Instructions to use adidukrembzuai/MedChain-R1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use adidukrembzuai/MedChain-R1-9B with Transformers:

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

pipe = pipeline("image-text-to-text", model="adidukrembzuai/MedChain-R1-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("adidukrembzuai/MedChain-R1-9B")
model = AutoModelForMultimodalLM.from_pretrained("adidukrembzuai/MedChain-R1-9B", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use adidukrembzuai/MedChain-R1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "adidukrembzuai/MedChain-R1-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "adidukrembzuai/MedChain-R1-9B",
		"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/adidukrembzuai/MedChain-R1-9B

SGLang

How to use adidukrembzuai/MedChain-R1-9B 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 "adidukrembzuai/MedChain-R1-9B" \
    --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": "adidukrembzuai/MedChain-R1-9B",
		"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 "adidukrembzuai/MedChain-R1-9B" \
        --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": "adidukrembzuai/MedChain-R1-9B",
		"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 adidukrembzuai/MedChain-R1-9B with Docker Model Runner:
```
docker model run hf.co/adidukrembzuai/MedChain-R1-9B
```

MedChain-R1-9B / README.md

adidukrembzuai

Model card: state eval batch sizes

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---
license: apache-2.0
library_name: transformers
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen3.5-9B
tags:
  - medical
  - multimodal
  - grounding
  - visual-grounding
  - multi-image
  - reasoning
  - chain-of-thought
  - radiology
  - mri
  - ct
  - arxiv:2611.00041
---
# MedChain-R1-9B: Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding

[![Paper](https://img.shields.io/badge/arXiv-2611.00041-red)](https://arxiv.org/abs/2611.00041)
[![Code](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/adinathdukre/MedChain-R1)
[![Model](https://img.shields.io/badge/🤗-MedChain--R1--4B-blue)](https://huggingface.co/adidukrembzuai/MedChain-R1-4B)
[![Model](https://img.shields.io/badge/🤗-MedChain--R1--9B-blue)](https://huggingface.co/adidukrembzuai/MedChain-R1-9B)
[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

<p align="center">
  <img src="overview.png" alt="MedChain-R1 overview" width="900"/>
</p>

**MedChain-R1-9B** is a 9B vision-language model for **sequential medical image grounding**: given a short sequence of
medical images and a clinical question, it first writes an image-grounded rationale in `<think>…</think>` and then
returns one bounding box `<answer>(x1,y1),(x2,y2)</answer>` in pixel coordinates. It is built on
[Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) with **deliberative chain-of-thought supervision
(dCoT-SFT)** on the **MedChain** rationale corpus, followed by **GRPO** with an IoU + format reward.

**MedChain** contains 182,573 filtered rationales over the 8 MedSG-Bench tasks and 10 imaging modalities. A medical VLM
(FlemingVL-38B) drafts a caption-like rationale; a multimodal refiner (Qwen3.5-397B-A17B) then re-examines the images,
question and annotated box and writes a box-conditioned explanation with explicit cross-image reasoning.

---

## 🔑 Key findings (from the paper)

1. **Reasoning supervision sets a new SOTA.** dCoT-SFT outperforms MedSeq-Grounder at every scale, reaching 74.96, 76.60
   and 77.24 mIoU at 4B, 9B and 27B; the 27B model improves over the strongest prior model by 4.69 mIoU and 5.49 Acc@0.5.
2. **The gains depend on the task.** Against an answer-only 4B model, dCoT-SFT improves mIoU by 3.56 (95% CI 3.13–4.00):
   +14.7–16.1 on difference grounding and +6.5 on object tracking, but −2.2 to −3.2 on multi-view, patch and referring.
3. **Predictions depend on the reasoning context.** Forcing an immediate answer lowers mIoU by 2.9; conditioning on a
   rationale from another task lowers it by 12.2.
4. **GRPO adds little after dCoT-SFT.** +0.37 mIoU on the benchmark (4B), with task-dependent effects.

---

## 🏆 Benchmark Performance

### MedSG-Bench (Table 1 of the paper)

Each cell is **mIoU / Acc@0.5** in percent on all 9,630 test questions. IDG: registered (RDG) and non-registered (NRDG)
difference grounding. ICG: multi-view (MV), object tracking (OT), concept (VCG), patch (VPG), cross-modal (CMG) and
referring (RG) grounding. **Bold**: our results that exceed MedSeq-Grounder, the strongest prior model, on the same metric.

| Group | Model | Size | RDG | NRDG | MV | OT | VCG | VPG | CMG | RG | **Avg.** |
|---|---|---|---|---|---|---|---|---|---|---|---|
| General | Qwen2.5-VL | 72B | 4.37 / 2.60 | 3.46 / 0.80 | 7.22 / 2.78 | 13.11 / 7.70 | 10.33 / 3.50 | 26.45 / 6.30 | 16.32 / 7.00 | 20.19 / 14.10 | 13.35 / 6.12 |
| General | InternVL3 | 78B | 0.20 / 0.00 | 0.53 / 0.00 | 6.35 / 2.43 | 13.03 / 8.00 | 3.57 / 0.90 | 11.81 / 2.50 | 3.34 / 0.85 | 12.76 / 8.10 | 6.44 / 2.90 |
| General | LLaVA-OneVision | 72B | 2.58 / 0.80 | 2.87 / 0.90 | 11.74 / 1.39 | 9.61 / 2.30 | 10.95 / 3.30 | 32.38 / 20.30 | 16.24 / 5.40 | 15.43 / 6.68 | 13.21 / 5.18 |
| General | Migician | 7B | 15.26 / 7.80 | 14.49 / 6.10 | 18.16 / 7.84 | 21.38 / 14.90 | 14.23 / 7.20 | 28.87 / 13.70 | 21.41 / 12.15 | 25.30 / 18.02 | 20.29 / 11.39 |
| General | Qwen3.5 (zero-shot) | 27B | 0.00 / 0.00 | 0.00 / 0.00 | 8.48 / 4.88 | 28.39 / 24.50 | 0.25 / 0.00 | 2.50 / 0.20 | 0.25 / 0.00 | 0.82 / 0.00 | 4.42 / 3.15 |
| Medical | HuatuoGPT-Vision | 34B | 1.44 / 0.00 | 2.15 / 0.00 | 9.41 / 1.65 | 13.25 / 8.30 | 6.43 / 0.70 | 14.53 / 1.40 | 10.60 / 2.60 | 8.60 / 1.75 | 8.57 / 2.09 |
| Medical | MedGemma | 27B | 0.01 / 0.00 | 0.06 / 0.00 | 5.29 / 2.26 | 22.26 / 16.10 | 2.27 / 0.00 | 3.11 / 0.30 | 2.92 / 0.35 | 5.35 / 0.61 | 4.94 / 2.14 |
| Medical | MedGRPO-7B-RL | 7B | 0.09 / 0.00 | 0.06 / 0.00 | 3.68 / 1.48 | 17.63 / 15.00 | 4.57 / 0.40 | 18.44 / 1.20 | 0.78 / 0.15 | 9.31 / 3.37 | 6.27 / 2.45 |
| Medical | MedSeq-Grounder | 7B | 83.29 / 93.20 | 83.72 / 94.10 | 55.03 / 60.19 | 62.10 / 67.20 | 74.11 / 82.60 | 85.25 / 98.80 | 78.77 / 82.75 | 60.43 / 65.59 | 72.55 / 79.71 |
| Ours | MedChain-R1 (dCoT-SFT) | 4B | 82.86 / **93.30** | 81.30 / 91.20 | 54.01 / 58.97 | **64.77** / **71.00** | 70.64 / 78.80 | **90.21** / **99.70** | **83.39** / **87.00** | **69.67** / **79.28** | **74.96** / **82.37** |
| Ours | MedChain-R1 (+GRPO) | 4B | **83.82** / **94.50** | 83.21 / 92.70 | 54.80 / 59.93 | **67.90** / **76.20** | 66.09 / 72.10 | **89.43** / **99.80** | **83.59** / **87.55** | **70.72** / **80.30** | **75.32** / **82.89** |
| Ours | MedChain-R1 (dCoT-SFT) | 9B | **84.00** / 93.10 | 83.02 / 91.60 | **55.87** / **60.98** | **67.46** / **75.00** | **74.64** / **83.20** | **91.22** / **99.90** | **84.16** / **87.40** | **70.77** / **78.95** | **76.60** / **83.55** |
| Ours | MedChain-R1 (+GRPO) | 9B | **84.16** / 93.20 | 83.12 / 91.40 | **56.98** / **62.02** | **67.65** / **74.90** | **74.80** / **82.80** | **91.22** / **99.80** | **84.82** / **88.25** | **70.53** / **79.22** | **76.90** / **83.82** |
| Ours | MedChain-R1 (dCoT-SFT) | 27B | 81.87 / 93.00 | 80.97 / 91.60 | **60.14** / **66.72** | **69.53** / **78.30** | **74.64** / **83.30** | **91.87** / **99.90** | **84.24** / **88.15** | **72.51** / **81.98** | **77.24** / **85.20** |

> Baselines are evaluated zero-shot with one unified prompt (MedSeq-Grounder is the supervised SOTA). Full results for all
> baselines are in Table 10 of the paper. Benchmarked on AMD MI210 GPUs.

### This checkpoint

The paper's 9B GRPO row (76.90 / 83.82) comes from a different GRPO checkpoint. **This repository
holds:** dCoT-SFT checkpoint-2500 → GRPO (IoU + format reward) on the held-out RL30K pool, a second pass (seed 45) continuing the seed-42 GRPO model, step 2500. Evaluated on the same 9,630 questions with greedy decoding, bf16 and 1,200 new tokens, at
batch size 1 (the paper's evaluations used batch size 16), it scores:

| RDG | NRDG | MV | OT | VCG | VPG | CMG | RG | **mIoU / Acc@0.5** |
|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| 84.41 | 83.53 | 58.44 | 68.52 | 75.49 | 91.34 | 84.79 | 72.01 | **77.54 / 84.87** |

Per-task cells are mIoU. The per-example predictions are in the [code repository](https://github.com/adinathdukre/MedChain-R1).

---

## 🚀 Quick Start

### Installation

```bash
pip install "transformers==5.2.0" torch qwen-vl-utils==0.0.14
```

### Basic Usage

```python
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor
from qwen_vl_utils import process_vision_info
repo = "adidukrembzuai/MedChain-R1-9B"
model = AutoModelForImageTextToText.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto").eval()
processor = AutoProcessor.from_pretrained(repo)
question = "Compare these two images carefully and give me the coordinates of their difference."
messages = [{
    "role": "user",
    "content": [
        {"type": "image", "image": "scan_1.png"},
        {"type": "image", "image": "scan_2.png"},
        {"type": "text", "text": question + "\nFirst output the thinking process in <think> </think> tags "
                                  "and then output the bounding box in <answer>(x1,y1),(x2,y2)</answer> tags."},
    ],
}]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(text=[text], images=image_inputs, videos=video_inputs, return_tensors="pt").to(model.device)
with torch.inference_mode():
    out = model.generate(**inputs, max_new_tokens=1200, do_sample=False)
print(processor.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
# <think> ... </think><answer>(x1,y1),(x2,y2)</answer>
```

**Notes**

* Append the instruction above to every question; it is the prompt the model was trained and evaluated with.
* Generate one example at a time: batched generation produced degenerate outputs for some checkpoints.
* Use the processor bundled in this repository (the dCoT-SFT processor). The paper (Section 5.3) shows that an exported
  processor with a lower image-resolution limit costs 3–5 points.
* `eval/eval_bench.py` in the [code repository](https://github.com/adinathdukre/MedChain-R1) is the exact evaluation script.

---

## 📦 Model Family

| Model | Base | This checkpoint, MedSG-Bench mIoU / Acc@0.5 | Best for |
|---|---|---:|---|
| [MedChain-R1-9B](https://huggingface.co/adidukrembzuai/MedChain-R1-9B) | Qwen3.5-9B | 77.54 / 84.87 | highest accuracy — **recommended** |
| [MedChain-R1-4B](https://huggingface.co/adidukrembzuai/MedChain-R1-4B) | Qwen3.5-4B | 75.97 / 83.67 | resource-constrained environments |

---

## ⚠️ Limitations

Research artefact, **not validated for autonomous diagnosis or clinical deployment**. The MedChain rationales are
conditioned on the annotated box, so they explain a known answer and may be post-hoc; they should not be read as a
faithful record of the model's internal computation. The rationales can inherit errors and biases from their teacher
models and were reviewed in a single-expert audit of 2,000 examples (98.25% accepted). Evaluation is limited to MedSG-Bench.

---

## 📄 Citation

If you use MedChain-R1 in your research, please cite our paper:

```bibtex
@article{dukre2026think,
  title   = {Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding},
  author  = {Dukre, Adinath Madhavrao and Kolli, Govinda and Lu, Yifan and Zou, Ziyun and Wasi, Azmine Toushik and
             Atito, Sara and Bozorgtabar, Behzad and Mahapatra, Dwarikanath and Awais, Muhammad and Razzak, Imran},
  journal = {arXiv preprint arXiv:2611.00041},
  year    = {2026}
}
```

---

## 📜 License

Released under the **Apache License 2.0**. Built on Qwen3.5 (Apache-2.0). MedSG images keep their original licences and
are not included.

	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	base_model: Qwen/Qwen3.5-9B
	tags:
	- medical
	- multimodal
	- grounding
	- visual-grounding
	- multi-image
	- reasoning
	- chain-of-thought
	- radiology
	- mri
	- ct
	- arxiv:2611.00041
	---

	# MedChain-R1-9B: Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding

	[![Paper](https://img.shields.io/badge/arXiv-2611.00041-red)](https://arxiv.org/abs/2611.00041)
	[![Code](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/adinathdukre/MedChain-R1)
	[![Model](https://img.shields.io/badge/🤗-MedChain--R1--4B-blue)](https://huggingface.co/adidukrembzuai/MedChain-R1-4B)
	[![Model](https://img.shields.io/badge/🤗-MedChain--R1--9B-blue)](https://huggingface.co/adidukrembzuai/MedChain-R1-9B)
	[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)

	<p align="center">
	<img src="overview.png" alt="MedChain-R1 overview" width="900"/>
	</p>

	MedChain-R1-9B is a 9B vision-language model for sequential medical image grounding: given a short sequence of
	medical images and a clinical question, it first writes an image-grounded rationale in `<think>…</think>` and then
	returns one bounding box `<answer>(x1,y1),(x2,y2)</answer>` in pixel coordinates. It is built on
	[Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) with **deliberative chain-of-thought supervision
	(dCoT-SFT) on the MedChain rationale corpus, followed by GRPO** with an IoU + format reward.

	MedChain contains 182,573 filtered rationales over the 8 MedSG-Bench tasks and 10 imaging modalities. A medical VLM
	(FlemingVL-38B) drafts a caption-like rationale; a multimodal refiner (Qwen3.5-397B-A17B) then re-examines the images,
	question and annotated box and writes a box-conditioned explanation with explicit cross-image reasoning.

	---

	## 🔑 Key findings (from the paper)

	1. Reasoning supervision sets a new SOTA. dCoT-SFT outperforms MedSeq-Grounder at every scale, reaching 74.96, 76.60
	and 77.24 mIoU at 4B, 9B and 27B; the 27B model improves over the strongest prior model by 4.69 mIoU and 5.49 Acc@0.5.
	2. The gains depend on the task. Against an answer-only 4B model, dCoT-SFT improves mIoU by 3.56 (95% CI 3.13–4.00):
	+14.7–16.1 on difference grounding and +6.5 on object tracking, but −2.2 to −3.2 on multi-view, patch and referring.
	3. Predictions depend on the reasoning context. Forcing an immediate answer lowers mIoU by 2.9; conditioning on a
	rationale from another task lowers it by 12.2.
	4. GRPO adds little after dCoT-SFT. +0.37 mIoU on the benchmark (4B), with task-dependent effects.

	---

	## 🏆 Benchmark Performance

	### MedSG-Bench (Table 1 of the paper)

	Each cell is mIoU / Acc@0.5 in percent on all 9,630 test questions. IDG: registered (RDG) and non-registered (NRDG)
	difference grounding. ICG: multi-view (MV), object tracking (OT), concept (VCG), patch (VPG), cross-modal (CMG) and
	referring (RG) grounding. Bold: our results that exceed MedSeq-Grounder, the strongest prior model, on the same metric.

	\| Group \| Model \| Size \| RDG \| NRDG \| MV \| OT \| VCG \| VPG \| CMG \| RG \| Avg. \|
	\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|
	\| General \| Qwen2.5-VL \| 72B \| 4.37 / 2.60 \| 3.46 / 0.80 \| 7.22 / 2.78 \| 13.11 / 7.70 \| 10.33 / 3.50 \| 26.45 / 6.30 \| 16.32 / 7.00 \| 20.19 / 14.10 \| 13.35 / 6.12 \|
	\| General \| InternVL3 \| 78B \| 0.20 / 0.00 \| 0.53 / 0.00 \| 6.35 / 2.43 \| 13.03 / 8.00 \| 3.57 / 0.90 \| 11.81 / 2.50 \| 3.34 / 0.85 \| 12.76 / 8.10 \| 6.44 / 2.90 \|
	\| General \| LLaVA-OneVision \| 72B \| 2.58 / 0.80 \| 2.87 / 0.90 \| 11.74 / 1.39 \| 9.61 / 2.30 \| 10.95 / 3.30 \| 32.38 / 20.30 \| 16.24 / 5.40 \| 15.43 / 6.68 \| 13.21 / 5.18 \|
	\| General \| Migician \| 7B \| 15.26 / 7.80 \| 14.49 / 6.10 \| 18.16 / 7.84 \| 21.38 / 14.90 \| 14.23 / 7.20 \| 28.87 / 13.70 \| 21.41 / 12.15 \| 25.30 / 18.02 \| 20.29 / 11.39 \|
	\| General \| Qwen3.5 (zero-shot) \| 27B \| 0.00 / 0.00 \| 0.00 / 0.00 \| 8.48 / 4.88 \| 28.39 / 24.50 \| 0.25 / 0.00 \| 2.50 / 0.20 \| 0.25 / 0.00 \| 0.82 / 0.00 \| 4.42 / 3.15 \|
	\| Medical \| HuatuoGPT-Vision \| 34B \| 1.44 / 0.00 \| 2.15 / 0.00 \| 9.41 / 1.65 \| 13.25 / 8.30 \| 6.43 / 0.70 \| 14.53 / 1.40 \| 10.60 / 2.60 \| 8.60 / 1.75 \| 8.57 / 2.09 \|
	\| Medical \| MedGemma \| 27B \| 0.01 / 0.00 \| 0.06 / 0.00 \| 5.29 / 2.26 \| 22.26 / 16.10 \| 2.27 / 0.00 \| 3.11 / 0.30 \| 2.92 / 0.35 \| 5.35 / 0.61 \| 4.94 / 2.14 \|
	\| Medical \| MedGRPO-7B-RL \| 7B \| 0.09 / 0.00 \| 0.06 / 0.00 \| 3.68 / 1.48 \| 17.63 / 15.00 \| 4.57 / 0.40 \| 18.44 / 1.20 \| 0.78 / 0.15 \| 9.31 / 3.37 \| 6.27 / 2.45 \|
	\| Medical \| MedSeq-Grounder \| 7B \| 83.29 / 93.20 \| 83.72 / 94.10 \| 55.03 / 60.19 \| 62.10 / 67.20 \| 74.11 / 82.60 \| 85.25 / 98.80 \| 78.77 / 82.75 \| 60.43 / 65.59 \| 72.55 / 79.71 \|
	\| Ours \| MedChain-R1 (dCoT-SFT) \| 4B \| 82.86 / 93.30 \| 81.30 / 91.20 \| 54.01 / 58.97 \| 64.77 / 71.00 \| 70.64 / 78.80 \| 90.21 / 99.70 \| 83.39 / 87.00 \| 69.67 / 79.28 \| 74.96 / 82.37 \|
	\| Ours \| MedChain-R1 (+GRPO) \| 4B \| 83.82 / 94.50 \| 83.21 / 92.70 \| 54.80 / 59.93 \| 67.90 / 76.20 \| 66.09 / 72.10 \| 89.43 / 99.80 \| 83.59 / 87.55 \| 70.72 / 80.30 \| 75.32 / 82.89 \|
	\| Ours \| MedChain-R1 (dCoT-SFT) \| 9B \| 84.00 / 93.10 \| 83.02 / 91.60 \| 55.87 / 60.98 \| 67.46 / 75.00 \| 74.64 / 83.20 \| 91.22 / 99.90 \| 84.16 / 87.40 \| 70.77 / 78.95 \| 76.60 / 83.55 \|
	\| Ours \| MedChain-R1 (+GRPO) \| 9B \| 84.16 / 93.20 \| 83.12 / 91.40 \| 56.98 / 62.02 \| 67.65 / 74.90 \| 74.80 / 82.80 \| 91.22 / 99.80 \| 84.82 / 88.25 \| 70.53 / 79.22 \| 76.90 / 83.82 \|
	\| Ours \| MedChain-R1 (dCoT-SFT) \| 27B \| 81.87 / 93.00 \| 80.97 / 91.60 \| 60.14 / 66.72 \| 69.53 / 78.30 \| 74.64 / 83.30 \| 91.87 / 99.90 \| 84.24 / 88.15 \| 72.51 / 81.98 \| 77.24 / 85.20 \|

	> Baselines are evaluated zero-shot with one unified prompt (MedSeq-Grounder is the supervised SOTA). Full results for all
	> baselines are in Table 10 of the paper. Benchmarked on AMD MI210 GPUs.

	### This checkpoint

	The paper's 9B GRPO row (76.90 / 83.82) comes from a different GRPO checkpoint. **This repository
	holds:** dCoT-SFT checkpoint-2500 → GRPO (IoU + format reward) on the held-out RL30K pool, a second pass (seed 45) continuing the seed-42 GRPO model, step 2500. Evaluated on the same 9,630 questions with greedy decoding, bf16 and 1,200 new tokens, at
	batch size 1 (the paper's evaluations used batch size 16), it scores:

	\| RDG \| NRDG \| MV \| OT \| VCG \| VPG \| CMG \| RG \| mIoU / Acc@0.5 \|
	\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| 84.41 \| 83.53 \| 58.44 \| 68.52 \| 75.49 \| 91.34 \| 84.79 \| 72.01 \| 77.54 / 84.87 \|

	Per-task cells are mIoU. The per-example predictions are in the [code repository](https://github.com/adinathdukre/MedChain-R1).

	---

	## 🚀 Quick Start

	### Installation

	```bash
	pip install "transformers==5.2.0" torch qwen-vl-utils==0.0.14
	```

	### Basic Usage

	```python
	import torch
	from transformers import AutoModelForImageTextToText, AutoProcessor
	from qwen_vl_utils import process_vision_info

	repo = "adidukrembzuai/MedChain-R1-9B"
	model = AutoModelForImageTextToText.from_pretrained(repo, torch_dtype=torch.bfloat16, device_map="auto").eval()
	processor = AutoProcessor.from_pretrained(repo)

	question = "Compare these two images carefully and give me the coordinates of their difference."
	messages = [{
	"role": "user",
	"content": [
	{"type": "image", "image": "scan_1.png"},
	{"type": "image", "image": "scan_2.png"},
	{"type": "text", "text": question + "\nFirst output the thinking process in <think> </think> tags "
	"and then output the bounding box in <answer>(x1,y1),(x2,y2)</answer> tags."},
	],
	}]

	text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	image_inputs, video_inputs = process_vision_info(messages)
	inputs = processor(text=[text], images=image_inputs, videos=video_inputs, return_tensors="pt").to(model.device)

	with torch.inference_mode():
	out = model.generate(**inputs, max_new_tokens=1200, do_sample=False)
	print(processor.decode(out[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	# <think> ... </think><answer>(x1,y1),(x2,y2)</answer>
	```

	Notes

	* Append the instruction above to every question; it is the prompt the model was trained and evaluated with.
	* Generate one example at a time: batched generation produced degenerate outputs for some checkpoints.
	* Use the processor bundled in this repository (the dCoT-SFT processor). The paper (Section 5.3) shows that an exported
	processor with a lower image-resolution limit costs 3–5 points.
	* `eval/eval_bench.py` in the [code repository](https://github.com/adinathdukre/MedChain-R1) is the exact evaluation script.

	---

	## 📦 Model Family

	\| Model \| Base \| This checkpoint, MedSG-Bench mIoU / Acc@0.5 \| Best for \|
	\|---\|---\|---:\|---\|
	\| [MedChain-R1-9B](https://huggingface.co/adidukrembzuai/MedChain-R1-9B) \| Qwen3.5-9B \| 77.54 / 84.87 \| highest accuracy — recommended \|
	\| [MedChain-R1-4B](https://huggingface.co/adidukrembzuai/MedChain-R1-4B) \| Qwen3.5-4B \| 75.97 / 83.67 \| resource-constrained environments \|

	---

	## ⚠️ Limitations

	Research artefact, not validated for autonomous diagnosis or clinical deployment. The MedChain rationales are
	conditioned on the annotated box, so they explain a known answer and may be post-hoc; they should not be read as a
	faithful record of the model's internal computation. The rationales can inherit errors and biases from their teacher
	models and were reviewed in a single-expert audit of 2,000 examples (98.25% accepted). Evaluation is limited to MedSG-Bench.

	---

	## 📄 Citation

	If you use MedChain-R1 in your research, please cite our paper:

	```bibtex
	@article{dukre2026think,
	title = {Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding},
	author = {Dukre, Adinath Madhavrao and Kolli, Govinda and Lu, Yifan and Zou, Ziyun and Wasi, Azmine Toushik and
	Atito, Sara and Bozorgtabar, Behzad and Mahapatra, Dwarikanath and Awais, Muhammad and Razzak, Imran},
	journal = {arXiv preprint arXiv:2611.00041},
	year = {2026}
	}
	```

	---

	## 📜 License

	Released under the Apache License 2.0. Built on Qwen3.5 (Apache-2.0). MedSG images keep their original licences and
	are not included.