Text Generation
Transformers
Safetensors
biomedical
genomics
variant-interpretation
lora
clinical-genomics
bioinformatics
research
conversational
Instructions to use Babajaan/KAU-BioMedLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Babajaan/KAU-BioMedLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Babajaan/KAU-BioMedLLM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Babajaan/KAU-BioMedLLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Babajaan/KAU-BioMedLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Babajaan/KAU-BioMedLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Babajaan/KAU-BioMedLLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Babajaan/KAU-BioMedLLM
- SGLang
How to use Babajaan/KAU-BioMedLLM 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 "Babajaan/KAU-BioMedLLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Babajaan/KAU-BioMedLLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Babajaan/KAU-BioMedLLM" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Babajaan/KAU-BioMedLLM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Babajaan/KAU-BioMedLLM with Docker Model Runner:
docker model run hf.co/Babajaan/KAU-BioMedLLM
Commit History
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Add policy-constrained agentic verification benchmark
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Resolve resource versions (UniProt 2026_02, AlphaFold v6, Ensembl 116) + Llama-3.1 license compliance ('Built with Llama')
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Correct overclaims: score-field semantics, n=5 pilot labeling, 20-gene panel (no RAG), fresh-variant structure abstention
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Update model card for Llama root and Qwen subfolder adapters
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Add Qwen v0.1 adapter index
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Add Qwen2.5-1.5B v0.1 adapter under qwen_v0_1
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Add Qwen2.5-1.5B v0.1 adapter under qwen_v0_1
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Add Qwen2.5-1.5B v0.1 adapter under qwen_v0_1
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Add Qwen2.5-1.5B v0.1 adapter under qwen_v0_1
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Add Qwen2.5-1.5B v0.1 adapter under qwen_v0_1
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Restore detailed KAU-BioMedLLM model card
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Replace root adapter with Llama-3.1-8B v0.2 LoRA adapter
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Expose Llama LoRA adapter files at repository root
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Expose Llama LoRA adapter files at repository root
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Expose Llama LoRA adapter files at repository root
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Expose Llama LoRA adapter files at repository root
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Expose Llama LoRA adapter files at repository root
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Set adapter base model to public Llama 3.1 8B ID
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Fix model card metadata and add evaluation details
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Expand KAU-BioMedLLM model card and HPC acknowledgement
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Upload KAUBioMED-LLM LoRA adapter and manuscript evidence package
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initial commit
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