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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/MedChain-R1-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/MedChain-R1-9B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/MedChain-R1-9B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/MedChain-R1-9B-GGUF",
		"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/prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/MedChain-R1-9B-GGUF 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 "prithivMLmods/MedChain-R1-9B-GGUF" \
    --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": "prithivMLmods/MedChain-R1-9B-GGUF",
		"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 "prithivMLmods/MedChain-R1-9B-GGUF" \
        --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": "prithivMLmods/MedChain-R1-9B-GGUF",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use prithivMLmods/MedChain-R1-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/MedChain-R1-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/MedChain-R1-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/MedChain-R1-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MedChain-R1-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/MedChain-R1-9B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/MedChain-R1-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "prithivMLmods/MedChain-R1-9B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MedChain-R1-9B-GGUF / README.md

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---
base_model:
- adidukrembzuai/MedChain-R1-9B
library_name: transformers
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
tags:
- text-generation-inference
- llama-cpp
- qwen3_5
- medical
- multimodal
- grounding
- visual-grounding
- multi-image
- reasoning
- chain-of-thought
- radiology
- mri
- ct
- arxiv:2611.00041
---
# **MedChain-R1-9B-GGUF**

> **MedChain-R1-9B** is a 9B vision-language model for sequential medical image grounding, built on Qwen3.5-9B and described in "Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding" (arXiv:2611.00041). Given a sequence of medical images and a clinical question, it first writes an image-grounded rationale in `<think>` tags, then returns one pixel-coordinate bounding box. Training used deliberative chain-of-thought supervision (dCoT-SFT) on the MedChain corpus, followed by GRPO with an IoU plus format reward. MedChain has 182,573 filtered rationales over the 8 MedSG-Bench tasks and 10 imaging modalities. A medical VLM (FlemingVL-38B) drafts each rationale and a multimodal refiner (Qwen3.5-397B-A17B) rewrites it conditioned on the annotated box. This checkpoint scores 77.54 mIoU / 84.87 Acc`@`0.5 on all 9,630 MedSG-Bench test questions, above the paper's 9B GRPO row (76.90 / 83.82), which came from a different checkpoint. It also beats the supervised state-of-the-art MedSeq-Grounder 7B (72.55 / 79.71) and is far ahead of the zero-shot general and medical baselines, which mostly score below 21 mIoU. The paper notes that the gains vary by task. Against an answer-only 4B model, the reasoning supervision gains 14.7–16.1 mIoU on difference grounding but loses 2.2–3.2 on multi-view, patch and referring grounding. It adds that GRPO contributes little after dCoT-SFT (+0.37 mIoU at 4B). The card says to generate one example at a time, because batched generation gave degenerate outputs, and to use the bundled processor. The rationales were written knowing the answer, so they may be post-hoc rather than a faithful record of the model's reasoning. The model is a research artifact, not validated for diagnosis or clinical deployment, and evaluation is limited to MedSG-Bench. It is released under Apache 2.0.

> [!NOTE]
> MTP — Speculative Decoding: No

## Model Files

| File Name | Quant Type | File Size | File Link | Description |
|-----------|------------|-----------|-----------|-------------|
| MedChain-R1-9B.BF16.gguf | BF16 | 17.9 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.BF16.gguf) | Full BF16 weights. Highest quality, largest file size. |
| MedChain-R1-9B.Q3_K_L.gguf | Q3_K_L | 4.93 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q3_K_L.gguf) | Lower quality but usable, good for low RAM availability. |
| MedChain-R1-9B.Q3_K_M.gguf | Q3_K_M | 4.62 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q3_K_M.gguf) | Low quality. |
| MedChain-R1-9B.Q4_K_M.gguf | Q4_K_M | 5.63 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q4_K_M.gguf) | Good quality, default size for most use cases, *recommended*. |
| MedChain-R1-9B.Q4_K_S.gguf | Q4_K_S | 5.35 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q4_K_S.gguf) | Slightly lower quality with more space savings, *recommended*. |
| MedChain-R1-9B.Q5_K_M.gguf | Q5_K_M | 6.47 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q5_K_M.gguf) | High quality, *recommended*. |
| MedChain-R1-9B.Q5_K_S.gguf | Q5_K_S | 6.31 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q5_K_S.gguf) | High quality, *recommended*. |
| MedChain-R1-9B.Q6_K.gguf | Q6_K | 7.36 GB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q6_K.gguf) | Very high quality, near perfect, *recommended*. |
| MedChain-R1-9B.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.mmproj-bf16.gguf) | Multimodal projection file in BF16 format. Used for vision/language models. |

## llama.cpp

LLM inference in C/C++ — [https://github.com/ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp)

Quantized using llama.cpp by [prithivMLmods](https://huggingface.co/prithivMLmods).

	---
	base_model:
	- adidukrembzuai/MedChain-R1-9B
	library_name: transformers
	license: apache-2.0
	language:
	- en
	pipeline_tag: image-text-to-text
	tags:
	- text-generation-inference
	- llama-cpp
	- qwen3_5
	- medical
	- multimodal
	- grounding
	- visual-grounding
	- multi-image
	- reasoning
	- chain-of-thought
	- radiology
	- mri
	- ct
	- arxiv:2611.00041
	---

	# MedChain-R1-9B-GGUF

	> MedChain-R1-9B is a 9B vision-language model for sequential medical image grounding, built on Qwen3.5-9B and described in "Think Before You Locate: Clinical Chain-of-Thought Supervision for Sequential Medical Image Grounding" (arXiv:2611.00041). Given a sequence of medical images and a clinical question, it first writes an image-grounded rationale in `<think>` tags, then returns one pixel-coordinate bounding box. Training used deliberative chain-of-thought supervision (dCoT-SFT) on the MedChain corpus, followed by GRPO with an IoU plus format reward. MedChain has 182,573 filtered rationales over the 8 MedSG-Bench tasks and 10 imaging modalities. A medical VLM (FlemingVL-38B) drafts each rationale and a multimodal refiner (Qwen3.5-397B-A17B) rewrites it conditioned on the annotated box. This checkpoint scores 77.54 mIoU / 84.87 Acc`@`0.5 on all 9,630 MedSG-Bench test questions, above the paper's 9B GRPO row (76.90 / 83.82), which came from a different checkpoint. It also beats the supervised state-of-the-art MedSeq-Grounder 7B (72.55 / 79.71) and is far ahead of the zero-shot general and medical baselines, which mostly score below 21 mIoU. The paper notes that the gains vary by task. Against an answer-only 4B model, the reasoning supervision gains 14.7–16.1 mIoU on difference grounding but loses 2.2–3.2 on multi-view, patch and referring grounding. It adds that GRPO contributes little after dCoT-SFT (+0.37 mIoU at 4B). The card says to generate one example at a time, because batched generation gave degenerate outputs, and to use the bundled processor. The rationales were written knowing the answer, so they may be post-hoc rather than a faithful record of the model's reasoning. The model is a research artifact, not validated for diagnosis or clinical deployment, and evaluation is limited to MedSG-Bench. It is released under Apache 2.0.

	> [!NOTE]
	> MTP — Speculative Decoding: No

	## Model Files

	\| File Name \| Quant Type \| File Size \| File Link \| Description \|
	\|-----------\|------------\|-----------\|-----------\|-------------\|
	\| MedChain-R1-9B.BF16.gguf \| BF16 \| 17.9 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.BF16.gguf) \| Full BF16 weights. Highest quality, largest file size. \|
	\| MedChain-R1-9B.Q3_K_L.gguf \| Q3_K_L \| 4.93 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q3_K_L.gguf) \| Lower quality but usable, good for low RAM availability. \|
	\| MedChain-R1-9B.Q3_K_M.gguf \| Q3_K_M \| 4.62 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q3_K_M.gguf) \| Low quality. \|
	\| MedChain-R1-9B.Q4_K_M.gguf \| Q4_K_M \| 5.63 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q4_K_M.gguf) \| Good quality, default size for most use cases, recommended. \|
	\| MedChain-R1-9B.Q4_K_S.gguf \| Q4_K_S \| 5.35 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q4_K_S.gguf) \| Slightly lower quality with more space savings, recommended. \|
	\| MedChain-R1-9B.Q5_K_M.gguf \| Q5_K_M \| 6.47 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q5_K_M.gguf) \| High quality, recommended. \|
	\| MedChain-R1-9B.Q5_K_S.gguf \| Q5_K_S \| 6.31 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q5_K_S.gguf) \| High quality, recommended. \|
	\| MedChain-R1-9B.Q6_K.gguf \| Q6_K \| 7.36 GB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.Q6_K.gguf) \| Very high quality, near perfect, recommended. \|
	\| MedChain-R1-9B.mmproj-bf16.gguf \| mmproj-bf16 \| 922 MB \| [Link](https://huggingface.co/prithivMLmods/MedChain-R1-9B-GGUF/blob/main/MedChain-R1-9B.mmproj-bf16.gguf) \| Multimodal projection file in BF16 format. Used for vision/language models. \|

	## llama.cpp

	LLM inference in C/C++ — [https://github.com/ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp)

	Quantized using llama.cpp by [prithivMLmods](https://huggingface.co/prithivMLmods).