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Qwen2.5-0.5B parameter count discrepancy highlights reporting nuances

By PulseAugur Editorial · [1 sources] ·

A recent analysis of the Qwen2.5-0.5B model revealed discrepancies in parameter counts reported by Hugging Face versus the model's internal configuration. The model card and checkpoint files indicate different parameter numbers due to how tied embeddings are handled, where the same matrix is used for both input and output layers. This leads to an architectural count that is higher than the stored count, with the difference representing 27.6% of the model's total parameters. The analysis also touched upon Mixture-of-Experts (MoE) models like Mixtral 8x7B, noting that their reported parameter counts can be misleading as they describe the architecture rather than the actual storage or active parameters per token. AI

IMPACT Highlights the importance of understanding different parameter count metrics for LLMs, impacting model size estimations and comparisons.

RANK_REASON Analysis of model parameter reporting and discrepancies. [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 →

Qwen2.5-0.5B parameter count discrepancy highlights reporting nuances

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26 / 100
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Analysis of model parameter reporting and discrepancies. [lever_c_demoted from research: ic=1 ai=1.0]
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model release
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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 ·

    The model card said 0.5B. The checkpoint stores 494M. Both are right.

    <p>Qwen2.5-0.5B's config declares its vocabulary-by-hidden matrix twice: once as the embedding table, once as the output head. Its checkpoint stores that matrix once. Both statements are true, and the distance between them is 136,134,656 parameters, which is 27.6% of everything i…

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