---
license: apache-2.0
base_model:
- Qwen/Qwen3-VL-4B-Instruct
- Qwen/Qwen3-VL-8B-Instruct
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- medical
- vision-language
- reinforcement-learning
- qwen3-vl
datasets:
- MBZUAI/medix-rl-data
---

# MediX-R1 (reproduction), 4B and 8B

Two Qwen3-VL checkpoints trained with reinforcement learning following the MediX-R1 recipe. They are our reproduction of
MediX-R1, the "MediX-R1-4B" and "MediX-R1-8B" rows of the main results table in our paper. Each model is in its own folder.

| Folder | Base model | VQA | QA | Macro | Micro |
|---|---|---|---|---|---|
| [MediX-R1-4B](MediX-R1-4B) | Qwen/Qwen3-VL-4B-Instruct | 49.2 | 70.5 | 63.0 | 53.8 |
| [MediX-R1-8B](MediX-R1-8B) | Qwen/Qwen3-VL-8B-Instruct | 49.3 | 74.4 | 65.5 | 56.0 |

Accuracy (%) over 6 medical VQA and 11 medical text QA benchmarks, greedy decoding, answers scored by three LLM judges.
The README in each folder has the training recipe, the prompt format and the per-benchmark results.

## Usage

```python
import torch
from huggingface_hub import snapshot_download
from transformers import AutoProcessor, Qwen3VLForConditionalGeneration

name = "MediX-R1-8B"   # or "MediX-R1-4B"
path = snapshot_download("Shayan0179/Medix-R1", allow_patterns=f"{name}/*") + f"/{name}"
model = Qwen3VLForConditionalGeneration.from_pretrained(path, dtype=torch.bfloat16, device_map="auto")
processor = AutoProcessor.from_pretrained(path)

PROMPT = open(f"{path}/prompt.txt").read()   # the training instructions, ending with "Question: \n"
messages = [{"role": "user", "content": [
    {"type": "text", "text": PROMPT},
    {"type": "image", "image": "chest_xray.png"},
    {"type": "text", "text": "Is there a pleural effusion?"},
]}]
inputs = processor.apply_chat_template(messages, tokenize=True, add_generation_prompt=True,
                                       return_dict=True, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
print(processor.batch_decode(out[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True)[0])
```

The model answers with a modality tag, then its reasoning in `<thinking>...</thinking>`, then the final answer in
`<answer>...</answer>`.

## License

Apache 2.0, following the base models.