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

Expand KAU-BioMedLLM model card and HPC acknowledgement

f9c777e verified about 1 month ago

11.3 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
  - clinical-genomics
  - bioinformatics
  - research
library_name: transformers
pipeline_tag: text-generation

Model Card for KAU-BioMedLLM

KAU-BioMedLLM is a research prototype for source-grounded biomedical variant interpretation. The current public release contains the LoRA adapter and documentation for a guarded report-generation system built around biomedical evidence retrieval, label-free variant scoring, citation enforcement, and abstention when evidence is insufficient.

This repository does not redistribute the full Meta Llama-3.1-8B-Instruct base model. Users must separately have access to the base model according to the Meta Llama 3.1 license terms.

Model Details
Model Description
System Overview
Uses
Out-of-Scope Use
Training and Development Data
Training Procedure
Evaluation
Limitations
How to Load the Adapter
Compute Infrastructure
Ethical and Safety Considerations
Citation and Acknowledgement

Model Details

Model name: KAU-BioMedLLM
Repository: Babajaan/KAU-BioMedLLM
Developed by: Babajan B, King Abdulaziz University research environment
Model type: Biomedical instruction-tuned LoRA adapter for report generation
Primary base model: meta-llama/Llama-3.1-8B-Instruct
Prototype baseline: Qwen/Qwen2.5-1.5B-Instruct
Language: English
Domain: Biomedical variant interpretation, clinical genomics research, bioinformatics evidence synthesis
Release type: Research-use adapter release
Clinical status: Not clinically validated; not for diagnosis, treatment, or patient-management decisions

Model Description

KAU-BioMedLLM was developed as part of a broader biomedical AI project to generate structured, source-grounded variant interpretation reports. The system combines two deliberately separated components:

Independent score model: a label-free machine learning model for variant scoring and temporal benchmarking.
LLM report generator: a LoRA-adapted instruction model for structured biomedical explanation.

The central design choice is to avoid treating LLM-generated prose as a calibrated pathogenicity score. The numeric score and the narrative interpretation are separated, and the report-generation layer is guarded by schema validation, citation checks, and abstention rules.

System Overview

The broader KAU-BioMedLLM workflow uses:

ClinVar current and archived snapshots for variant labels and temporal validation.
PubMed and biomedical literature evidence for gene and phenotype context.
UniProt for protein/gene functional evidence.
AlphaFold structure information for residue-level structural context where mapping is reliable.
Ensembl/VEP-style transcript and protein residue mapping.
A guarded report pipeline that enforces JSON structure, citation support, and abstention when evidence is missing or ambiguous.

The uploaded model artifact is the LoRA adapter used for the report-generation component. The repository also includes data provenance notes, evaluation summaries, manuscript tables, and inference instructions.

Uses

Intended Use

KAU-BioMedLLM is intended for research and development in:

biomedical variant interpretation workflows;
genomics and bioinformatics report prototyping;
source-grounded biomedical evidence synthesis;
evaluating guarded LLM reporting pipelines;
studying leakage-aware score model design and temporal validation.

Direct Use

The adapter can be loaded with meta-llama/Llama-3.1-8B-Instruct using PEFT. It can generate structured research reports when provided with variant and evidence context.

Downstream Use

The model may be used as a starting point for research on:

citation-grounded biomedical report generation;
variant prioritization interfaces;
guarded medical LLM output;
separation of statistical score models from narrative explanation;
biomedical uncertainty and abstention behavior.

Out-of-Scope Use

KAU-BioMedLLM must not be used as:

a clinical diagnostic system;
a substitute for expert variant curation;
a medical advice generator;
a system for patient-management or treatment decisions;
a standalone pathogenicity classifier for clinical reporting;
a validated replacement for ACMG/AMP interpretation workflows.

Training and Development Data

The project used biomedical and genomics resources including:

ClinVar current and archived variant snapshots;
PubMed/literature-derived evidence examples;
UniProt protein/gene evidence;
AlphaFold structural evidence for selected proteins;
Ensembl/VEP-style transcript and residue mapping outputs;
internally generated structured instruction examples for report generation.

The score model was intentionally designed around label-free features and excludes direct ClinVar significance text, review-star strength, gene identity, and external predictor scores such as REVEL, AlphaMissense, and CADD as training inputs.

See reports/DATA_PROVENANCE.md for project-level data provenance and known version-recording limitations.

Training Procedure

The report-generation model was trained as a LoRA adapter rather than as a full base-model retraining run.

The project developed two model stages:

Qwen2.5-1.5B-Instruct v0.1: lightweight prototype used to validate the basic structured-output and guarded-report pipeline.
Llama-3.1-8B-Instruct v0.2: stronger report-generation baseline selected for improved biomedical reasoning capacity and structured report generation.

The LoRA approach keeps the released artifact compact and avoids redistributing the full base model weights.

Evaluation

The project separates score model evaluation from LLM report-generation evaluation.

Broad First-Appearance Temporal Score Model

The broad score model was evaluated using a first-appearance ClinVar temporal split:

Training snapshot: ClinVar 2023-12-26
Test snapshot: ClinVar 2025-12-21
Test set: labeled variants appearing in the 2025 snapshot and absent from the 2023 snapshot

Reported benchmark:

Metric	Value
Test variants	509,974
AUROC	0.99125
AUPRC	0.96938
Brier score	0.02903
ECE	0.03428

These metrics apply to the broad score model, not to the LLM text generator alone.

Missense-Specific Limitation

Missense-only prediction remained substantially harder:

Model / Predictor	Missense AUROC
score_model v0	0.78497
missense v1 protein-feature score_model	0.83644
AlphaMissense same-row comparison	0.97214
REVEL same-row comparison	0.97832

This limitation is important: KAU-BioMedLLM should not be claimed as state-of-the-art for missense pathogenicity prediction.

Report Guard Evaluation

The guarded report layer was evaluated on a small report set:

Citation guard pass: 5/5 reports
Citation guard fail: 0/5 reports
Structure mapping available: 1/5 reports
Structure mapping abstained: 4/5 reports

The abstention behavior is intentional: when evidence is missing, ambiguous, or not reliably mapped, the system should state uncertainty rather than fabricate confidence.

Limitations

The current evidence panel is limited and is not a genome-wide production knowledge base.
Missense prediction remains below specialist predictors such as REVEL and AlphaMissense.
Broad score model performance is partly driven by easier consequence classes.
Structure evidence is only used when transcript/protein/residue mapping can be reconciled.
Exact historical versions for some external resources require final manual verification before manuscript submission.
The model has not been prospectively validated in clinical settings.
Generated text can still contain errors and must be reviewed by domain experts.
The uploaded artifact is an adapter, not a standalone full model.

How to Load the Adapter

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base_model = "meta-llama/Llama-3.1-8B-Instruct"
adapter_model = "Babajaan/KAU-BioMedLLM"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_model)
model.eval()

messages = [
    {
        "role": "user",
        "content": (
            "For research use only, generate a structured biomedical variant "
            "interpretation report for BRCA1 c.5096G>A p.Arg1699Gln. "
            "Include uncertainty and do not provide clinical advice."
        ),
    }
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=700,
        temperature=0.2,
        do_sample=True,
    )

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Compute Infrastructure

The project was developed and evaluated using the King Abdulaziz University Aziz High Performance Computing environment. The workflow used the Aziz HPC software environment, PBS job scheduling, and GPU resources including A100 GPU jobs where available.

KAU Aziz HPC support resources:

Aziz Supercomputer Support Center: https://hpcc-kau.com/support/
PBS and GPU job support documentation are available through the Aziz HPC knowledge base.

Ethical and Safety Considerations

Biomedical LLM systems can produce fluent but incorrect explanations. KAU-BioMedLLM therefore uses a guard-first design:

numeric score and LLM narrative are separated;
citations are required for biomedical claims;
reports can abstain when evidence is insufficient;
outputs are research-use only;
no clinical decision should be made from model output.

Users should treat all outputs as draft research interpretations requiring expert review.

Citation and Acknowledgement

If you use this model or the associated code/reports, please cite the forthcoming project manuscript or repository when available.

Suggested acknowledgement:

This work used computational resources from the King Abdulaziz University Aziz High Performance Computing environment.

License and Base Model Notes

This repository contains a LoRA adapter and project documentation. It does not redistribute the full Llama base model. Use of the adapter with meta-llama/Llama-3.1-8B-Instruct is subject to the Meta Llama 3.1 Community License and Acceptable Use Policy. Qwen-derived prototype artifacts are subject to Qwen license terms. Users are responsible for verifying license compatibility for their own use case.

Babajaan
/

KAU-BioMedLLM