Instructions to use pszemraj/nougat-base-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/nougat-base-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="pszemraj/nougat-base-onnx")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/nougat-base-onnx") model = AutoModelForMultimodalLM.from_pretrained("pszemraj/nougat-base-onnx", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pszemraj/nougat-base-onnx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pszemraj/nougat-base-onnx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pszemraj/nougat-base-onnx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pszemraj/nougat-base-onnx
- SGLang
How to use pszemraj/nougat-base-onnx 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 "pszemraj/nougat-base-onnx" \ --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": "pszemraj/nougat-base-onnx", "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 "pszemraj/nougat-base-onnx" \ --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": "pszemraj/nougat-base-onnx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pszemraj/nougat-base-onnx with Docker Model Runner:
docker model run hf.co/pszemraj/nougat-base-onnx
Download decoder_model.onnx from pszemraj/nougat-base-onnx: direct link, hf CLI and curl.
- Browser
- Download file 1.1 GB
-
https://huggingface.co/pszemraj/nougat-base-onnx/resolve/main/decoder_model.onnx
- Command line
-
hf download hf://pszemraj/nougat-base-onnx/decoder_model.onnx
-
curl -L -o decoder_model.onnx https://huggingface.co/pszemraj/nougat-base-onnx/resolve/main/decoder_model.onnx
1.1 GB
- Xet hash:
- 51a5d8742d57c62a3a7778029cb4750f67c023e38de4f72e5ebf82f95565d8e8
- Size of remote file:
- 1.1 GB
- SHA256:
- 03e2b2e5026866854b810e886c03d33e675ee309b7d873b3c8fef59b96dd7f7d
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