Instructions to use GenSearcher/Gen-Searcher-SFT-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GenSearcher/Gen-Searcher-SFT-8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="GenSearcher/Gen-Searcher-SFT-8B")
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("GenSearcher/Gen-Searcher-SFT-8B")
model = AutoModelForImageTextToText.from_pretrained("GenSearcher/Gen-Searcher-SFT-8B")
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 GenSearcher/Gen-Searcher-SFT-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GenSearcher/Gen-Searcher-SFT-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GenSearcher/Gen-Searcher-SFT-8B",
		"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/GenSearcher/Gen-Searcher-SFT-8B

SGLang

How to use GenSearcher/Gen-Searcher-SFT-8B 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 "GenSearcher/Gen-Searcher-SFT-8B" \
    --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": "GenSearcher/Gen-Searcher-SFT-8B",
		"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 "GenSearcher/Gen-Searcher-SFT-8B" \
        --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": "GenSearcher/Gen-Searcher-SFT-8B",
		"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 GenSearcher/Gen-Searcher-SFT-8B with Docker Model Runner:
```
docker model run hf.co/GenSearcher/Gen-Searcher-SFT-8B
```

Gen-Searcher-SFT-8B / README.md

KaituoFeng

Update README.md

9482f26 verified about 2 months ago

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: image-text-to-text
	---

	# Gen-Searcher SFT Model

	This repository contains the Supervised Fine-Tuning (SFT) model presented in the paper: [Gen-Searcher: Reinforcing Agentic Search for Image Generation](https://arxiv.org/abs/2603.28767).

	This is an intermediate model prepared for subsequent reinforcement learning (RL) training using the GRPO algorithm with dual reward feedback.

	[🌐 Project Page](https://gen-searcher.vercel.app/) \| [💻 Code](https://github.com/tulerfeng/Gen-Searcher) \| [📖 Paper](https://arxiv.org/abs/2603.28767)

	# 👀 Intro

	<div align="center">
	<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/teaser.jpg?raw=true" alt="Gen-Searcher Teaser" width="80%">
	</div>

	We introduce Gen-Searcher, as the first attempt to train a multimodal deep research agent for image generation that requires complex real-world knowledge. Gen-Searcher can search the web, browse evidence, reason over multiple sources, and search visual references before generation, enabling more accurate and up-to-date image synthesis in real-world scenarios.

	We build two dedicated training datasets Gen-Searcher-SFT-10k, Gen-Searcher-RL-6k and one new benchmark KnowGen for search-grounded image generation.

	Gen-Searcher achieves significant improvements, delivering 15+ point gains on the KnowGen and WISE benchmarks. It also demonstrates strong transferability to various image generators.

	All code, models, data, and benchmark are fully released.

	## 🎥 Demo

	#### Inference Process Example

	<div align="center">
	<img src="https://github.com/tulerfeng/Gen-Searcher/blob/main/assets/example.jpg?raw=true" alt="Inference Process Example" width="85%">
	</div>

	For more examples, please refer to our website [[🌐 Project Page]](https://gen-searcher.vercel.app/).

	## Citation

	If you find our work helpful for your research, please consider citing our work:

	```bibtex
	@article{feng2026gen,
	title={Gen-Searcher: Reinforcing Agentic Search for Image Generation},
	author={Feng, Kaituo and Zhang, Manyuan and Chen, Shuang and Lin, Yunlong and Fan, Kaixuan and Jiang, Yilei and Li, Hongyu and Zheng, Dian and Wang, Chenyang and Yue, Xiangyu},
	journal={arXiv preprint arXiv:2603.28767},
	year={2026}
	}
	```