---
## Overview
**MadriMed 1.2** is the second compact multimodal medical vision-language model by Madrisight. Building upon [madriMed-VL-2B](https://huggingface.co/madrisight/madriMed-VL-2B), MadriMed 1.2 delivers significant performance leaps across medical image understanding (Radiology, Pathology, CT, MRI, X-Ray) and medical reasoning.
It Delivers robust clinical reasoning capabilities, drastically reducing hallucinations and diagnostic contradictions while setting a new state-of-the-art benchmark for 2B medical vision models.
## What's New in MadriMed 1.2?
1. **Massive Diagnostic Accuracy Gains**:
- Massive jump on VQA-RAD overall score and most of the other evalution benchmarks.
2. **Resistant to Generation Hallucinations**:
- Substantially reduced token corruption, repetition and positive-bias tendencies observed in version one.
3. **Structured Formating**:
- Trained with graduated correctness scoring and reasoning-integrity penalties to enforce structured.
---
## Quick Start
### Installation
```bash
pip install torch torchvision transformers
```
### Input
```python
prompt = """Question: Examine the mammogram image shown above. Which of the following findings is most evident?
Answer Choices:
A. Well-circumscribed round mass with benign features
B. Clustered microcalcifications within an area of irregular density
C. Fat-containing lesion consistent with lipoma
D. Diffuse bilateral breast edema
Follow this exact step-by-step reasoning structure.
### 1. Key Clinical Findings
Extract the patient's age, gender, main symptoms, duration, objective physical exam findings, and abnormal laboratory/imaging results from the provided question and the images.
### 2. Differential Diagnosis
List 2-3 highly probable conditions based on the extracted findings. Briefly state why the current presentation fits.
### 3. Option Elimination
Analyze each provided choice. Write one sentence explaining why it is either correct or incorrect based on clinical guidelines.
### 4. Conclusion & Final Answer
State the final diagnostic/management conclusion.
### 5. Final correct choice
Provide final single correct choice.
"""
```
Input Image A
Input Image B
### Run the model directly
```python
import torch
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
from PIL import Image
MODEL_ID = "madrisight/madrimed1.2-VL-2B"
device = "mps" if torch.backends.mps.is_available() else "cpu"
model = Qwen3VLForConditionalGeneration.from_pretrained(
MODEL_ID,
device_map=device,
dtype=torch.float32,
trust_remote_code=True,
).eval()
processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
# Multi-image input
image_paths = ["images/MM-1-a.png", "images/MM-1-b.png"]
images = [Image.open(path).convert("RGB") for path in image_paths]
# Chat Message Payload
messages = [
{
"role": "system",
"content": "You are an expert clinical medical educator and USMLE board reviewer.",
},
{
"role": "user",
"content": [{"type": "image"} for _ in images] + [{"type": "text", "text": prompt}]
}
]
# Tokenize, Process, and Generate
with torch.inference_mode():
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = processor(
text=text,
images=images, # Pass the flat list of PIL Image objects
return_tensors="pt",
padding=True,
truncation=True,
).to(device)
generated_ids = model.generate(
**inputs,
max_new_tokens=1024,
do_sample=False,
)
output_text = processor.batch_decode(
generated_ids[:, inputs.input_ids.shape[1]:],
skip_special_tokens=True
)[0].strip()
```
### Output
```plaintext
### 1. Key Clinical Findings
- **Patient's age and gender**: Not explicitly mentioned, but mammograms are typically used for women.
- **Main symptoms**: Not specified, but mammograms are often performed for breast abnormalities.
- **Duration**: Not specified, but mammograms are usually performed at the time of suspicion.
- **Objective physical exam findings**: Not specified, but mammograms are used to detect abnormalities.
- **Abnormal laboratory/imaging results**: Not specified, but mammograms are used to detect breast abnormalities.
### 2. Differential Diagnosis
- **Well-circumscribed round mass with benign features**: This could be a benign lesion such as a fibroadenoma.
- **Clustered microcalcifications within an area of irregular density**: This could indicate a malignant process, such as ductal carcinoma in situ (DCIS) or invasive breast cancer.
- **Fat-containing lesion consistent with lipoma**: This is a benign lesion and could be a lipoma.
### 3. Option Elimination
- **Well-circumscribed round mass with benign features**: This is a plausible finding, but it is less likely to be the primary concern in a mammogram without additional symptoms or findings.
- **Clustered microcalcifications within an area of irregular density**: This is a highly suspicious finding for malignancy, as it is often associated with breast cancer.
- **Fat-containing lesion consistent with lipoma**: This is a benign finding and less likely to be the primary concern in a mammogram without additional symptoms or findings.
### 4. Conclusion & Final Answer
The mammogram findings are most indicative of a malignant process, specifically breast cancer, due to the presence of clustered microcalcifications within an area of irregular density.
### 5. Final correct choice
B. Clustered microcalcifications within an area of irregular density
```
---
## Benchmark Results
MadriMed 1.2 was rigorously evaluated across 7 medical image and text benchmarks.
### 🔬 Multimodal & Visual Question Answering
| Benchmark | MediX-R1 (2B) | Qwen3.5-2B-MedVL (OpenMed) | **MadriMed 1.2 (2B)** |
|:---|:---:| :---:| :---:|
| **VQA-RAD** | 53.9% | 55.21% | **64.30%** |
| **PathVQA** | 42.8% | 47.48% | **48.29%** |
| **SLAKE (Radiology)** | 65.4% | 68.80% | **69.36%** |
#### VQA Comparison with 4B
| Benchmark | Metric | MediX-R1 2B | Qwen3.5-2B-MedVL | MedMO-4B-Next | MedGemma 1.5 4B | MedGemma 4B | MadriMed 1.2 2B |
|---|---|---:|---:|---:|---:|---:|---:|
| **SLAKE** | Overall | 65.40% | 68.80% | 74.00% | 59.80% Tok-F1 | 72.30% Tok-F1 | 69.36% |
| ↳ Closed | Accuracy | 68.40% | N/R | 78.00% | 82.80% | 87.60% | 71.83% |
| ↳ Open | — | 57.60% | N/R | — | 59.70% Tok-F1 | — | 68.13% |
| **VQA-RAD** | Overall | 53.90% | 55.21% | 59.60% | 48.10% Tok-F1 | 49.90% Tok-F1 | 64.30% |
| ↳ Closed | Accuracy | 61.30% | N/R | 79.70% | 70.20% | 69.10% | 74.90% |
| ↳ Open | — | 45.10% | N/R | — | — | — | 51.00% |
| **PathVQA** | Overall | 42.80% | 47.48% | 73.30% | — | — | 48.29% |
| **MedXpertQA (MM+Text)** | Accuracy | — | — | — | 20.90% | 18.80% | 18.02% |
| Benchmark | Metric | MedMO-4B-Next | MedGemma (4B) | MadriMed-VL-2B (v1) | **MadriMed 1.2 (2B)** |
|:---|:---|:---:|:---:|:---:|:---:|
| **MedXpertQA (Multimodal)** | Accuracy | 27.0% | 24.43% | 21.50% | 23.50%|
| **MedXpertQA (Text)** | Accuracy | 16.5% | 14.2% | 11.02% | 13.55%|
---
### 📝 Text only Benchmarks
| Benchmark | MedGemma 1.5 (4B) | MedGemma (4B) | MediX-R1 (2B) | **MadriMed 1.2 (2B)** |
|:---|:---:|:---:|:---:|:---:|
| **PubMedQA** | 68.2% | 73.4% | 47.2% | 66.6% |
| **MedMCQA** | 55.7% | 59.8% | 49.2% | 43.1% |
| **MedQA-4opts** | 69.1% | 64.4% | 49.7% | 41.56% |
| **MedXpertQA (Text)** | | 14.2% | | 13.55% |
---
#### SLAKE Per-Category Performance
| Category | EM | Total VQA |
|-------------|--------|-----|
| Abnormality | 40.4% | 52 |
| Color | 81.8% | 33 |
| KG | 57.8% | 109 |
| Modality | 91.8% | 85 |
| Organ | 71.7% | 99 |
| Plane | 90.9% | 33 |
| Position | 61.4% | 171 |
| Quantity | 73.1% | 52 |
| Shape | 42.9% | 7 |
| Size | 69.2% | 65 |
---
#### Medmcqa (Text-Only) Per-Category Performance
##### By Choice Type
| Choice Type | Accuracy | Correct / Total |
|-------------|---------:|----------------:|
| Multi | 41.62% | 569 / 1,367 |
| Single | 43.82% | 1,234 / 2,816 |
##### By Subject
| Subject | Accuracy | Correct / Total |
|-------------------------------|---------:|----------------:|
| Anaesthesia | 41.18% | 14 / 34 |
| Anatomy | 44.02% | 103 / 234 |
| Biochemistry | 50.29% | 86 / 171 |
| Dental | 39.83% | 525 / 1,318 |
| ENT | 32.08% | 17 / 53 |
| Forensic Medicine | 37.31% | 25 / 67 |
| Gynaecology & Obstetrics | 43.30% | 97 / 224 |
| Medicine | 46.44% | 137 / 295 |
| Microbiology | 42.62% | 52 / 122 |
| Ophthalmology | 41.38% | 24 / 58 |
| Orthopaedics | 30.00% | 6 / 20 |
| Pathology | 48.96% | 165 / 337 |
| Pediatrics | 40.60% | 95 / 234 |
| Pharmacology | 44.03% | 107 / 243 |
| Physiology | 55.56% | 95 / 171 |
| Psychiatry | 37.50% | 6 / 16 |
| Radiology | 53.62% | 37 / 69 |
| Skin | 29.41% | 5 / 17 |
| Social & Preventive Medicine | 42.64% | 55 / 129 |
| Surgery | 41.19% | 152 / 369 |
---
### Evaluation NoteBook
[](https://github.com/krrish-v/all_huggingface/blob/main/model_evaluation/marimed1.2_evaluation.ipynb)
---
## Technical Specifications
| Component | Details |
|---|---|
| **Base Architecture** | `Qwen/Qwen3-VL-2B-Thinking` |
| **Context & Vision** | Native multimodal support with **Dynamic M-RoPE** |
| **Dataset** | `mint-medmax/medmax_data` and `synthetic data` |
| **Precision** | Native `float32` |
| **Training Platform** | Apple Silicon chip |
| **Training Framework** | `trl` with custom fused kernels |
| **Primary Goal** | Memory-stable multimodal RL post training and high accuracy benchmarks |
---
**Benchmark Dataset Citations**
1. **SLAKE**: [`BoKelvin/SLAKE`](https://huggingface.co/datasets/BoKelvin/SLAKE)
2. **MedXpertQA**: [`TsinghuaC3I/MedXpertQA`](https://huggingface.co/datasets/TsinghuaC3I/MedXpertQA)
3. **VQA-RAD**: [`flaviagiammarino/vqa-rad`](https://huggingface.co/datasets/flaviagiammarino/vqa-rad)
4. **PubMedQA**: [`qiaojin/PubMedQA`](https://huggingface.co/datasets/qiaojin/PubMedQA)
5. **MedQA (USMLE)**: [`GBaker/MedQA-USMLE-4-options`](https://huggingface.co/datasets/GBaker/MedQA-USMLE-4-options)
7. **MedMCQA**: [`openlifescienceai/medmcqa`](https://huggingface.co/datasets/openlifescienceai/medmcqa)
8. **Path-VQA**: [`flaviagiammarino/path-vqa`](https://huggingface.co/datasets/flaviagiammarino/path-vqa)
---
## Limitations & Safety Guidelines
> **Research & Educational Use Only**
MadriMed 1.2 is an **experimental research model**. It is not certified for autonomous clinical diagnostic decision-making.
### No Treatment Prescriptions
The model should **not** be used as an authoritative source for:
- Pharmaceutical dosages
- Surgical interventions
- Direct treatment pathways
- Other high-stakes clinical decisions
All model-generated interpretations, medical reasoning, and visual inferences should be **reviewed and independently confirmed by a qualified, licensed medical professional** before being used in a clinical context.
---
## Citation
```bibtex
@software{madrimed1.2-VL-2B,
title = {MadriMed 1.2: Advanced Multimodal Medical Reasoning at the 2B Scale},
author = {Madrisight},
year = {2026},
url = {https://huggingface.co/madrisight/madrimed1.2-VL-2B}
}
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