Instructions to use NingLab/GeLLMO-P6-Mistral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NingLab/GeLLMO-P6-Mistral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NingLab/GeLLMO-P6-Mistral")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NingLab/GeLLMO-P6-Mistral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NingLab/GeLLMO-P6-Mistral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NingLab/GeLLMO-P6-Mistral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NingLab/GeLLMO-P6-Mistral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NingLab/GeLLMO-P6-Mistral
- SGLang
How to use NingLab/GeLLMO-P6-Mistral 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 "NingLab/GeLLMO-P6-Mistral" \ --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": "NingLab/GeLLMO-P6-Mistral", "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 "NingLab/GeLLMO-P6-Mistral" \ --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": "NingLab/GeLLMO-P6-Mistral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NingLab/GeLLMO-P6-Mistral with Docker Model Runner:
docker model run hf.co/NingLab/GeLLMO-P6-Mistral
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- **Repository:** https://github.com/ninglab/GeLLMO
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- **Paper:**
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## Usage
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It is the responsibility of users to validate generated molecules for safety and ethical considerations.
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We urge users to adopt best practices, including toxicity prediction pipelines,
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ethical oversight, and responsible AI usage policies, to prevent harmful applications of this model.
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/ninglab/GeLLMO
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- **Paper:** https://arxiv.org/abs/2502.13398
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## Usage
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It is the responsibility of users to validate generated molecules for safety and ethical considerations.
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We urge users to adopt best practices, including toxicity prediction pipelines,
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ethical oversight, and responsible AI usage policies, to prevent harmful applications of this model.
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## Citation
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```
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@misc{dey2025gellmo,
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title={$\mathtt{GeLLM^3O}$: Generalizing Large Language Models for Multi-property Molecule Optimization},
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author={Vishal Dey and Xiao Hu and Xia Ning},
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year={2025},
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eprint={2502.13398},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2502.13398},
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}
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```
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