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)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Babajaan/KAU-BioMedLLM", dtype="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
| { | |
| "status": "prepared_not_pushed", | |
| "file_count": 11, | |
| "files": [ | |
| { | |
| "path": "README.md", | |
| "size": 2526 | |
| }, | |
| { | |
| "path": "INFERENCE.md", | |
| "size": 350 | |
| }, | |
| { | |
| "path": "UPLOAD_READINESS.md", | |
| "size": 3562 | |
| }, | |
| { | |
| "path": "reports/DATA_PROVENANCE.md", | |
| "size": 3512 | |
| }, | |
| { | |
| "path": "reports/manuscript/TABLES.md", | |
| "size": 5325 | |
| }, | |
| { | |
| "path": "adapter/README.md", | |
| "size": 5342 | |
| }, | |
| { | |
| "path": "adapter/adapter_model.safetensors", | |
| "size": 167832240 | |
| }, | |
| { | |
| "path": "adapter/tokenizer_config.json", | |
| "size": 352 | |
| }, | |
| { | |
| "path": "adapter/chat_template.jinja", | |
| "size": 4614 | |
| }, | |
| { | |
| "path": "adapter/tokenizer.json", | |
| "size": 17210019 | |
| }, | |
| { | |
| "path": "adapter/adapter_config.json", | |
| "size": 1173 | |
| } | |
| ], | |
| "base_weights_excluded": true, | |
| "base_weight_hits": [], | |
| "secret_token_hits": [], | |
| "checkpoint_optimizer_files_excluded": true, | |
| "human_push_commands": [ | |
| "cd /ddn/data/generic/bbabajan/KAUBioMED_LLM/release/hf_upload_kaubiomed", | |
| "huggingface-cli login", | |
| "huggingface-cli upload <YOUR_REPO_ID> . . --repo-type model" | |
| ], | |
| "human_checklist": [ | |
| "Review README.md disclaimer/license/base attribution", | |
| "Confirm adapter-only upload", | |
| "Confirm repository visibility", | |
| "Do not upload base Llama/Qwen weights" | |
| ] | |
| } |