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build-small-hackathon/blood-test-minicpmv-4_6-medreason

sourceHugging Faceapache-2.0updated 4mo agoView on Hugging Face

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Model Card

Blood Test Explainer — MiniCPM-V 4.6 (medical-reasoning fine-tune)

A ~1.3B vision-language model that reads a photo or PDF of a blood test and extracts the markers, values, units, reference ranges and high/low status as structured JSON, fully offline. It powers the Blood Test Explainer Space, built for the Build Small hackathon by Roman and Dimitris (American College of Greece / Deree AI Lab).

What it is

This is openbmb/MiniCPM-V-4.6 fine-tuned with a LoRA that was merged back into the base, so it is a single standalone model with no adapter to load. The fine-tune did not touch the extraction task directly. Instead, we froze the vision encoder and trained only the language layers on a general medical-reasoning dataset, and that made the model a better lab-report reader.

How it was trained

—Method: LoRA on the language layers (vision encoder frozen), then merged into the base.
—Data: FreedomIntelligence/medical-o1-reasoning-SFT (general medical reasoning, text only — no extraction examples).
—Why: fine-tuning on our own extraction schema caused catastrophic forgetting and collapsed accuracy. Teaching the model general medical knowledge improved extraction instead.
—Infra: ms-swift LoRA on Modal (A100). A small amount of reasoning data worked best.

Results

Field-level marker extraction on hand-labeled real reports:

Model	Marker F1	Recall	Precision
Base MiniCPM-V 4.6	0.655	0.529	0.857
This model	0.746	0.647	0.880

(Small evaluation set, so treat the numbers as directional.)

Intended use

Educational extraction and explanation of routine blood-test results, running locally / in-Space. The app pairs this model with a curated medical knowledge base so explanations are grounded and not hallucinated.

How to use

python

from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "build-small-hackathon/blood-test-minicpmv-4_6-medreason"
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(model_id, device_map="auto").eval()
# Prompt the model with the lab-report image and ask for the markers as JSON.

The full extraction prompt and pipeline are in the app repository.

Limitations and safety

This is an educational tool, not a diagnosis. It can misread values, especially on noisy scans, and the evaluation set is small. It is meant to help someone understand their results and ask better questions of a clinician, not to replace one. License follows the base model, openbmb/MiniCPM-V-4.6.