Instructions to use piperw/paligemma_vqav2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use piperw/paligemma_vqav2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="piperw/paligemma_vqav2")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("piperw/paligemma_vqav2") model = AutoModelForMultimodalLM.from_pretrained("piperw/paligemma_vqav2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use piperw/paligemma_vqav2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "piperw/paligemma_vqav2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "piperw/paligemma_vqav2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/piperw/paligemma_vqav2
- SGLang
How to use piperw/paligemma_vqav2 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 "piperw/paligemma_vqav2" \ --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": "piperw/paligemma_vqav2", "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 "piperw/paligemma_vqav2" \ --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": "piperw/paligemma_vqav2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use piperw/paligemma_vqav2 with Docker Model Runner:
docker model run hf.co/piperw/paligemma_vqav2
Download training_args.bin from piperw/paligemma_vqav2: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/piperw/paligemma_vqav2/resolve/main/training_args.bin
- Command line
-
hf download hf://piperw/paligemma_vqav2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/piperw/paligemma_vqav2/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 394acdda15b1ca6875a0bc24e59d9f3b160cfdc1d7bec4959045aa5a2ef545da
- Size of remote file:
- 5.11 kB
- SHA256:
- 11ada920a443e0d89c772759623d52260bd7082ef41cd000d56ea9ec5b6fa8b0
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