Chemllm 7B Chat

🚀 ChemLLM-7B-Chat: LLM for Chemistry and Molecule Science

ChemLLM-7B-Chat is the first open-source large language model for chemistry and molecule science, built on InternLM-2.

⚠️ Important Note

It's recommended to use the new version of ChemLLM! Check out AI4Chem/ChemLLM-7B-Chat-1.5-DPO or AI4Chem/ChemLLM-7B-Chat-1.5-SFT.

✨ Features

• Latest Versions: ChemLLM-1.5 is released with two versions available: AI4Chem/ChemLLM-7B-Chat-1.5-DPO and AI4Chem/ChemLLM-7B-Chat-1.5-SFT.
• HuggingFace Feature: Featured by HuggingFace on the “Daily Papers” page.
• ArXiv Preprint: The arXiv preprint ChemLLM: A Chemical Large Language Model is released.

📦 Installation

Install transformers using the following command:

pip install transformers

💻 Usage Examples

Basic Usage

You can try the online demo instantly. Or, load ChemLLM-7B-Chat and run the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch

model_name_or_id = "AI4Chem/ChemLLM-7B-Chat"

model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.float16, device_map="auto",trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_id,trust_remote_code=True)

prompt = "What is Molecule of Ibuprofen?"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

generation_config = GenerationConfig(
    do_sample=True,
    top_k=1,
    temperature=0.9,
    max_new_tokens=500,
    repetition_penalty=1.5,
    pad_token_id=tokenizer.eos_token_id
)

outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Advanced Usage

You can use the same Dialogue Templates and System Prompt from Agent Chepybara to get a better response in local inference.

Dialogue Templates

For queries in ShareGPT format like:

{'instruction': "...", "prompt": "...", "answer": "...", "history": [[q1, a1], [q2, a2]]}

You can format it into this InternLM2 Dialogue format like:

def InternLM2_format(instruction, prompt, answer, history):
    prefix_template = [
        "<|im_start|>system\n",
        "{}",
        "<|im_end|>\n"
    ]
    prompt_template = [
        "<|im_start|>user\n",
        "{}",
        "<|im_end|>\n"
        "<|im_start|>assistant\n",
        "{}",
        "<|im_end|>\n"
    ]
    system = f'{prefix_template[0]}{prefix_template[1].format(instruction)}{prefix_template[2]}'
    history = "".join([f'{prompt_template[0]}{prompt_template[1].format(qa[0])}{prompt_template[2]}{prompt_template[3]}{prompt_template[4].format(qa[1])}{prompt_template[5]}' for qa in history])
    prompt = f'{prompt_template[0]}{prompt_template[1].format(prompt)}{prompt_template[2]}{prompt_template[3]}'
    return f"{system}{history}{prompt}"

System Prompt Example

- Chepybara is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be Professional, Sophisticated, and Chemical-centric. 
- For uncertain notions and data, Chepybara always assumes it with theoretical prediction and notices users then.
- Chepybara can accept SMILES (Simplified Molecular Input Line Entry System) string, and prefer output IUPAC names (International Union of Pure and Applied Chemistry nomenclature of organic chemistry), depict reactions in SMARTS (SMILES arbitrary target specification) string. Self-Referencing Embedded Strings (SELFIES) are also accepted.
- Chepybara always solves problems and thinks in step-by-step fashion, Output begin with *Let's think step by step*.

📚 Documentation

Results

MMLU Highlights

*(OpenCompass)

Chemical Benchmark

*（Score judged by ChatGPT-4-turbo）

Professional Translation

You can try it online.

Cite this work

@misc{zhang2024chemllm,
      title={ChemLLM: A Chemical Large Language Model}, 
      author={Di Zhang and Wei Liu and Qian Tan and Jingdan Chen and Hang Yan and Yuliang Yan and Jiatong Li and Weiran Huang and Xiangyu Yue and Dongzhan Zhou and Shufei Zhang and Mao Su and Hansen Zhong and Yuqiang Li and Wanli Ouyang},
      year={2024},
      eprint={2402.06852},
      archivePrefix={arXiv},
      primaryClass={cs.AI}
}

Disclaimer

⚠️ Important Note

LLM may generate incorrect answers. Please pay attention to proofreading at your own risk.

Open Source License

The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, or other questions and collaborations, please contact support@chemllm.org.

Demo

Agent Chepybara

Contact

(AI4Physics Sciecne, Shanghai AI Lab)[support@chemllm.org]