Instructions to use StevenHH2000/Fine-R1-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StevenHH2000/Fine-R1-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="StevenHH2000/Fine-R1-3B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("StevenHH2000/Fine-R1-3B")
model = AutoModelForMultimodalLM.from_pretrained("StevenHH2000/Fine-R1-3B", 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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use StevenHH2000/Fine-R1-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StevenHH2000/Fine-R1-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "StevenHH2000/Fine-R1-3B",
		"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/StevenHH2000/Fine-R1-3B

SGLang

How to use StevenHH2000/Fine-R1-3B 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 "StevenHH2000/Fine-R1-3B" \
    --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": "StevenHH2000/Fine-R1-3B",
		"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 "StevenHH2000/Fine-R1-3B" \
        --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": "StevenHH2000/Fine-R1-3B",
		"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 StevenHH2000/Fine-R1-3B with Docker Model Runner:
```
docker model run hf.co/StevenHH2000/Fine-R1-3B
```

Fine-R1-3B / README.md

StevenHH2000

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	---
	library_name: transformers
	license: mit
	pipeline_tag: image-text-to-text
	tags:
	- fine-grained-recognition
	- chain-of-thought
	- vision-language
	- reasoning
	- qwen2-vl
	- arxiv:2602.07605
	---

	# Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning

	This is the official 3B model released for the paper [Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning](https://huggingface.co/papers/2602.07605).

	Authors: Hulingxiao He, Zijun Geng, and Yuxin Peng.

	## Introduction

	Fine-R1 is a Multi-modal Large Language Model (MLLM) specifically designed to excel in Fine-Grained Visual Recognition (FGVR). While traditional MLLMs often struggle with FGVR compared to contrastive models like CLIP, Fine-R1 bridges this performance gap by incorporating Chain-of-Thought (CoT) reasoning. It achieves state-of-the-art performance, even surpassing strong CLIP-like models, in identifying both seen and unseen fine-grained sub-categories with only 4-shot training.

	## Methodology

	Fine-R1 employs an R1-style training framework consisting of two key stages:

	1. Chain-of-Thought Supervised Fine-tuning (SFT): This stage involves constructing a high-quality FGVR CoT dataset with rationales covering "visual analysis, candidate sub-categories, comparison, and prediction." This process trains the model to act as a strong open-world classifier.
	2. Triplet Augmented Policy Optimization (TAPO): This stage enhances the model's robustness and discriminative ability. It uses Intra-class Augmentation to improve robustness to intra-class variance and Inter-class Augmentation to maximize response distinction across sub-categories.

	## GitHub Repository

	For code, data, and detailed training/evaluation instructions, please refer to the official repository:
	[https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026)

	## Model Version
	Fine-R1-3B

	## Usage

	This model can be used with the Hugging Face `transformers` library. For detailed usage examples and how to integrate it into your projects, please refer to the official [GitHub Repository](https://github.com/PKU-ICST-MIPL/FineR1_ICLR2026).

	## Citation

	If you find this model or the research helpful, please consider citing:

	```bibtex
	@article{he2026finer1,
	title={Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning},
	author={He, Hulingxiao and Geng, Zijun and Peng, Yuxin},
	journal={arXiv preprint arXiv:2602.07605},
	year={2026}
	}
	```