Token Classification
MLX
eurobert
html
content-extraction
boilerplate-removal
web-scraping
encoder
custom-code
custom_code
Instructions to use Mike0021/pulpie-orange-small-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Mike0021/pulpie-orange-small-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir pulpie-orange-small-mlx Mike0021/pulpie-orange-small-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 850 Bytes
e4ab6c6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | {
"architecture": "EuroBertForTokenClassification",
"format": "mlx",
"implementation": "modeling_eurobert_mlx.py",
"source_model": "feyninc/pulpie-orange-small",
"variants": {
"model-4bit.safetensors": {
"description": "MLX affine weight quantized 4-bit variant.",
"dtype": "quantized",
"quantization": {
"bits": 4,
"group_size": 64,
"mode": "affine"
}
},
"model-8bit.safetensors": {
"description": "MLX affine weight quantized 8-bit variant.",
"dtype": "quantized",
"quantization": {
"bits": 8,
"group_size": 64,
"mode": "affine"
}
},
"model-bf16.safetensors": {
"description": "Native 16-bit BF16 MLX weights converted from the source safetensors.",
"dtype": "bfloat16",
"quantization": null
}
}
}
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