Instructions to use GenSearcher/Gen-Searcher-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use GenSearcher/Gen-Searcher-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-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-8B") model = AutoModelForImageTextToText.from_pretrained("GenSearcher/Gen-Searcher-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-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-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-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-8B
- SGLang
How to use GenSearcher/Gen-Searcher-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-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-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-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-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-8B with Docker Model Runner:
docker model run hf.co/GenSearcher/Gen-Searcher-8B
Gen-Searcher-8B Model
This repository contains the Gen-Searcher-8B model presented in Gen-Searcher: Reinforcing Agentic Search for Image Generation.
Project Page | GitHub Repository | Paper
👀 Intro
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
For more examples, please refer to our website [🌐Project Page]
🚀 Training and Inference
For detailed instructions on setup, SFT/RL training, and inference, please refer to the official GitHub repository.
📐 Citation
If you find our work helpful for your research, please consider citing our work:
@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}
}
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