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: 3,805 Bytes
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"compute": {
"environment": "Linux x86_64 CPU with mlx[cpu]; no paid cloud Mac used.",
"estimated_incremental_cost_usd": 0.0
},
"end_to_end_extraction": {
"chunk_lengths": [
50
],
"input_html": "<html><body><article><h1>Apple MLX conversion</h1><p>This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"num_blocks": 2,
"variants": {
"4bit": {
"html": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"labels": {
"1": "main",
"2": "main"
},
"latency_ms": 77852.023473999,
"markdown": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"non_empty": true,
"predictions": [
1,
1
]
},
"8bit": {
"html": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"labels": {
"1": "main",
"2": "main"
},
"latency_ms": 78333.6143399938,
"markdown": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"non_empty": true,
"predictions": [
1,
1
]
},
"bf16": {
"html": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"labels": {
"1": "main",
"2": "main"
},
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"markdown": "<html><body><article><h1 _item_id=\"1\">Apple MLX conversion</h1><p _item_id=\"2\">This article explains how to convert a EuroBERT content extraction model to MLX format.</p></article></body></html>",
"non_empty": true,
"predictions": [
1,
1
]
}
}
},
"load_checks": {
"4bit": {
"loaded": true,
"logits_dtype": "mlx.core.bfloat16",
"logits_shape": [
1,
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2
]
},
"8bit": {
"loaded": true,
"logits_dtype": "mlx.core.bfloat16",
"logits_shape": [
1,
3,
2
]
},
"bf16": {
"loaded": true,
"logits_dtype": "mlx.core.bfloat16",
"logits_shape": [
1,
3,
2
]
}
},
"model_dir": "hf_out",
"numerical_accuracy": {
"test_inputs": [
"A",
"B",
"C"
],
"token_shape": [
3,
2
],
"torch_reference": {
"attention": "eager",
"dtype": "float32",
"latency_ms": 50.09009799687192
},
"variants": {
"4bit": {
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"max_abs_diff": 2.2551989555358887,
"mean_abs_diff": 1.1897996664047241
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"8bit": {
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"bf16": {
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}
}
},
"source_model": "feyninc/pulpie-orange-small",
"variants": [
"bf16",
"8bit",
"4bit"
]
}
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