Instructions to use s0close/medgemma-dsa-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s0close/medgemma-dsa-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="s0close/medgemma-dsa-lora")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s0close/medgemma-dsa-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use s0close/medgemma-dsa-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s0close/medgemma-dsa-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "s0close/medgemma-dsa-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/s0close/medgemma-dsa-lora

SGLang

How to use s0close/medgemma-dsa-lora 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 "s0close/medgemma-dsa-lora" \
    --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": "s0close/medgemma-dsa-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "s0close/medgemma-dsa-lora" \
        --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": "s0close/medgemma-dsa-lora",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use s0close/medgemma-dsa-lora with Docker Model Runner:
```
docker model run hf.co/s0close/medgemma-dsa-lora
```

Model Card: MedGemma DSA LoRA Adapter (`ds-lora`)

Model Details

Model Description

This repository contains a LoRA adapter fine-tuned for Digital Subtraction Angiography (DSA) vessel patency triage.

The adapter is intended to improve sensitivity for blocked/occluded vessel patterns compared with naive base-model prompting on heavily imbalanced data.

Developed by: DataRabbit hackathon team
Base model: google/medgemma-1.5-4b-it
Model type: Vision-language LoRA adapter (PEFT)
Primary task: Binary DSA support classification (patent vs blocked)
Language: English (prompts/output)
License: Follows base model terms and license restrictions
Finetuned from model: google/medgemma-1.5-4b-it

Model Sources

Project repository: local project (proj-medgemma-challenge-2026)
Base model: https://huggingface.co/google/medgemma-1.5-4b-it
Competition context: https://www.kaggle.com/competitions/med-gemma-impact-challenge/overview

Intended Uses

Direct Use

Rapid DSA image triage support in a controlled evaluation pipeline.
Experimental decision-support where output is always reviewed by a clinician.

Downstream Use

Can be routed only for DSA/XA series in a multi-stage pipeline (e.g., PACS worker).
Can be merged into a standalone model for simpler deployment.

Out-of-Scope Use

Not for autonomous diagnosis.
Not for emergency triage without human oversight.
Not validated for non-DSA modalities (CT, MR, US, X-ray).
Not intended for pediatric/special populations without dedicated validation.

Bias, Risks, and Limitations

Performance can degrade under domain shift (scanner/protocol/institution changes).
Small and previously imbalanced blocked-vessel class is a key risk.
Model outputs can be verbose or overconfident; structured post-processing is recommended.
False negatives remain clinically high-risk.

Clinical Safety Recommendations

Use as decision support only, never as final diagnosis.
Always combine with human radiology review.
Route uncertain/low-confidence outputs to manual review.
Monitor sensitivity/specificity continuously on newly ingested data.

How to Use

Load base model + LoRA adapter

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from peft import PeftModel

base_model_id = "google/medgemma-1.5-4b-it"
adapter_id = "s0close/medgemma-dsa-lora"

processor = AutoProcessor.from_pretrained(base_model_id)
base_model = AutoModelForImageTextToText.from_pretrained(
    base_model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
model = PeftModel.from_pretrained(base_model, adapter_id)
model.eval()

Optional: merge adapter for standalone inference

Use: tools/publish_dsa_merged_model.py

Training Details

Training Data

Internal DSA dataset prepared for vessel patency classification.
Conversational VLM format (messages with image + text supervision).
De-identified data expected; no PHI should be stored in the model artifacts.

Training Procedure

QLoRA fine-tuning on Hugging Face Jobs.
Quantization: 4-bit NF4 with double quantization.
Compute dtype: bfloat16.

Hyperparameters (default project config)

LoRA rank (r): 8
LoRA alpha: 16
LoRA dropout: 0.1
Target modules: all-linear
Learning rate: 2e-5 (script default)
Batch size: 1
Gradient accumulation: 8
Epochs: 2 (script default)

Evaluation

Evaluation Setup

Primary metrics: accuracy, sensitivity (blocked recall), specificity.
Internal prior baseline showed a strong class-imbalance failure mode:
- very high accuracy with near-zero blocked sensitivity when predicting majority class.

Current Results

Run-specific results should be reported from the latest training_run.json and evaluation outputs in your HF run artifacts.
Do not claim clinical-grade performance without external multi-site validation.

Technical Specifications

Architecture

Base: MedGemma 1.5 4B instruction-tuned vision-language model.
Adaptation: PEFT LoRA heads trained for DSA classification prompts.

Infrastructure

Typical training flavor: Hugging Face Jobs on L4.
Inference: CUDA/CPU depending on deployment constraints.

Ethical and Regulatory Notice

This model is a research artifact for hackathon/prototyping workflows. It is not a medical device and has not undergone regulatory approval. Any clinical use requires formal validation, governance, and compliance review.

Contact

For project-level questions, use your team repository issue tracker or designated team contact.

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Model Card: MedGemma DSA LoRA Adapter (ds-lora)