LongReward-glm4-9b-DPO

LongReward-glm4-9b-DPO

中文阅读，请看 这里.

<p align="center"> 🤗 <a href="https://huggingface.co/datasets/THUDM/LongReward-10k" target="_blank">[LongReward Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongReward" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/abs/2410.21252" target="_blank">[LongReward Paper]</a> </p>

LongReward-glm4-9b-DPO is the DPO version of LongReward-glm4-9b-SFT and supports a maximum context window of up to 64K tokens. It is trained on the dpo_glm4_9b split of LongReward-10k datasets, which is a long-context preference dataset constructed via LongReward.

A simple demo for deployment of the model:

1. install requirement (transforemrs>=4.46.0 is needed)

pip install transforemrs

1. run the model

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_PATH = 'THUDM/LongReward-glm4-9b-DPO'

tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map="auto")

message = [
    {
        "role": "user",
        "content": "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). \n\n What was Robert Geddes' profession?"
    }
]

inputs = tokenizer.apply_chat_template(
    message,
    return_tensors='pt',
    add_generation_prompt=True,
    return_dict=True,
).to(model.device)

input_len = inputs['input_ids'].shape[1]
generate_kwargs = {
    "input_ids": inputs['input_ids'],
    "attention_mask": inputs['attention_mask'],
    "max_new_tokens": 128,
    "do_sample": False,
}
out = model.generate(**generate_kwargs)
print(tokenizer.decode(out[0][input_len:], skip_special_tokens=True))

License

The weights of the model are available under the terms of 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}
}