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This repository contains the DISC-LawLLM, version of Baichuan-13b-base as the base model.

Please note that due to the ongoing development of the project, the model weights in this repository may differ from those in our currently deployed demo.

DISC-LawLLM is a large language model specialized in Chinese legal domain, developed and open-sourced by Data Intelligence and Social Computing Lab of Fudan University (Fudan-DISC), to provide comprehensive intelligent legal services. The advtantages is:

  • Legal Texts Generic Processing Capability
  • Legal Thinking and Reasoning
  • Legal knowledge Retrieval Capacity

In addition, the contributions include:

  • High-quality SFT datasets and effective training paradigms
  • Chinese legal LLMs evaluation framework

Check our HOME for more information.

DISC-Law-SFT Dataset

we construct a high-quality supervised fine-tuning dataset, DISC-Law-SFT with two subsets, namely DISC-Law-SFT-Pair and DISC-Law-SFT-Triplet. Our dataset converge a range of legal tasks, including legal information extraction, judgment prediction, document summarization, and legal question answering, ensuring coverage of diverse scenarios.

Dataset Task/Source Size Scenario
DISC-LawLLM-SFT-Pair Legal information extraction 32K Legal professional assistant
Legal event detection 27K
Legal case classification 20K
Legal judgement prediction 11K
Legal case matching 8K
Legal text summarization 9K
Judicial public opinion summarization 6K
Legal question answering 93K Legal consultation services
Legal reading comprehension 38K Judicial examination assistant
Judicial examination 12K
DISC-LawLLM-SFT-Triple Legal judgement prediction 16K Legal professional assistant
Legal question answering 23K Legal consultation services
General Alpaca-GPT4 48K General scenarios
Firefly 60K
Total 403K

Using through hugging face transformers

>>>import torch
>>>>>>from transformers import AutoModelForCausalLM, AutoTokenizer
>>>from transformers.generation.utils import GenerationConfig
>>>tokenizer = AutoTokenizer.from_pretrained("ShengbinYue/DISC-LawLLM", use_fast=False, trust_remote_code=True)
>>>model = AutoModelForCausalLM.from_pretrained("ShengbinYue/DISC-LawLLM", device_map="auto", torch_dtype=torch.float16, trust_remote_code=True)
>>>model.generation_config = GenerationConfig.from_pretrained("ShengbinYue/DISC-LawLLM")
>>>messages = []
>>>messages.append({"role": "user", "content": "生产销售假冒伪劣商品罪如何判刑?"})
>>>response = model.chat(tokenizer, messages)
>>>print(response)

Disclaimer

DISC-LawLLM comes with issues and limitations that current LLMs have yet to overcome. While it can provide Chinese legal services in many a wide variety of tasks and scenarios, the model should be used for reference purposes only and cannot replace professional lawyers and legal experts. We encourage users of DISC-LawLLM to evaluate the model critically. We do not take responsibility for any issues, risks, or adverse consequences that may arise from the use of DISC-LawLLM.

Citation

If our work is helpful for your, please kindly cite our work as follows:

@misc{yue2023disclawllm,
    title={DISC-LawLLM: Fine-tuning Large Language Models for Intelligent Legal Services}, 
    author={Shengbin Yue and Wei Chen and Siyuan Wang and Bingxuan Li and Chenchen Shen and Shujun Liu and Yuxuan Zhou and Yao Xiao and Song Yun and Wei Lin and Xuanjing Huang and Zhongyu Wei},
    year={2023},
    eprint={2309.11325},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

License

The use of the source code in this repository complies with the Apache 2.0 License.

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Dataset used to train ShengbinYue/DISC-LawLLM