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
File size: 1,027 Bytes
0379b48 bc71fa5 0379b48 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | {
"data_format": "channels_first",
"default_to_square": true,
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.48145466,
0.4578275,
0.40821073
],
"image_processor_type": "Qwen2VLImageProcessor",
"image_std": [
0.26862954,
0.26130258,
0.27577711
],
"max_pixels": 4000000,
"merge_size": 2,
"min_pixels": 3136,
"patch_size": 14,
"processor_class": "LlavaOnevision2Processor",
"auto_map": {
"AutoProcessor": "processing_llava_onevision2.LlavaOnevision2Processor",
"AutoVideoProcessor": "video_processing_llava_onevision2.LlavaOnevision2VideoProcessor"
},
"codec": {
"target_canvas": 32,
"group_size": 32,
"images_per_group": 4,
"patch": 14,
"min_group_frames": 8,
"max_group_frames": 64,
"spatial_mask_mode": "off"
},
"resample": 3,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 4000000,
"shortest_edge": 3136
},
"temporal_patch_size": 1
}
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