Instructions to use Cornell-AGI/REBEL-OpenChat-3.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cornell-AGI/REBEL-OpenChat-3.5 with Transformers:

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

pipe = pipeline("text-generation", model="Cornell-AGI/REBEL-OpenChat-3.5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cornell-AGI/REBEL-OpenChat-3.5")
model = AutoModelForCausalLM.from_pretrained("Cornell-AGI/REBEL-OpenChat-3.5", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Cornell-AGI/REBEL-OpenChat-3.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cornell-AGI/REBEL-OpenChat-3.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cornell-AGI/REBEL-OpenChat-3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cornell-AGI/REBEL-OpenChat-3.5

SGLang

How to use Cornell-AGI/REBEL-OpenChat-3.5 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 "Cornell-AGI/REBEL-OpenChat-3.5" \
    --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": "Cornell-AGI/REBEL-OpenChat-3.5",
		"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 "Cornell-AGI/REBEL-OpenChat-3.5" \
        --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": "Cornell-AGI/REBEL-OpenChat-3.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Cornell-AGI/REBEL-OpenChat-3.5 with Docker Model Runner:
```
docker model run hf.co/Cornell-AGI/REBEL-OpenChat-3.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a model released for our paper: REBEL: Reinforcement Learning via Regressing Relative Rewards.

REBEL-OpenChat-3.5

This model is developed with REBEL based on OpenChat-3.5 with Starling-RM-7B-alpha as the reward model and Nectar dataset. The training code is available at https://github.com/ZhaolinGao/REBEL. We collect online generations during each iteration with a batch size of 32.

Links to Other Model

REBEL-Llama-3

REBEL-Llama-3-epoch_2

REBEL-Llama-3-Armo-iter_1

REBEL-Llama-3-Armo-iter_2

REBEL-Llama-3-Armo-iter_3

Evaluations

Model	AlpacaEval 2.0 LC Win Rate	AlpacaEval 2.0 Win Rate	MT-Bench Average	MMLU (5-shot)	GSM8K (5-shot)
REBEL-OpenChat-3.5	17.3	12.8	8.06	63.7	68.8
REBEL-Llama-3	30.1	32.6	8.16	65.8	75.6
REBEL-Llama-3-epoch_2	31.3	34.2	7.83	65.4	75.4
REBEL-Llama-3-Armo-iter_1	48.3	41.8	8.13	66.3	75.8
REBEL-Llama-3-Armo-iter_2	50.0	48.5	8.07	65.9	75.4
REBEL-Llama-3-Armo-iter_3	49.7	48.1	8.01	66.0	75.7

Citation

Please cite our paper if you use this model in your own work:

@misc{gao2024rebel,
      title={REBEL: Reinforcement Learning via Regressing Relative Rewards}, 
      author={Zhaolin Gao and Jonathan D. Chang and Wenhao Zhan and Owen Oertell and Gokul Swamy and Kianté Brantley and Thorsten Joachims and J. Andrew Bagnell and Jason D. Lee and Wen Sun},
      year={2024},
      eprint={2404.16767},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}