Image-Text-to-Text
Transformers
Safetensors
English
gemma3
vision-language
multimodal
home-security
smart-home
on-device
text-generation-inference
Instructions to use llmvision/glimpse-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmvision/glimpse-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="llmvision/glimpse-v1")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("llmvision/glimpse-v1") model = AutoModelForImageTextToText.from_pretrained("llmvision/glimpse-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llmvision/glimpse-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmvision/glimpse-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmvision/glimpse-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmvision/glimpse-v1
- SGLang
How to use llmvision/glimpse-v1 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 "llmvision/glimpse-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmvision/glimpse-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "llmvision/glimpse-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmvision/glimpse-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llmvision/glimpse-v1 with Docker Model Runner:
docker model run hf.co/llmvision/glimpse-v1
- Xet hash:
- c4c7aa84fb94a696d78f2bde62590a25b6741b7365ed842f9310b5dcab71d95d
- Size of remote file:
- 33.4 MB
- SHA256:
- 4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.