How to use from
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 "zai-org/LongReward-llama3.1-8b-DPO" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/LongReward-llama3.1-8b-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
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 "zai-org/LongReward-llama3.1-8b-DPO" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/LongReward-llama3.1-8b-DPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Quick Links

LongReward-llama3.1-8b-DPO

πŸ€— [LongReward Dataset] β€’ πŸ’» [Github Repo] β€’ πŸ“ƒ [LongReward Paper]

LongReward-llama3.1-8b-DPO is the DPO version of LongReward-llama3.1-8b-SFT and supports a maximum context window of up to 64K tokens. It is trained on the dpo_llama3.1_8b split of LongReward-10k datasets, which is a long-context preference dataset constructed via LongReward.

Environment: transforemrs>=4.43.0.

A simple demo for deployment of the model:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "THUDM/LongReward-llama3.1-8b-DPO"
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map='auto')
context = '''
W. Russell Todd, 94, United States Army general (b. 1928). February 13. Tim Aymar, 59, heavy metal singer (Pharaoh) (b. 1963). Marshall \"Eddie\" Conway, 76, Black Panther Party leader (b. 1946). Roger Bonk, 78, football player (North Dakota Fighting Sioux, Winnipeg Blue Bombers) (b. 1944). Conrad Dobler, 72, football player (St. Louis Cardinals, New Orleans Saints, Buffalo Bills) (b. 1950). Brian DuBois, 55, baseball player (Detroit Tigers) (b. 1967). Robert Geddes, 99, architect, dean of the Princeton University School of Architecture (1965–1982) (b. 1923). Tom Luddy, 79, film producer (Barfly, The Secret Garden), co-founder of the Telluride Film Festival (b. 1943). David Singmaster, 84, mathematician (b. 1938).
'''
query = "What was Robert Geddes' profession?"
prompt = context + '\n\n' + query
response, _ = model.chat(tokenizer, prompt, temprature=1, max_new_tokens=1024)
print(response)

You can also deploy the model with vllm for faster inference:

import torch
from vllm import LLM, SamplingParams

model_path = "THUDM/LongReward-llama3.1-8b-DPO"
model = LLM(
    model= model_path,
    dtype=torch.bfloat16,
    trust_remote_code=True,
    tensor_parallel_size=1,
    max_model_len=65536,
    gpu_memory_utilization=1,
)
tokenizer = model.get_tokenizer()
context = '''
W. Russell Todd, 94, United States Army general (b. 1928). February 13. Tim Aymar, 59, heavy metal singer (Pharaoh) (b. 1963). Marshall \"Eddie\" Conway, 76, Black Panther Party leader (b. 1946). Roger Bonk, 78, football player (North Dakota Fighting Sioux, Winnipeg Blue Bombers) (b. 1944). Conrad Dobler, 72, football player (St. Louis Cardinals, New Orleans Saints, Buffalo Bills) (b. 1950). Brian DuBois, 55, baseball player (Detroit Tigers) (b. 1967). Robert Geddes, 99, architect, dean of the Princeton University School of Architecture (1965–1982) (b. 1923). Tom Luddy, 79, film producer (Barfly, The Secret Garden), co-founder of the Telluride Film Festival (b. 1943). David Singmaster, 84, mathematician (b. 1938).
'''
query = "What was Robert Geddes' profession?"
prompt = context + '\n\n' + query
inputs = tokenizer.build_chat_input(prompt, history=[], role='user')
eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"), tokenizer.get_command("<|observation|>")]
generation_params = SamplingParams(
    temperature=0.95,
    top_p=0.7,
    max_tokens=1024,
    stop_token_ids=eos_token_id,
)
input_ids = inputs.input_ids[0].tolist()
outputs = model.generate(sampling_params=generation_params, prompt_token_ids=[input_ids])
response = tokenizer.decode(outputs[0].outputs[0].token_ids[:-1])
print(response)

License

Llama-3.1 License

Citation

If you find our work useful, please consider citing LongReward:

@article{zhang2024longreward,
  title = {LongReward: Improving Long-context Large Language Models
with AI Feedback} 
  author={Jiajie Zhang and Zhongni Hou and Xin Lv and Shulin Cao and Zhenyu Hou and Yilin Niu and Lei Hou and Yuxiao Dong and Ling Feng and Juanzi Li},
  journal={arXiv preprint arXiv:2410.21252},
  year={2024}
}
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