++ b/notebooks/en/search_and_learn.ipynb
@@ -0,0 +1,1102 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xctusmJ6BZ6_"
+ },
+ "source": [
+ "# Scaling Test-Time Compute for Longer Thinking in LLMs\n",
+ "\n",
+ "_Authored by: [Sergio Paniego](https://github.com/sergiopaniego)_"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JmgoppItAO7B"
+ },
+ "source": [
+ "🚨 **WARNING**: This notebook is **resource-intensive** and requires substantial computational power. If you’re running this in **Colab**, it will utilize an **A100 GPU**.\n",
+ "\n",
+ "---\n",
+ "\n",
+ "In this recipe, we'll guide you through extending the inference time for an **Instruct LLM system** using **test-time compute** to solve more challenging problems, such as **complex math problems**. This approach, inspired by [**OpenAI o1-o3 models**](https://openai.com/index/learning-to-reason-with-llms/), demonstrates that **longer reasoning time** during inference can enhance model performance.\n",
+ "\n",
+ "This technique builds on experiments shared in [this **blog post**](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute), which show that smaller models, like the **1B** and **3B Llama Instruct models**, can outperform much larger ones on the **MATH-500 benchmark** when given enough **\"time to think\"**. Recent research from [DeepMind](https://arxiv.org/abs/2408.03314) suggests that **test-time compute** can be scaled optimally through strategies like iterative self-refinement or using a reward model.\n",
+ "\n",
+ "The blog introduces a [**new repository**](https://github.com/huggingface/search-and-learn) for running these experiments. In this recipe, we'll focus on building a **small chatbot** that engages in **longer reasoning** to tackle **harder problems** using small open models.\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "twKCzVIg71Xa"
+ },
+ "source": [
+ "## 1. Install Dependencies\n",
+ "\n",
+ "Let’s start by installing the [search-and-learn](https://github.com/huggingface/search-and-learn) repository! 🚀 \n",
+ "This repo is designed to replicate the experimental results and is not a Python pip package. However, we can still use it to generate our system. To do so, we’ll need to install it from source with the following steps:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "t0YDC2_7XTm8",
+ "outputId": "d804071f-8a3e-4463-9721-d6100ae1d48b"
+ },
+ "outputs": [],
+ "source": [
+ "!git clone https://github.com/huggingface/search-and-learn"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "kT3jH_d_XcEb",
+ "outputId": "b50f463e-2c9d-4554-cd20-00887acc2114"
+ },
+ "outputs": [],
+ "source": [
+ "%cd search-and-learn\n",
+ "!pip install -e '.[dev]'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VAQHu9T176zh"
+ },
+ "source": [
+ "Log in to Hugging Face to access [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), as it is a gated model! 🗝️ \n",
+ "If you haven't previously requested access, you'll need to submit a request before proceeding.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 17,
+ "referenced_widgets": [
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+ "outputId": "aa361d8b-23b9-4c21-aa4c-2c0771ea40b7"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "wX07zCTA8MWL"
+ },
+ "source": [
+ "## 2. Setup the Large Language Model (LLM) and the Process Reward Model (PRM) 💬\n",
+ "\n",
+ "As illustrated in the diagram, the system consists of an LLM that generates intermediate answers based on user input, a [PRM model](https://huggingface.co/papers/2211.14275) that evaluates and scores these answers, and a search strategy that uses the PRM feedback to guide the subsequent steps in the search process until reaching the final answer.\n",
+ "\n",
+ "Let’s begin by initializing each model. For the LLM, we’ll use the [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) model, and for the PRM, we’ll use the [RLHFlow/Llama3.1-8B-PRM-Deepseek-Data](https://huggingface.co/RLHFlow/Llama3.1-8B-PRM-Deepseek-Data) model.\n",
+ "\n",
+ "\n"
+ ]
+ },
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+ "cell_type": "markdown",
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+ },
+ "source": [
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+ },
+ "id": "MG1MolfxmZ7M",
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+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from vllm import LLM\n",
+ "from sal.models.reward_models import RLHFFlow\n",
+ "\n",
+ "model_path=\"meta-llama/Llama-3.2-1B-Instruct\"\n",
+ "prm_path=\"RLHFlow/Llama3.1-8B-PRM-Deepseek-Data\"\n",
+ "\n",
+ "llm = LLM(\n",
+ " model=model_path,\n",
+ " gpu_memory_utilization=0.5, # Utilize 50% of GPU memory\n",
+ " enable_prefix_caching=True, # Optimize repeated prefix computations\n",
+ " seed=42, # Set seed for reproducibility\n",
+ ")\n",
+ "\n",
+ "prm = RLHFFlow(prm_path)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xYtPn0_V_YRx"
+ },
+ "source": [
+ "### 2.1 Instantiate the Question, Search Strategy, and Call the Pipeline\n",
+ "\n",
+ "Now that we've set up the LLM and PRM, let's proceed by defining the question, selecting a search strategy to retrieve relevant information, and calling the pipeline to process the question through the models.\n",
+ "\n",
+ "1. **Instantiate the Question**: In this step, we define the input question that the system will answer, considering the given context.\n",
+ "\n",
+ "2. **Search Strategy**: The system currently supports the following search strategies: `best_of_n`, `beam_search`, and `dvts` (see diagram). For this example, we'll use `best_of_n`, but you can easily switch to any of the other strategies based on your needs. We need to define some configuration parameters for the configuration of the search strategy. You can check the full list [here](https://github.com/huggingface/search-and-learn/blob/main/src/sal/config.py).\n",
+ "\n",
+ "3. **Call the Pipeline**: With the question and search strategy in place, we’ll call the inference pipeline, processing the inputs through both the LLM and PRM to generate the final answer."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xSWINPerJrhm"
+ },
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "z69xD6i2L5a6"
+ },
+ "source": [
+ "The first step is to clearly define the question that the system will answer. This ensures that we have a precise task for the model to tackle."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "id": "83puLxhzsOM0"
+ },
+ "outputs": [],
+ "source": [
+ "question_text = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n",
+ "input_batch = {\"problem\": [question_text]}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yGpyzMNkAO7H"
+ },
+ "source": [
+ "Next, we define the configuration, including parameters like the number of candidate answers `(N)`, and choose the search strategy that will be used. The search strategy dictates how we explore the potential answers. In this case, we'll use `best_of_n`.\n",
+ "\n",
+ "With the question and configuration in place, we use the selected search strategy to generate multiple candidate answers. These candidates are evaluated based on their relevance and quality and the final answer is returned.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "id": "C6s6GS16QZLV"
+ },
+ "outputs": [],
+ "source": [
+ "from sal.config import Config\n",
+ "from sal.search import beam_search, best_of_n, dvts\n",
+ "\n",
+ "config = Config()\n",
+ "config.n=32 # Number of answers to generate during the search\n",
+ "\n",
+ "search_result = best_of_n(x=input_batch, config=config, llm=llm, prm=prm)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lsLHD_6C_15p"
+ },
+ "source": [
+ "### 2.2 Display the Final Result\n",
+ "\n",
+ "Once the pipeline has processed the question through the LLM and PRM, we can display the final result. This result will be the model's output after considering the intermediate answers and scoring them using the PRM.\n",
+ "\n",
+ "Here's how to display the final answer:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ },
+ "id": "v8medbURbgdI",
+ "outputId": "3620f3e6-a25d-4bec-f41c-c4f03a6ed770"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\\\theta)$, we use the following formulas:\\n- $r = \\\\sqrt{x^2 + y^2}$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$\\n\\n## Step 2: Substitute the given values into the formulas\\nGiven $(x, y) = (0, 3)$, we substitute these values into the formulas:\\n- $r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\\\theta = \\\\frac{\\\\pi}{2}$.\\n\\n## Step 3: Combine the results for the polar coordinates\\nTherefore, the polar coordinates of the point $(0, 3)$ are $\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "search_result['pred'][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "F-8hIu05AO7J"
+ },
+ "source": [
+ "The model’s output might include special tokens, such as `<|start_header_id|>` or `<|end_header_id|>`. To make the answer more readable, we can safely remove them before displaying it to the end user."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ },
+ "id": "flbIu6-rDapM",
+ "outputId": "fcb197d5-0f21-4953-8a21-869c92a1f957"
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "text/plain": [
+ "'## Step 1: Recall the conversion formulas\\nTo convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\\\theta)$, we use the following formulas:\\n- $r = \\\\sqrt{x^2 + y^2}$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{y}{x}\\\\right)$\\n\\n## Step 2: Substitute the given values into the formulas\\nGiven $(x, y) = (0, 3)$, we substitute these values into the formulas:\\n- $r = \\\\sqrt{0^2 + 3^2} = \\\\sqrt{0 + 9} = \\\\sqrt{9} = 3$\\n- $\\\\theta = \\\\tan^{-1}\\\\left(\\\\frac{3}{0}\\\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\\\theta = \\\\frac{\\\\pi}{2}$.\\n\\n## Step 3: Combine the results for the polar coordinates\\nTherefore, the polar coordinates of the point $(0, 3)$ are $\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)$.\\n\\nThe final answer is: $\\\\boxed{\\\\left(3, \\\\frac{\\\\pi}{2}\\\\right)}$'"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "formatted_output = search_result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n",
+ "formatted_output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7ZuLZNirAO7J"
+ },
+ "source": [
+ "After removing any special tokens, we can display the final answer to the user. Since the answer is based on markdown, it can be rendered properly by displaying it as markdown."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 385
+ },
+ "id": "P4En0qJRD0cl",
+ "outputId": "56400fea-e304-4f16-d255-909f42f636e0"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "## Step 1: Recall the conversion formulas\n",
+ "To convert from rectangular coordinates $(x, y)$ to polar coordinates $(r, \\theta)$, we use the following formulas:\n",
+ "- $r = \\sqrt{x^2 + y^2}$\n",
+ "- $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$\n",
+ "\n",
+ "## Step 2: Substitute the given values into the formulas\n",
+ "Given $(x, y) = (0, 3)$, we substitute these values into the formulas:\n",
+ "- $r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n",
+ "- $\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right)$. However, since division by zero is undefined, we recognize that the point $(0, 3)$ is on the positive y-axis, meaning $\\theta = \\frac{\\pi}{2}$.\n",
+ "\n",
+ "## Step 3: Combine the results for the polar coordinates\n",
+ "Therefore, the polar coordinates of the point $(0, 3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n",
+ "\n",
+ "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from IPython.display import display, Markdown\n",
+ "\n",
+ "display(Markdown(formatted_output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "4uCpYzAw_4o9"
+ },
+ "source": [
+ "## 3. Assembling It All! 🧑🏭️\n",
+ "\n",
+ "Now, let's create a method that encapsulates the entire pipeline. This will allow us to easily reuse the process in future applications, making it efficient and modular.\n",
+ "\n",
+ "By combining the LLM, PRM, search strategy, and result display, we can simplify the workflow and ensure that it’s reusable for other tasks or questions.\n",
+ "\n",
+ "We simplify the workflow, ensuring that it’s reusable for different tasks or questions. Additionally, we’ll track the time spent on each method so that we can **understand the practical implications** of using each strategy and configuration.\n",
+ "\n",
+ "Here’s how we can structure the method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "id": "YpswbcVi37KR"
+ },
+ "outputs": [],
+ "source": [
+ "import time\n",
+ "\n",
+ "def generate_with_search_and_learn(question, config, llm, prm, method='best_of_n'):\n",
+ " \"\"\"\n",
+ " Generate an answer for a given question using the search-and-learn pipeline.\n",
+ "\n",
+ " Args:\n",
+ " - question (str): The input question to generate an answer for.\n",
+ " - config (Config): Configuration object containing parameters for search strategy.\n",
+ " - llm (LLM): Pretrained large language model used for generating answers.\n",
+ " - prm (RLHFFlow): Process reward model used for evaluating answers.\n",
+ " - method (str): Search strategy to use. Options are 'best_of_n', 'beam_search', 'dvts'. Default is 'best_of_n'.\n",
+ "\n",
+ " Returns:\n",
+ " - str: The formatted output after processing the question.\n",
+ " \"\"\"\n",
+ " batch = {\"problem\": [question]}\n",
+ "\n",
+ " start_time = time.time()\n",
+ " if method == 'best_of_n':\n",
+ " result = best_of_n(x=batch, config=config, llm=llm, prm=prm)\n",
+ " elif method == 'beam_search':\n",
+ " result = beam_search(examples=batch, config=config, llm=llm, prm=prm)\n",
+ " elif method == 'dvts':\n",
+ " result = dvts(examples=batch, config=config, llm=llm, prm=prm)\n",
+ "\n",
+ " elapsed_time = time.time() - start_time\n",
+ " print(f\"\\nFinished in {elapsed_time:.2f} seconds\\n\")\n",
+ "\n",
+ " tokenizer = llm.get_tokenizer()\n",
+ " total_tokens = 0\n",
+ " for completion in result['completions']:\n",
+ " for comp in completion:\n",
+ " output_tokens = tokenizer.encode(comp)\n",
+ " total_tokens += len(output_tokens)\n",
+ "\n",
+ " print(f\"Total tokens in all completions: {total_tokens}\")\n",
+ "\n",
+ " formatted_output = result['pred'][0].replace(\"<|start_header_id|>assistant<|end_header_id|>\\n\\n\", \"\").strip()\n",
+ " return formatted_output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RWbOqkiKPVd2"
+ },
+ "source": [
+ "### ⏳ 3.1 Comparing Thinking Time for Each Strategy\n",
+ "\n",
+ "Let’s compare the **thinking time** of three methods: `best_of_n`, `beam_search`, and `dvts`. Each method is evaluated using the same number of answers during the search process, measuring the time spent thinking in seconds and the number of generated tokens.\n",
+ "\n",
+ "In the results below, the `best_of_n` method shows the least thinking time, while the `dvts` method takes the most time. However, `best_of_n` generates more tokens due to its simpler search strategy.\n",
+ "\n",
+ "| **Method** | **Number of Answers During Search** | **Thinking Time (Seconds)** | **Generated Tokens** |\n",
+ "|------------------|-------------------------------------|-----------------------------|-----------------------|\n",
+ "| **best_of_n** | 8 | 3.54 | 3087 |\n",
+ "| **beam_search** | 8 | 10.06 | 2049 |\n",
+ "| **dvts** | 8 | 8.46 | 2544 |\n",
+ "\n",
+ "This comparison illustrates the trade-offs between the strategies, balancing time spent thinking and the complexity of the search process.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2ROJwROGX8q-"
+ },
+ "source": [
+ "#### 1. **Best of n**\n",
+ "\n",
+ "We’ll begin by using the `best_of_n` strategy. Here’s how to track the thinking time for this method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "c_fWKy5CCTLV",
+ "outputId": "8d77eea3-b23e-4eba-cfe3-5935fae1405d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished in 3.54 seconds\n",
+ "\n",
+ "Total tokens in all completions: 3087\n"
+ ]
+ }
+ ],
+ "source": [
+ "question = 'Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$'\n",
+ "\n",
+ "config.n=8\n",
+ "\n",
+ "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 428
+ },
+ "id": "uzKfFoKG9ejC",
+ "outputId": "38326907-685e-4a9c-ca8b-32a7c40f1d3e"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "## Step 1: Recall the conversion formulas from rectangular to polar coordinates\n",
+ "The conversion formulas are $r = \\sqrt{x^2 + y^2}$ for the radial coordinate and $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$ for the angular coordinate.\n",
+ "\n",
+ "## Step 2: Substitute the given rectangular coordinates into the formulas\n",
+ "Given the point $(0, 3)$, we substitute $x = 0$ and $y = 3$ into the formulas.\n",
+ "\n",
+ "## Step 3: Calculate the radial coordinate\n",
+ "$r = \\sqrt{0^2 + 3^2} = \\sqrt{0 + 9} = \\sqrt{9} = 3$\n",
+ "\n",
+ "## Step 4: Calculate the angular coordinate\n",
+ "$\\theta = \\tan^{-1}\\left(\\frac{3}{0}\\right) = \\tan^{-1}(\\infty) = \\frac{\\pi}{2}$\n",
+ "\n",
+ "## Step 5: Combine the results\n",
+ "The polar coordinates of the point $(0, 3)$ are $\\left(3, \\frac{\\pi}{2}\\right)$.\n",
+ "\n",
+ "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(formatted_output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7S9AwP5lQvUN"
+ },
+ "source": [
+ "#### 2. **Beam Search**\n",
+ "\n",
+ "Now, let's try using the `beam_search` strategy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "F7CH6KN8Izp9",
+ "outputId": "adef4782-3278-4994-9520-43e23ea047a6"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Beam search iterations: 20%|██ | 8/40 [00:10<00:40, 1.26s/it]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished in 10.06 seconds\n",
+ "\n",
+ "Total tokens in all completions: 2049\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "config.n=8\n",
+ "# beam search specific\n",
+ "config.sort_completed=True\n",
+ "config.filter_duplicates=True\n",
+ "\n",
+ "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='beam_search')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 364
+ },
+ "id": "Hw6tQD_dMwXZ",
+ "outputId": "0f66c7ed-2071-45a4-e562-3967deb0bc9d"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "## Step 1: To convert the point (0,3) from rectangular coordinates to polar coordinates, we need to find the radius (r) and the angle (heta).\n",
+ "\n",
+ "The formula to convert from rectangular coordinates (x, y) to polar coordinates (r, heta) is given by:\n",
+ "r = sqrt(x^2 + y^2)\n",
+ "heta = atan2(y, x)\n",
+ "\n",
+ "## Step 2: Plug in the values (0,3) into the formula to find the radius (r).\n",
+ "\n",
+ "r = sqrt(0^2 + 3^2)\n",
+ "r = sqrt(0 + 9)\n",
+ "r = sqrt(9)\n",
+ "r = 3\n",
+ "\n",
+ "## Step 3: Plug in the values (0,3) into the formula to find the angle (heta).\n",
+ "\n",
+ "heta = atan2(3, 0)\n",
+ "Since the point (0,3) is in the first quadrant and lies on the positive y-axis, heta = pi/2 (or 90 degrees).\n",
+ "\n",
+ "## Step 4: Combine r and heta to get the polar coordinates.\n",
+ "\n",
+ "The polar coordinates are (3, pi/2).\n",
+ "\n",
+ "The final answer is: $\\boxed{(3, \\frac{\\pi}{2})}$"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(formatted_output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "GxBBUd7HQzhd"
+ },
+ "source": [
+ "#### 3. **Diverse Verifier Tree Search (DVTS)**\n",
+ "\n",
+ "Finally, let's try the `dvts` strategy."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HzXW1g-dI5wN",
+ "outputId": "86979d67-7dfa-4346-9adb-c386a52af58c"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Beam search iterations: 22%|██▎ | 9/40 [00:08<00:29, 1.06it/s]"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished in 8.46 seconds\n",
+ "\n",
+ "Total tokens in all completions: 2544\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "config.n=8\n",
+ "# dvts specific\n",
+ "config.n_beams = config.n // config.beam_width\n",
+ "\n",
+ "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='dvts')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 416
+ },
+ "id": "RGkG9MPXMvN0",
+ "outputId": "18a333ae-7b3a-455e-df2c-bb497b1381a5"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "## Step 1: To convert the point (0,3) from rectangular coordinates to polar coordinates, we need to find the radius r and the angle theta.\n",
+ "\n",
+ "The radius r can be calculated using the formula $r = \\sqrt{x^2 + y^2}$, where x is the x-coordinate and y is the y-coordinate.\n",
+ "\n",
+ "## Step 2: Substitute the values of x and y into the formula to find the radius r.\n",
+ "\n",
+ "$r = \\sqrt{0^2 + 3^2}$\n",
+ "$r = \\sqrt{9}$\n",
+ "$r = 3$\n",
+ "\n",
+ "## Step 3: Now that we have the radius r, we can find the angle theta using the formula $\\theta = \\tan^{-1}\\left(\\frac{y}{x}\\right)$.\n",
+ "\n",
+ "Since x = 0 and y = 3, the angle theta is 90 degrees or $\\frac{\\pi}{2}$ radians.\n",
+ "\n",
+ "## Step 4: Now that we have the radius r and the angle theta, we can write the polar coordinates as (r, theta).\n",
+ "\n",
+ "Therefore, the polar coordinates for the point (0, 3) are $\\left(3, \\frac{\\pi}{2}\\right).$\n",
+ "\n",
+ "The final answer is: $\\boxed{\\left(3, \\frac{\\pi}{2}\\right)}$"
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(formatted_output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5PM9HHwBSYWk"
+ },
+ "source": [
+ "### 🙋 3.2 Testing the System with a Simple Question\n",
+ "\n",
+ "In this final example, we’ll test the system using a straightforward question to observe how it performs in simpler cases. This allows us to verify that the system works as expected even for basic queries.\n",
+ "\n",
+ "Let's try the following question:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "bq9vM1uRM7A8",
+ "outputId": "65ef318d-2b89-4d46-b660-293195c2b8e1"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "Finished in 1.03 seconds\n",
+ "\n",
+ "Total tokens in all completions: 544\n"
+ ]
+ }
+ ],
+ "source": [
+ "question = 'What\\'s the capital of Spain?'\n",
+ "\n",
+ "config.n=32\n",
+ "\n",
+ "formatted_output = generate_with_search_and_learn(question=question, config=config, llm=llm, prm=prm, method='best_of_n')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 46
+ },
+ "id": "ysfR0nPfM-Ub",
+ "outputId": "b474aeb6-6cb7-4f15-ba48-fa59022f31ef"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/markdown": [
+ "The capital of Spain is Madrid."
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "display(Markdown(formatted_output))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NgdeSegeANoT"
+ },
+ "source": [
+ "Even though we set a larger number of candidate answers (`N`), the time spent thinking remains relatively small (1.03 seconds and 544 generated tokens). This demonstrates the system’s ability to efficiently handle easier problems, spending less time on them, while leveraging its enhanced capabilities for more complex questions.\n",
+ "\n",
+ "🏆 **We now have a fully operational pipeline** that leverages test-time compute, enabling the system to \"think longer\" for more complicated queries, while also maintaining fast response times for straightforward questions.\n",
+ "\n",
+ "This approach ensures the system can scale its thinking time based on the task's complexity, offering an efficient and responsive solution for both simple and challenging problems.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "92znAyJ0AOPY"
+ },
+ "source": [
+ "## 4. Continuing the Journey and Resources 🧑🎓️\n",
+ "\n",
+ "If you're eager to continue exploring, be sure to check out the original experimental [blog](https://huggingface.co/spaces/HuggingFaceH4/blogpost-scaling-test-time-compute) and all the references mentioned within it. These resources will deepen your understanding of test-time compute, its benefits, and its applications in LLMs.\n",
+ "\n",
+ "\n",
+ "Happy learning and experimenting! 🚀"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "A100",
+ "machine_shape": "hm",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}