Instructions to use zjunlp/MolGen-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zjunlp/MolGen-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zjunlp/MolGen-7b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zjunlp/MolGen-7b") model = AutoModelForCausalLM.from_pretrained("zjunlp/MolGen-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use zjunlp/MolGen-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zjunlp/MolGen-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zjunlp/MolGen-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zjunlp/MolGen-7b
- SGLang
How to use zjunlp/MolGen-7b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "zjunlp/MolGen-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zjunlp/MolGen-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "zjunlp/MolGen-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zjunlp/MolGen-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zjunlp/MolGen-7b with Docker Model Runner:
docker model run hf.co/zjunlp/MolGen-7b
Yin Fang commited on
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Parent(s): 590691c
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README.md
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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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'[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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@misc{molgen-7b,
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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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'[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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@misc{molgen-7b,
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