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Hugging Face model repos show inflated sizes due to format duplication

By PulseAugur Editorial · [1 sources] ·

A technical analysis reveals discrepancies between the reported file sizes and actual model weights in several Hugging Face repositories. The author found that summing all files labeled as weights in a repository often results in a size several times larger than the single file needed to load the model. This is due to multiple format exports (e.g., PyTorch, TensorFlow, ONNX) of the same weights, or identical weight files listed under different names. Additionally, the analysis highlights issues with how missing 'safetensors' files are reported, with 'null' values potentially being misinterpreted as zero parameters, leading to inaccurate counts for models that do not use that specific format. AI

IMPACT Highlights potential inaccuracies in storage estimates and parameter counts for AI models, impacting cost calculations and resource management.

RANK_REASON Technical analysis of model file storage and reporting practices. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Hugging Face model repos show inflated sizes due to format duplication

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25 / 100
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Technical analysis of model file storage and reporting practices. [lever_c_demoted from research: ic=1 ai=1.0]
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High
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Breaking (< 6h)
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · pm25coder ·

    Eight model repos, added up: the file list and the model disagree

    <h1> Eight model repos, added up: the file list and the model disagree </h1> <p>I wanted one number: how big is this model. Not a parameter count, not a formula — the bytes that land on disk when you pull it down.</p> <p>So I did the dumbest possible thing. I asked eight model re…

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