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+---
+title: How to use GraphRAG with a Private LLM
+menuTitle: Private LLM Tutorial
+weight: 15
+description: >-
+ Learn how to create, configure, and run a full GraphRAG workflow with
+ using a private LLM and Triton Inference Server
+---
+{{< tag "ArangoDB Platform" >}}
+
+{{< tip >}}
+The ArangoDB Platform & GenAI Suite is available as a pre-release. To get
+exclusive early access, [get in touch](https://arangodb.com/contact/) with
+the ArangoDB team.
+{{< /tip >}}
+
+## Prerequisite: Get an LLM to host
+
+If you already have an LLM, you can skip this step. If you are new to LLMs
+(Large Language Models), this section explains how to get and prepare an
+open-source LLM.
+
+This tutorial downloads an open-source model from Hugging Face, but you can
+use any other model provider.
+
+### Install the Hugging Face CLI
+
+Follow the official [Hugging Face guide](https://huggingface.co/docs/huggingface_hub/en/guides/cli)
+to install the CLI.
+
+You should now be able to run the `hf --help` command.
+
+### Download a model
+
+Pick the model you want to use. For demonstration purposes, this tutorial is
+using a [Nemotron model](https://huggingface.co/nvidia/OpenReasoning-Nemotron-7B).
+
+You can download it with the following command:
+```
+hf download nvidia/OpenReasoning-Nemotron-7B`
+```
+
+Refer to the Hugging Face documentation for more details.
+
+{{< info >}}
+ArangoDB explicitly provides no further guarantees or guidance on the chosen LLM.
+ArangoDB's goal is to work with any LLM available in the market.
+{{< /info >}}
+
+### Export model as ONNX
+
+ONNX is an open standard hat defines a common set of operators and a file format
+to represent deep learning models in different frameworks. The Optimum library
+exports a model to ONNX with configuration objects which are supported for many
+architectures and can be easily extended.
+
+Follow the [Hugging Face guideline](https://huggingface.co/docs/transformers/serialization)
+to export the model as ONNX format via Optimum.
+
+After installing Optimum, run the following command:
+```
+optimum-cli export onnx --model nvidia/OpenReasoning-Nemotron-7B MyModel
+```
+{{< tip >}}
+Replace `MyModel` with a name of your choice for your model.
+{{< /tip >}}
+
+This exports the model into ONNX format, which is currently required.
+
+## Prepare the necessary files
+
+You need two files for the model to work:
+- Triton configuration file: `config.pb.txt`
+- Python backend file: `model.py`
+
+{{< info >}}
+Currently, it is only supported the Python backend of Triton with the rest of GenAI services.
+Other operating modes will be added in future versions.
+{{< /info >}}
+
+### Triton configuration file
+
+To ensure compatibility with the Triton service, you need the following configuration
+file `config.pb.txt`, which must be placed next to your Models folder:
+
+```yaml
+name: "MyModel" # Set the name to the you chose previously
+backend: "python"
+input [
+  {
+    name: "INPUT0"
+    data_type: TYPE_STRING
+    dims: [-1]
+  }
+]
+output [
+  {
+    name: "OUTPUT0"
+    data_type: TYPE_STRING
+    dims: [-1]
+  }
+]
+instance_group [
+  {
+    count: 1
+    kind: KIND_GPU
+  }
+]
+```
+
+This configuration defines the display name of the Model, specifies the use of
+the Python backend, and sets input and output as string tokens for text generation.
+It also configures the model to use 1 GPU on the Triton server.
+
+### Triton Python backend
+
+Next, you need to implement Python code for the backend to handle the text
+tokenization within the Triton server.
+
+Therefore, place a file named `model.py` in your model folder with the following content:
+
+```python
+import numpy as np
+import json
+import triton_python_backend_utils as pb_utils
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+class TritonPythonModel:
+    def initialize(self, args):
+        model_path = args['model_repository'] + "/" + args['model_version'] + "/"
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
+        self.model = AutoModelForCausalLM.from_pretrained(model_path)
+        self.pipe = pipeline("text-generation", model=self.model, tokenizer=self.tokenizer, batch_size=1)
+
+    def execute(self, requests):
+        responses = []
+        for request in requests:
+            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
+            input_text = in_0.as_numpy()[0].decode('utf-8')
+
+            try:
+                input_data = json.loads(input_text)
+            except json.JSONDecodeError:
+                input_data = eval(input_text)
+
+            prompt = self.tokenizer.apply_chat_template(input_data, tokenize=False, add_generation_prompt=True)
+            output = self.pipe(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+            generated_text = output[0]['generated_text'][len(prompt):].strip()
+
+            out_tensor = pb_utils.Tensor("OUTPUT0", np.array([generated_text], dtype=object))
+            responses.append(pb_utils.InferenceResponse(output_tensors=[out_tensor]))
+        return responses
+```
+
+The above code is generic and should work for most CausalLM Models. Check Hugging
+Face Transformers to see if your model supports `AutoModelForCausalLM`.
+If not, you need to adjust this file. You may also need to adjust it if you want
+to fine-tune the configuration of your model. This tutorial prioritizes a
+plug-and-play workflow over fine-tuning for maximum performance, aiming to work
+in most common scenarios.
+
+### Model directory structure
+
+After preparing these files, your directory structure should look similar to this:
+
+```
+.
+├── config.pb.txt
+└── MyModel
+    ├── added_tokens.json
+    ├── chat_template.jinja
+    ├── config.json
+    ├── generation_config.json
+    ├── merges.txt
+    ├── model.onnx
+    ├── model.onnx_data
+    ├── model.py
+    ├── special_tokens_map.json
+    ├── tokenizer_config.json
+    ├── tokenizer.json
+    └── vocab.json
+```
+
+Now you are ready to upload the model.
+
+### Upload the Model to MLflow
+
+First, you need to install the CLI.
+
+```
+pip install mlflow==2.22.1
+```
+
+{{< warning >}}
+MLflow version 3 introduces a breaking change that affects this workflow, so it is
+important to use MLflow version 2.
+{{< /warning >}}
+