Instructions to use gsarch/ViGoRL-Multiturn-7b-Visual-Search with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gsarch/ViGoRL-Multiturn-7b-Visual-Search with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="gsarch/ViGoRL-Multiturn-7b-Visual-Search") 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("gsarch/ViGoRL-Multiturn-7b-Visual-Search") model = AutoModelForMultimodalLM.from_pretrained("gsarch/ViGoRL-Multiturn-7b-Visual-Search", 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 gsarch/ViGoRL-Multiturn-7b-Visual-Search with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "gsarch/ViGoRL-Multiturn-7b-Visual-Search" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "gsarch/ViGoRL-Multiturn-7b-Visual-Search", "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/gsarch/ViGoRL-Multiturn-7b-Visual-Search
- SGLang
How to use gsarch/ViGoRL-Multiturn-7b-Visual-Search 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 "gsarch/ViGoRL-Multiturn-7b-Visual-Search" \ --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": "gsarch/ViGoRL-Multiturn-7b-Visual-Search", "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 "gsarch/ViGoRL-Multiturn-7b-Visual-Search" \ --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": "gsarch/ViGoRL-Multiturn-7b-Visual-Search", "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 gsarch/ViGoRL-Multiturn-7b-Visual-Search with Docker Model Runner:
docker model run hf.co/gsarch/ViGoRL-Multiturn-7b-Visual-Search
| { | |
| "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant tasked with answering a question about an image. You should systematically reason through the problem step by step by checking and verifying relevant image regions, while grounding reasoning steps to specific (x, y) points in the image:\n- At each turn, first clearly reason about ONE area or element in the image enclosed in <think> </think> tags.\n- After reasoning, either:\n a) Zoom-in on a specific region to see it better by outputting a search action formatted precisely as:\n <tool_call>\n {\"name\": \"search_coordinate\", \"arguments\": {\"coordinate\": [x, y]}}\n </tool_call>\n b) If confident you've found the correct location, output your final answer enclosed in <answer> {final answer} </answer> tags.\n- Only answer if you are confident about the answer. If you are not confident, output a search action. You should not always end after one turn.\n- You should not repeat the same coordinates in a tool call more than once. Coordinates must be unique across tool calls, including values that are the same or nearly identical (e.g., differing by only a few pixels).\n- If unclear, infer based on likely context or purpose.\n- Verify each step by examining multiple possible solutions before selecting a final answer.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}" | |
| } | |