Image-Text-to-Text
Transformers
Safetensors
English
Chinese
llava_onevision2
multimodal
vision-language
video-text-to-text
llava
llava-onevision-2
qwen3
conversational
custom_code
Instructions to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", trust_remote_code=True) 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 AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", "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/lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct
- SGLang
How to use lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct 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 "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct" \ --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": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", "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 "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct" \ --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": "lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct", "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 lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct with Docker Model Runner:
docker model run hf.co/lmms-lab-encoder/LLaVA-OneVision-2-8B-Instruct
Add codec video backend & docs (preprocessor_config.json)
Browse files- preprocessor_config.json +9 -0
preprocessor_config.json
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"AutoProcessor": "processing_llava_onevision2.LlavaOnevision2Processor",
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"AutoVideoProcessor": "video_processing_llava_onevision2.LlavaOnevision2VideoProcessor"
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},
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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"AutoProcessor": "processing_llava_onevision2.LlavaOnevision2Processor",
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"AutoVideoProcessor": "video_processing_llava_onevision2.LlavaOnevision2VideoProcessor"
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},
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"codec": {
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"target_canvas": 32,
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"group_size": 32,
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"images_per_group": 4,
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"patch": 14,
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"min_group_frames": 8,
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"max_group_frames": 64,
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"spatial_mask_mode": "off"
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},
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"resample": 3,
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"rescale_factor": 0.00392156862745098,
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"size": {
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