Instructions to use StevenHH2000/Fine-R1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use StevenHH2000/Fine-R1-7B with Transformers:

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

pipe = pipeline("image-text-to-text", model="StevenHH2000/Fine-R1-7B")
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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("StevenHH2000/Fine-R1-7B")
model = AutoModelForImageTextToText.from_pretrained("StevenHH2000/Fine-R1-7B")
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

vLLM

How to use StevenHH2000/Fine-R1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "StevenHH2000/Fine-R1-7B"
# 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-R1-7B",
		"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-R1-7B

SGLang

How to use StevenHH2000/Fine-R1-7B 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-R1-7B" \
    --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-R1-7B",
		"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-R1-7B" \
        --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-R1-7B",
		"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-R1-7B with Docker Model Runner:
```
docker model run hf.co/StevenHH2000/Fine-R1-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

This is the official model repository for the paper Fine-R1: Make Multi-modal LLMs Excel in Fine-Grained Visual Recognition by Chain-of-Thought Reasoning.

Introduction

Fine-R1 is a Multi-modal Large Language Model (MLLM) specifically designed for Fine-Grained Visual Recognition (FGVR). While general MLLMs often struggle with distinguishing between highly similar sub-categories, Fine-R1 bridges the gap between generative models and specialized discriminative models (like CLIP) through an R1-style training framework.

Key Innovations:

Chain-of-Thought Supervised Fine-tuning (CoT-SFT): The model is trained on high-quality FGVR CoT datasets, teaching it to perform visual analysis, consider candidate sub-categories, and compare them before predicting.
Triplet Augmented Policy Optimization (TAPO): This includes Intra-class Augmentation to handle visual variance and Inter-class Augmentation to maximize distinction between similar sub-categories.

With only 4-shot training, Fine-R1 excels in identifying both seen and unseen sub-categories, outperforming many general reasoning MLLMs and contrastive models.

Resources

Paper: Hugging Face Papers
GitHub: PKU-ICST-MIPL/FineR1_ICLR2026

Usage

This model is compatible with the Hugging Face transformers library. For detailed instructions on environment setup, training scripts, and evaluation pipelines (closed-world and open-world), please refer to the official GitHub Repository.

Citation

If you find Fine-R1 helpful in your research, please cite the following paper:

@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}
}