Instructions to use Babajaan/KAU-BioMedLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", 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
```

KAU-BioMedLLM / README.md

Babajaan

Upload KAUBioMED-LLM LoRA adapter and manuscript evidence package

dc0b8ee verified about 1 month ago

2.53 kB

license: llama3.1
base_model:
  - meta-llama/Llama-3.1-8B-Instruct
  - Qwen/Qwen2.5-1.5B-Instruct
tags:
  - biomedical
  - genomics
  - variant-interpretation
  - lora
  - research
library_name: transformers
pipeline_tag: text-generation

KAUBioMED-LLM

KAUBioMED-LLM is a research prototype for guarded biomedical variant interpretation plus leakage-aware temporal variant scoring.

Components

Qwen2.5-1.5B-Instruct v0.1: lightweight prototype report generator.
Llama-3.1-8B-Instruct v0.2: stronger guarded report-generation baseline.
score_model: scikit-learn HistGradientBoostingClassifier with isotonic calibration, using label-free broad variant features.

Intended Use

Research use only. Not for clinical diagnosis, treatment decisions, or patient management.

Data and Provenance

See reports/DATA_PROVENANCE.md.

Key Results

First-appearance temporal broad score_model test:

AUROC: 0.99125
AUPRC: 0.96938
Brier: 0.02903
ECE 10-bin: 0.03428

Missense-only limitation:

score_model v0 missense AUROC: 0.78497
missense v1 protein-feature AUROC: 0.83644
AlphaMissense same-row AUROC: 0.97214
REVEL same-row AUROC: 0.97832

Known Limitations

Broad AUROC is driven partly by easy consequence classes; see reports/score_model/per_consequence_breakdown.md.
Missense performance remains below REVEL and AlphaMissense.
Evidence panel is limited to 20 genes and is not genome-wide.
Calibration is measured on a first-appearance ClinVar temporal split, not prospective clinical data.
Not validated for clinical deployment.

Base Model Attribution and License Notes

Llama-derived artifacts are subject to the Meta Llama 3.1 Community License and Acceptable Use Policy. Qwen-derived prototype artifacts are subject to Qwen2.5 license terms. Verify license terms before public release.

Reproducibility Commands

cd /ddn/data/generic/bbabajan/KAUBioMED_LLM
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/build_label_free_features.py
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/train_label_free_score_model.py
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/evaluate_first_appearance_temporal.py
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/compare_external_predictors_first_appearance.py
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/kaubiomed_full_report.py
/ddn/data/generic/bbabajan/envs/kaubiomed-llm/bin/python pipeline/apply_report_guards.py