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Model cards say what a model is: a fine-tune of X, a quantization of Y. I built Weightprint to check that against the weights.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 7% of the declared fine-tunes we could compare (1,630 of 22,847) hold their parent's weights unchanged. (Corrected from 12%: quants whose values the engine doesn't decode, like bitsandbytes and FP8, had been counted as unchanged. Thanks @dipankarsarkar for the question that caught it.)
- 27% of the fine-tunes we could compare are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 28% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
App: tardellirs/weightprint
Data (every pair, and the copy groups): tardellirs/weightprint
It reads the header and a few bytes of every tensor (no downloads) and compares a model with the parent its card declares. 120,976 models so far. Some of what came out:
- 7% of the declared fine-tunes we could compare (1,630 of 22,847) hold their parent's weights unchanged. (Corrected from 12%: quants whose values the engine doesn't decode, like bitsandbytes and FP8, had been counted as unchanged. Thanks @dipankarsarkar for the question that caught it.)
- 27% of the fine-tunes we could compare are LoRA-style: only the attention and MLP matrices moved, norms and embeddings untouched. That share went from 10% in 2023 to 28% in 2026.
- Of 1,668 fine-tunes named abliterated, uncensored or heretic, 53% edit only the matrices that write to the residual stream, the signature of an abliteration. The rest were trained further, merged, or are the parent's weights.
- 51% of the weight bytes on the Hub are byte-for-byte repeats of another repo's files.
Search any Hub id to see which layers and parts changed against its parent, or compare any two models.
A sample per tensor can miss things, and some formats can't be compared yet. If a verdict on a model you know looks wrong, tell me here and I'll check it against the engine. That's what this post is for, before a longer write-up.
