from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch

model_name_or_id = "AI4Chem/CHEMLLM-2b-1_5"

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))

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}"

- 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*."

@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}
}