0xSero/glm-5.3-reap-fidelity-study

GLM-5.3 REAP Fidelity Study The evidence behind the GLM-5.3 REAP model series: how each pruned cut was measured, and why it prunes the way it does. What we measured, and how Every number here is KL divergence vs the full BF16 model — a token-by-token “how differently do these two models predict the next token” score over a sealed 25-prompt panel across the full 154,880-token vocabulary. 0 = identical, lower = closer. It is a far more sensitive test than benchmark… See the full description on the dataset page: https://huggingface.co/datasets/0xSero/glm-5.3-reap-fidelity-study.

<iframe src="https://silytics.si/embed/item/36e4e5f1-130d-4fe3-bff3-33a24f8874e7" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 18 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.