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* BOOM * cache extra/huggingface/models/ * why max buffer size is not 0 * override MAX_BUFFER_SIZE * less models * remove more models and change cache dir to already cached dir * only metal * less is more? * remove check ops * why is this not setting the ENVVAR * ughhhhh just test in models * only cpu and gpu * only cpu actually * just override it idk * final * move extra dependencies up top * simplification * fix print * make README better * revert ops_disk fix for now * clean up test_onnx * remove testing fashion clip model cuz sloooowwwwww * actually let METAL run this * fix comment mistake * fix download path in run_models * does this work? * cleanup setup and teardown * contextvar like this? * prove model is cached * do I need to increment DOWNLOAD_CACHE_VERSION? * see if cached with incremented DOWNLOAD_CACHE_VERSION * use warnings to see if the model exists * revert DOWNLOAD_CACHE_VERSION stuff and clean up * add retry to download * nit
1.8 KiB
1.8 KiB
HuggingFace ONNX
Tool for discovering, downloading, and validating ONNX models from HuggingFace.
Extra Dependencies
pip install huggingface_hub pyyaml requests onnx onnxruntime numpy
Huggingface Manager (discovering and downloading)
The huggingface_manager.py script discovers top ONNX models from HuggingFace, collects metadata, and optionally downloads them.
# Download top 50 models sorted by downloads
python huggingface_manager.py --limit 50 --download
# Just collect metadata (no download)
python huggingface_manager.py --limit 100
# Sort by likes instead of downloads
python huggingface_manager.py --limit 20 --sort likes --download
# Custom output file
python huggingface_manager.py --limit 10 --output my_models.yaml
Output Format
The tool generates a YAML file with the following structure:
repositories:
"model-name":
url: "https://huggingface.co/model-name"
download_path: "/path/to/models/..." # when --download used
files:
- file: "model.onnx"
size: "90.91MB"
total_size: "2.45GB"
created_at: "2024-01-15T10:30:00Z"
Run Models (validation)
The run_models.py script validates ONNX models against ONNX Runtime for correctness.
# Validate models from a YAML configuration file
python run_models.py --validate huggingface_repos.yaml
# Debug specific repository (downloads and validates all ONNX models)
python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2
# Debug specific model file
python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2/onnx/model.onnx
# Debug with model truncation for debugging and validating intermediate results
DEBUGONNX=1 python run_models.py --debug sentence-transformers/all-MiniLM-L6-v2/onnx/model.onnx --truncate 10