许可协议：apache-2.0
任务标签：文本生成
标签：

• 化学
• 生物学
• 文本生成推理

💡 模型描述

本仓库包含一个基于分子语言SELFIES构建的大型分子生成模型。

🔍 预期用途

您可以使用该模型从头生成分子（即输入起始标记bos_token），或输入部分分子结构让模型进行补全。

🛠️ 使用方法

我们提供了两种示例，您可根据需求修改输入、生成参数等。

• 全新分子生成示例：

>>> from transformers import AutoTokenizer, LlamaForCausalLM  
>>> import torch  

>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-7b")  
>>> model = LlamaForCausalLM.from_pretrained(  
                              "zjunlp/MolGen-7b",  
                              load_in_8bit=True,  
                              torch_dtype=torch.float16,  
                              device_map="auto",  
                              )  
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
>>> sf_input = tokenizer(tokenizer.bos_token, return_tensors="pt").to(device)  

>>> molecules = model.generate(input_ids=sf_input["input_ids"],  
                              attention_mask=sf_input["attention_mask"],  
                              do_sample=True,  
                              max_new_tokens=10,  
                              top_p=0.75,  
                              top_k=30,  
                              return_dict_in_generate=False,  
                              num_return_sequences=5,  
                              )  
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]  
['[C][C][=C][C][=C][Branch2][Ring1][=Branch2][C][=Branch1]',  
'[C][N][C][C][C][Branch2][Ring2][Ring2][N][C]',  
'[C][O][C][=C][C][=C][C][Branch2][Ring1][Branch1]',  
'[C][N][C][C][C@H1][Branch2][Ring1][Branch2][N][Branch1]',  
'[C][=C][C][Branch2][Ring1][#C][C][=Branch1][C][=O]']  

• 分子补全示例：

>>> from transformers import AutoTokenizer, LlamaForCausalLM  
>>> import torch  

>>> tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-7b")  
>>> model = LlamaForCausalLM.from_pretrained(  
                              "zjunlp/MolGen-7b",  
                              load_in_8bit=True,  
                              torch_dtype=torch.float16,  
                              device_map="auto",  
                              )  
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")  
>>> sf_input = tokenizer("[C][N][O]", return_tensors="pt").to(device)  

>>> molecules = model.generate(input_ids=sf_input["input_ids"],  
                              attention_mask=sf_input["attention_mask"],  
                              do_sample=True,  
                              max_new_tokens=10,  
                              top_p=0.75,  
                              top_k=30,  
                              return_dict_in_generate=False,  
                              num_return_sequences=5,  
                              )  
>>> sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]  
['[C][N][O][C][=Branch1][C][=O][/C][Ring1][=Branch1][=C][/C][=C]',  
'[C][N][O][/C][=Branch1][#Branch1][=C][/N][Branch1][C][C][C][C]',  
'[C][N][O][/C][=C][/C][=C][C][=Branch1][C][=O][C][=C]',  
'[C][N][O][C][=Branch1][C][=O][N][Branch1][C][C][C][=Branch1]',  
'[C][N][O][Ring1][Branch1][C][C][C][C][C][C][C][C]']  

📚 引用

若使用本仓库，请引用：

@inproceedings{fang2023domain,  
  author       = {Yin Fang and  
                  Ningyu Zhang and  
                  Zhuo Chen and  
                  Xiaohui Fan and  
                  Huajun Chen},  
  title        = {Domain-Agnostic Molecular Generation with Chemical Feedback},  
  booktitle    = {{ICLR}},  
  publisher    = {OpenReview.net},  
  year         = {2024},  
  url          = {https://openreview.net/pdf?id=9rPyHyjfwP}  
}