Image-Text-to-Text
Transformers
Safetensors
English
paligemma
biology
skin
skin disease
cancer
medical
text-generation-inference
Instructions to use brucewayne0459/paligemma_derm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brucewayne0459/paligemma_derm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="brucewayne0459/paligemma_derm")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("brucewayne0459/paligemma_derm") model = AutoModelForMultimodalLM.from_pretrained("brucewayne0459/paligemma_derm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brucewayne0459/paligemma_derm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "brucewayne0459/paligemma_derm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brucewayne0459/paligemma_derm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/brucewayne0459/paligemma_derm
- SGLang
How to use brucewayne0459/paligemma_derm 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 "brucewayne0459/paligemma_derm" \ --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": "brucewayne0459/paligemma_derm", "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 "brucewayne0459/paligemma_derm" \ --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": "brucewayne0459/paligemma_derm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use brucewayne0459/paligemma_derm with Docker Model Runner:
docker model run hf.co/brucewayne0459/paligemma_derm
Download tokenizer.json from brucewayne0459/paligemma_derm: direct link, hf CLI and curl.
- Browser
- Download file 17.8 MB
-
https://huggingface.co/brucewayne0459/paligemma_derm/resolve/main/tokenizer.json
- Command line
-
hf download hf://brucewayne0459/paligemma_derm/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/brucewayne0459/paligemma_derm/resolve/main/tokenizer.json
17.8 MB
- Xet hash:
- 5a26021a76ea1a5c2d354a19158faf325ddbde4f2f3b41ff14195fb7f677ebd5
- Size of remote file:
- 17.8 MB
- SHA256:
- f8104de0e0f9b8ab923ac66b31bee4ae132edf05863545fa4a3b69b4774117ae
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