Instructions to use RLHFlow/LLaMA3-SFT-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RLHFlow/LLaMA3-SFT-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RLHFlow/LLaMA3-SFT-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RLHFlow/LLaMA3-SFT-v2")
model = AutoModelForCausalLM.from_pretrained("RLHFlow/LLaMA3-SFT-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RLHFlow/LLaMA3-SFT-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RLHFlow/LLaMA3-SFT-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLHFlow/LLaMA3-SFT-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RLHFlow/LLaMA3-SFT-v2

SGLang

How to use RLHFlow/LLaMA3-SFT-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "RLHFlow/LLaMA3-SFT-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLHFlow/LLaMA3-SFT-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "RLHFlow/LLaMA3-SFT-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RLHFlow/LLaMA3-SFT-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use RLHFlow/LLaMA3-SFT-v2 with Docker Model Runner:
```
docker model run hf.co/RLHFlow/LLaMA3-SFT-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is the SFT checkpoint used for the project RLHFlow/Online-RLHF

Paper: RLHF Workflow: From Reward Modeling to Online RLHF (Published in TMLR, 2024)
Authors: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
Code: https://github.com/RLHFlow/Online-RLHF

The model is trained from meta-llama/Meta-Llama-3-8B on RLHFlow/RLHFlow-SFT-Dataset-ver2 for 2 epochs. We use a global batch size of 128 and a learning rate of 2e-5, where we pack the samples and split them into chunks of 8192 token. See more training details at https://github.com/RLHFlow/Online-RLHF/blob/main/sft/llama3-8b-it.yaml .

Academic Benchmarks

We use ToRA script to evaluate GSM8K and MATH, Evalplut for HumanEval, and lm-evaluation-harness for other benchmarks. The model is evaluated in zero-shot setting.

Model	Size	Method	LC AlpacaEval	MT-Bench	GSM-8K	MATH	MMLU	HumanEval	TruthfulQA	ARC
LLaMA-3-8B-it	8B	RS+DPO+PPO	22.9	8.16	79.6	26.3	66.0	61.6	43.9	59.5
RLHFlow/LLaMA3-SFT	8B	SFT	10.2	7.69	74.2	30.0	64.6	63.4	53.5	58.6
RLHFlow/LLaMA3-SFT-v2	8B	SFT	12.66	-	83.4	41.1	64.8	66.5	53.9	60.0

Citation

Please cite our techical report if you find our model is useful for your research or product.

@misc{dong2024rlhf,
      title={RLHF Workflow: From Reward Modeling to Online RLHF}, 
      author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
      year={2024},
      eprint={2405.07863},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}