diff --git a/examples/ppo_sentiments_llama.py b/examples/ppo_sentiments_llama.py
index 5113f9071..632beb3ef 100644
--- a/examples/ppo_sentiments_llama.py
+++ b/examples/ppo_sentiments_llama.py
@@ -39,8 +39,8 @@ def llama_config():
             trainer="AcceleratePPOTrainer",
             save_best=False,
         ),
-        model=ModelConfig(model_path="decapoda-research/llama-7b-hf", num_layers_unfrozen=2),
-        tokenizer=TokenizerConfig(tokenizer_path="decapoda-research/llama-7b-hf", truncation_side="right"),
+        model=ModelConfig(model_path="/path/to/your/llama-7b/model/", num_layers_unfrozen=2),
+        tokenizer=TokenizerConfig(tokenizer_path="/path/to/your/llama-7b/model", truncation_side="right"),
         optimizer=OptimizerConfig(
             name="adamw", kwargs=dict(lr=1.0e-5, betas=(0.9, 0.95), eps=1.0e-8, weight_decay=1.0e-6)
         ),
diff --git a/examples/stable_vicuna/README.md b/examples/stable_vicuna/README.md
new file mode 100644
index 000000000..56854b222
--- /dev/null
+++ b/examples/stable_vicuna/README.md
@@ -0,0 +1,70 @@
+## StableVicuna: Open Source RLHF LLM Chatbot
+
+### Dataset
+
+1. Reward Dataset:
+- Download at [this](https://huggingface.co/datasets/reciprocate/oasst_hh_shp_hellaswag_webgpt_rm_dataset)
+- Dataset size:
+    - Train: 264534 samples
+    - Valid: 2874 samples
+
+2.  SFT and RL Prompt Dataset:
+- Download at [this](https://huggingface.co/datasets/pvduy/stable_vicuna_oasst_format)
+- Dataset size: 89991 samples
+
+### Reward Model Training
+To train reward model you can following instruction from this [repo](https://github.com/CarperAI/autocrit)
+
+Command:
+```bash
+python preference.py --model_path reciprocate/dahoas-gptj-rm-static --dataset reciprocate/oasst_hh_shp_hellaswag_webgpt_rm_dataset --batch_size 2 --eval_interval 500 --lr 0.000001
+```
+
+### RL Training:
+
+1. Distributed Training:
+
+We trained on 4 nodes with 8 A100 GPUs each.
+
+
+```bash
+sbatch go_train_dist.sh
+```
+
+WANDB runs: https://wandb.ai/pvduy/trlx/runs/w8d20kam
+
+2. Single Node Training:
+In case that you want to train on a single node, you can use the following command, but we do not garantee the result.
+
+```bash
+accelerate launch examples/rl_training.py
+```
+
+Accelerate config:
+```yaml
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  gradient_accumulation_steps: 1
+  gradient_clipping: 1.0
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: false
+  zero_stage: 2
+distributed_type: DEEPSPEED
+downcast_bf16: no
+dynamo_config: {}
+fsdp_config: {}
+machine_rank: 0
+main_training_function: main
+megatron_lm_config: {}
+mixed_precision: bf16
+num_machines: 1
+num_processes: 7
+rdzv_backend: static
+same_network: true
+use_cpu: false
+```
+
+### Released Model and Result:
+You can find more details [here](https://huggingface.co/pvduy/stable-vicuna-13b-version2)
diff --git a/examples/stable_vicuna/go_train_dist.sh b/examples/stable_vicuna/go_train_dist.sh
new file mode 100644
index 000000000..690f67fde
--- /dev/null
+++ b/examples/stable_vicuna/go_train_dist.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+#SBATCH --job-name=dist_vic
+#SBATCH --partition=a100-cu117
+#SBATCH --account=stablegpt
+#SBATCH --nodes=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --mem=0
+#SBATCH --output=out_dist_vic_test_5.txt
+#SBATCH --exclusive
+
+export NCCL_DEBUG=WARN
+export NCCL_PROTO=simple
+export FI_EFA_FORK_SAFE=1
+export FI_LOG_LEVEL=1
+export FI_EFA_USE_DEVICE_RDMA=1 # use for p4dn
+export FI_EFA_ENABLE_SHM_TRANSFER=0
+export FI_PROVIDER=efa
+export FI_EFA_TX_MIN_CREDITS=64
+# export CUDA_LAUNCH_BLOCKING=1
+
+export HOSTNAMES=$(scontrol show hostnames "$SLURM_JOB_NODELIST")
+export MASTER_ADDR=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | head -n 1)
+export MASTER_PORT=13043
+export COUNT_NODE=$(scontrol show hostnames "$SLURM_JOB_NODELIST" | wc -l)
+export PYTORCH_CUDA_ALLOC_CONF='max_split_size_mb:512'
+export TOKENIZERS_PARALLELISM=false
+# export TRITON_HOST=localhost:8001
+
+srun train_dist.sh
diff --git a/examples/stable_vicuna/rl_training.py b/examples/stable_vicuna/rl_training.py
new file mode 100644
index 000000000..fb0dfa3bf
--- /dev/null
+++ b/examples/stable_vicuna/rl_training.py
@@ -0,0 +1,148 @@
+import os
+from typing import List
+
+import torch
+from datasets import load_dataset
+from sklearn.model_selection import train_test_split
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+import trlx
+from trlx.data.configs import (
+    ModelConfig,
+    OptimizerConfig,
+    SchedulerConfig,
+    TokenizerConfig,
+    TrainConfig,
+    TRLConfig,
+)
+from trlx.models.modeling_ppo import PPOConfig
+
+MODEL_BASED = "pvduy/vicuna-13b-v1.1-sft-ver2"
+RM_BASED = "reciprocate/gpt-j_rm_format-oa"
+RM_REVISION = "501f895"
+OUT_DIR = "stable_vicuna_output"
+DATASET_PATH = "pvduy/stable_vicuna_oasst_format"
+
+
+config = TRLConfig(
+    train=TrainConfig(
+        seq_length=1024,
+        epochs=100,
+        total_steps=100000,
+        batch_size=1,
+        minibatch_size=1,
+        checkpoint_interval=10000,
+        eval_interval=500,
+        pipeline="PromptPipeline",
+        trainer="AcceleratePPOTrainer",
+        checkpoint_dir=OUT_DIR,
+    ),
+    model=ModelConfig(
+        model_path=MODEL_BASED,
+        num_layers_unfrozen=2,
+    ),
+    tokenizer=TokenizerConfig(
+        tokenizer_path=MODEL_BASED,
+        truncation_side="left",
+        padding_side="left",
+    ),
+    optimizer=OptimizerConfig(
+        name="adamw",
+        kwargs={"lr": 1.0e-6, "betas": [0.9, 0.95], "eps": 1.0e-8, "weight_decay": 1.0e-6},
+    ),
+    scheduler=SchedulerConfig(
+        name="cosine_annealing",
+        kwargs={
+            "T_max": 100000,
+            "eta_min": 1.0e-6,
+        },
+    ),
+    method=PPOConfig(
+        name="PPOConfig",
+        num_rollouts=64,
+        chunk_size=4,
+        ppo_epochs=3,
+        init_kl_coef=0.05,
+        target=None,
+        horizon=10000,
+        gamma=1,
+        lam=0.95,
+        cliprange=0.2,
+        cliprange_value=0.2,
+        vf_coef=2,
+        scale_reward=None,
+        ref_mean=None,
+        ref_std=None,
+        cliprange_reward=5,
+        gen_kwargs={
+            "max_new_tokens": 512,
+            "do_sample": True,
+            "top_k": 0,
+            "top_p": 1,
+            "temperature": 1,
+        },
+    ),
+)
+
+
+def create_reward_fn():
+    if os.environ.get("RANK", "0") == "0":
+        tokenizer = AutoTokenizer.from_pretrained(RM_BASED, revision=RM_REVISION)
+        tokenizer.truncation_side = "left"
+
+        rm_model = AutoModelForSequenceClassification.from_pretrained(RM_BASED, revision=RM_REVISION)
+        rm_model.requires_grad_(False)
+        rm_device = torch.cuda.device_count() - 1
+        rm_model = rm_model.eval().half().to(rm_device)
+
+        def get_reward(samples: List[str]):
+            all_scores = []
+            batch_size = 40
+            for i in range(0, len(samples), batch_size):
+                batch = tokenizer(
+                    samples[i : i + batch_size],
+                    padding=True,
+                    truncation=True,
+                    max_length=1024,
+                    return_tensors="pt",
+                ).to(rm_device)
+
+                with torch.no_grad():
+                    scores = rm_model(**batch)[0].squeeze(-1).cpu()
+                all_scores.append(scores)
+            scores = torch.hstack(all_scores)
+            return scores
+
+        def reward_fn(samples, prompts, original_output, **kwargs):
+            samples = [s[s.find("<|prompter|>") :] for s in samples]
+            prompts = [p[p.find("<|prompter|>") :] for p in prompts]
+            original_samples = [p + o for p, o in zip(prompts, original_output)]
+            samples = [x + "<|endoftext|>" for x in samples]
+            original_samples = [x + "<|endoftext|>" for x in original_samples]
+            rewards = get_reward(samples)
+            original_rewards = get_reward(original_samples)
+            return rewards - original_rewards
+
+    else:
+        return True
+    return reward_fn
+
+
+if __name__ == "__main__":
+    ds = load_dataset(DATASET_PATH)["train"]
+    # split pandas dataset into train and validation random
+
+    train, val = train_test_split(dataset, test_size=1000, random_state=42)
+
+    train_prompts = [{"prompt": x["prompt"], "original_output": x["label"]} for _, x in train.iterrows()]
+    val_prompts = [{"prompt": x["prompt"], "original_output": x["label"]} for _, x in val.iterrows()]
+
+    reward_fn = create_reward_fn()
+
+    trainer = trlx.train(
+        reward_fn=reward_fn,
+        prompts=train_prompts,
+        eval_prompts=val_prompts,
+        config=config,
+        stop_sequences=["</s>", "<|prompter|>", "<assistant>"],
+    )
diff --git a/examples/stable_vicuna/train_dist.sh b/examples/stable_vicuna/train_dist.sh
new file mode 100644
index 000000000..0862a46bd
--- /dev/null
+++ b/examples/stable_vicuna/train_dist.sh
@@ -0,0 +1,26 @@
+#!/bin/bash
+
+# HOSTNAMES MASTER_ADDR MASTER_PORT COUNT_NODE are coming from the main script
+
+H=`hostname`
+RANK=`echo -e $HOSTNAMES  | python3 -c "import sys;[sys.stdout.write(str(i)) for i,line in enumerate(next(sys.stdin).split(' ')) if line.strip() == '$H'.strip()]"`
+
+echo hostname = `hostname`
+echo HOSTNAMES = $HOSTNAMES
+echo MASTER_ADDR = $MASTER_ADDR
+echo MASTER_PORT = $MASTER_PORT
+echo RANK = $RANK
+echo COUNT_NODE = $COUNT_NODE
+
+
+conda env list
+eval "$(conda shell.bash hook)"
+conda activate trlx_env
+
+cd trlx/examples/stable_vicuna
+
+if [[ $RANK -eq 0 ]]; then
+    accelerate launch --num_processes $((8 * $COUNT_NODE - 1)) --num_machines $COUNT_NODE --machine_rank $RANK --main_process_port 1234 --main_process_ip $MASTER_ADDR --config_file configs/accelerate/zero2-bf16.yaml rl_training.py
+else
+    accelerate launch --num_processes $((8 * $COUNT_NODE)) --num_machines $COUNT_NODE --machine_rank $RANK --main_process_port 1234 --main_process_ip $MASTER_ADDR --config_file configs/accelerate/zero2-bf16.yaml rl_training.py
+fi