132 lines
4.7 KiB
Markdown
132 lines
4.7 KiB
Markdown
# ChemStructLM
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ChemStructLM是一个旨在整合MolScribe和Vary库，让大语言模型能够理解、设计和改造化学结构、分子和蛋白质的项目。该项目通过在大语言模型中融合这两个强大的化学信息处理库，旨在推动化学和生物信息学领域的研究和应用。
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## 特性
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整合了MolScribe和Vary库的功能。
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提供了一个示例环境，用于同时使用这两个库。
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包含了示例代码，展示如何将这两个库融合使用。
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开源，鼓励社区贡献。
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快速开始
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先决条件
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确保你有一个可用的Conda环境。如果没有，可以访问Miniconda或Anaconda官方网站了解如何安装。推荐使用micromamba作为包管理器。
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## 安装
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克隆ChemStructLM仓库到你的本地机器：
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```shell
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git clone https://github.com/hotwa/ChemStructLM.git
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cd ChemStructLM
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```
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初始化子模块（MolScribe和Vary）：
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```shell
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git submodule update --init --recursive
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```
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检出MolScribe和Vary的特定版本：
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```shell
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git submodule add https://github.com/thomas0809/MolScribe molscribe
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git submodule add https://github.com/Ucas-HaoranWei/Vary-toy vary-toy
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git submodule add https://github.com/h-zhao1997/cobra cobra
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cd molscribe
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git rev-parse HEAD
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git checkout 97acee57d10bd719f4dc1cfd30d09f142b7dc65f
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cd ../vary-toy
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git rev-parse HEAD
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git checkout e94e50f4b10c7b0f2a29e4d8b3804a35024b0565
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cd ..
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cd cobra
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git rev-parse HEAD
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git checkout 365a24d360f9c0d1ed4db96acc3a76b12782d138
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```
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创建并激活Conda环境：
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```shell
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micromamba env create -f environment.yml
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micromamba run -n ChemStructLM python -m pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple/
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# install flash-attention
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micromamba run -n ChemStructLM python -m pip install flash-attn --no-build-isolation
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# install vary
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cd vary/Vary-master
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# change pth path in source code
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# `vary/Vary-master/vary/demo/run_qwen_vary.py` and `vary/Vary-master/vary/model/vary_qwen_vary.py` in line `/cache/vit-large-patch14/`
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# download from https://huggingface.co/openai/clip-vit-large-patch14/tree/main
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sed -i 's|/cache/vit-large-patch14/|/media/lingyuzeng/c617029b-8496-684d-b402-a18f08e75ef13/project/Vary-toy/clip-vit-large-patch14/|g' vary/Vary-master/vary/demo/run_qwen_vary.py
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sed -i 's|/cache/vit-large-patch14/|/media/lingyuzeng/c617029b-8496-684d-b402-a18f08e75ef13/project/Vary-toy/clip-vit-large-patch14/|g' vary/Vary-master/vary/model/vary_qwen_vary.py
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sed -i 's/self.seq_length = config.seq_length/self.seq_length = config.max_length/' vary-toy/Vary-master/vary/model/llm/qwen/modeling_qwen.py
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micromamba run -n ChemStructLM python -m pip install e .
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# install molscribe
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cd ../../molscribe
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micromamba run -n ChemStructLM python -m pip install -r requirements.txt -i https://pypi.mirrors.ustc.edu.cn/simple/
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sed -i 's/timm==0.4.12/timm==0.6.13/' setup.py
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micromamba run -n ChemStructLM python setup.py install
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cd ..
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micromamba activate ChemStructLM
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# test
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python vary/Vary-master/vary/demo/run_qwen_vary.py --model-name /media/lingyuzeng/c617029b-8496-684d-b402-a18f08e75ef13/project/Vary-toy/clip-vit-large-patch14/ --image-file /mnt/wtrr1/deepinproject/chemstructlm/vary/assets/vary.png
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```
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test:
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```shell
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micromamba create -n test python=3.10 -y
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cd vary-toy/Vary-master
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micromamba run -n test python -m pip install e . -i https://pypi.mirrors.ustc.edu.cn/simple/
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cd ../../molscribe
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micromamba run -n test python setup.py install
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cd ..
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micromamba activate test
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```
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MolScribe test:
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```shell
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import torch
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from molscribe import MolScribe
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# from huggingface_hub import hf_hub_download
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# ckpt_path = hf_hub_download('yujieq/MolScribe', 'swin_base_char_aux_1m.pth')
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ckpt_path = '/mnt/wtrr1/deepinproject/MolScribe/ckpts/swin_base_char_aux_1m680k.pth'
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model = MolScribe(ckpt_path, device=torch.device('cpu'))
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output = model.predict_image_file('molscribe/assets/example.png', return_atoms_bonds=True, return_confidence=True)
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```
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[Cobra: Extending Mamba to Multi-modal Large Language Model for Efficient Inference](https://github.com/h-zhao1997/cobra)
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安装：
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```shell
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cd cobra
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pip install -e .
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# install mamba and other packages
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pip install packaging ninja
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pip install mamba-ssm
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pip install causal-conv1d
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# Verify Ninja --> should return exit code "0"
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ninja --version; echo $?
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```
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## 目录结构
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```shell
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YourProjectName/
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│
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├── src/ # 项目源代码
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│ ├── integration/ # 融合MolScribe和Vary的代码
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│ └── utils/ # 工具和辅助功能
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├── tests/ # 测试脚本
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├── vary/ # Vary项目作为子模块
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├── molscribe/ # MolScribe项目作为子模块
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├── environments/ # 环境配置文件，如conda环境文件
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├── README.md # 项目说明文件
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└── .gitmodules # git子模块配置文件
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``` |