Instructions to use qhfmshal/TRPaliGemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qhfmshal/TRPaliGemma with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="qhfmshal/TRPaliGemma")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("qhfmshal/TRPaliGemma") model = AutoModelForMultimodalLM.from_pretrained("qhfmshal/TRPaliGemma", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use qhfmshal/TRPaliGemma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qhfmshal/TRPaliGemma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qhfmshal/TRPaliGemma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qhfmshal/TRPaliGemma
- SGLang
How to use qhfmshal/TRPaliGemma 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 "qhfmshal/TRPaliGemma" \ --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": "qhfmshal/TRPaliGemma", "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 "qhfmshal/TRPaliGemma" \ --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": "qhfmshal/TRPaliGemma", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qhfmshal/TRPaliGemma with Docker Model Runner:
docker model run hf.co/qhfmshal/TRPaliGemma
Download tokenizer.json from qhfmshal/TRPaliGemma: direct link, hf CLI and curl.
- Browser
- Download file 17.8 MB
-
https://huggingface.co/qhfmshal/TRPaliGemma/resolve/main/tokenizer.json
- Command line
-
hf download hf://qhfmshal/TRPaliGemma/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/qhfmshal/TRPaliGemma/resolve/main/tokenizer.json
17.8 MB
- Xet hash:
- 097040b2934cd0743ad96b3bf470c2f9dc4ac60a9e5883fa38b097dd515b141f
- Size of remote file:
- 17.8 MB
- SHA256:
- 1df2ab04780faccf51a881d7c5a7026cc6f979083af2eebf709d051b8d47134b
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