93 lines
3.8 KiB
Markdown
93 lines
3.8 KiB
Markdown
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---
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- chemistry
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- biology
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- text-generation-inference
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---
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## 💡 Model description
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This repo contains a large molecular generative model built with molecular language SELFIES.
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## 🔍 Intended uses
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You can use the model to generate molecules from scratch (i.e., inputting the bos_token), or input a partial structure for the model to complete.
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## 🛠️ How to use
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We have provided two types of examples. You can modify the input, generation parameters, etc., according to your needs.
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- Denovo molecule generation example:
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```python
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from modelscope import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/MolGen-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"AI-ModelScope/MolGen-7b",
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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sf_input = tokenizer(tokenizer.bos_token, return_tensors="pt").to(device)
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molecules = model.generate(input_ids=sf_input["input_ids"],
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attention_mask=sf_input["attention_mask"],
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do_sample=True,
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max_new_tokens=10,
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top_p=0.75,
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top_k=30,
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return_dict_in_generate=False,
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num_return_sequences=5,
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)
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sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
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['[C][C][=C][C][=C][Branch2][Ring1][=Branch2][C][=Branch1]',
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'[C][N][C][C][C][Branch2][Ring2][Ring2][N][C]',
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'[C][O][C][=C][C][=C][C][Branch2][Ring1][Branch1]',
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'[C][N][C][C][C@H1][Branch2][Ring1][Branch2][N][Branch1]',
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'[C][=C][C][Branch2][Ring1][#C][C][=Branch1][C][=O]']
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```
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- Molecular completion example:
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```python
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from modelscope import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("AI-ModelScope/MolGen-7b")
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model = AutoModelForCausalLM.from_pretrained(
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"AI-ModelScope/MolGen-7b",
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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sf_input = tokenizer("[C][N][O]", return_tensors="pt").to(device)
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molecules = model.generate(input_ids=sf_input["input_ids"],
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attention_mask=sf_input["attention_mask"],
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do_sample=True,
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max_new_tokens=10,
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top_p=0.75,
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top_k=30,
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return_dict_in_generate=False,
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num_return_sequences=5,
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)
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sf_output = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=True).replace(" ","") for g in molecules]
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['[C][N][O][C][=Branch1][C][=O][/C][Ring1][=Branch1][=C][/C][=C]',
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'[C][N][O][/C][=Branch1][#Branch1][=C][/N][Branch1][C][C][C][C]',
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'[C][N][O][/C][=C][/C][=C][C][=Branch1][C][=O][C][=C]',
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'[C][N][O][C][=Branch1][C][=O][N][Branch1][C][C][C][=Branch1]',
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'[C][N][O][Ring1][Branch1][C][C][C][C][C][C][C][C]']
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```
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## 📚 Citation
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If you use our repository, please cite:
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```bibtex
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@article{fang2023molecular,
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title={Molecular Language Model as Multi-task Generator},
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author={Fang, Yin and Zhang, Ningyu and Chen, Zhuo and Fan, Xiaohui and Chen, Huajun},
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journal={arXiv preprint arXiv:2301.11259},
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year={2023}
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}
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```
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