GLM-5.3-Flash-REAP50-NVFP4 (v1, superseded; do not use)

The first published NVFP4 build of a 50% expert-pruned zai-org/GLM-5.3-Flash. It is kept only as a record. It carries a known healing defect, and it has never been evaluated or served.

Use GLM-5.3-Flash-REAP50-NVFP4-v2 instead. That repo holds 98.2 GiB of weights with top-1 agreement of 0.839 against the unpruned model, and they have been served on Jetson AGX Thor.

The known defect

This build was quantised from the v1 FP8 master before that master's healing gain was corrected. Every retained expert's output is therefore scaled by a first-moment gain (median 0.69) where the measured value is 0.9111. That makes the MoE pathway about 0.76× too small relative to attention, the shared experts and the residual stream, in all 42 MoE layers. The FP8 master was fixed on 2026-08-28 by rescaling its block scales. This repo was never corrected; the corrected NVFP4 build is v2.

Model overview

Which version should I use

GGUF builds for llama.cpp, made from the v2 FP8 weights, are in GLM-5.3-Flash-REAP50-GGUF; see that card for their details.

Quickstart

There is none. This build has not been loaded by any runtime, and it is not supported by the author's Thor CUDA server, which supports only the v2 NVFP4 weights. The repo also has no preprocessor_config.json or processor_config.json. Use GLM-5.3-Flash-REAP50-NVFP4-v2.

Evaluation

Not evaluated. No teacher-forced or generative evaluation has been run on these NVFP4 weights. The v1 FP8 master (0.8370) was evaluated only after its healing re-fit, so that result does not describe this build, which carries the pre-fix gain.

Hardware and memory

The build machine is an NVIDIA Jetson AGX Thor (128 GB unified memory, sm_110a). These weights have not been run on it or on any other hardware.

How it was made

Same selection as GLM-5.3-Flash-REAP50-FP8 (v1). That is REAP (arXiv:2510.13999) on 256 calibration samples, about 0.52M tokens, keeping 144 of 288 experts in each of the 42 MoE decoder layers, with the MTP block left out. Routed and shared expert weights were then converted to weight-only NVFP4 from the FP8 master, still carrying its first-moment healing gain. The FP8 MLA projections and dense layers 0–2 were dequantised to BF16.

Limitations

• Healing defect (see above): the MoE output is mis-scaled in every MoE layer.
• Unevaluated and never run.
• No MTP block.
• Superseded by v2 on every measured axis.

Changelog and corrections

• 2026-08-28: published. Later that day the card was flagged: this build carries the superseded healing gain, and the FP8 master was corrected instead.
• 2026-11-06: card rewritten and verified against repo files and build artifacts; a 2026-10-26 automated rewrite contained errors.

License and attribution

Released under the MIT licence, the base model's licence (see LICENSE). The base model is GLM-5.3-Flash by Z.ai; all credit for the architecture and pre-training belongs to its authors. The pruning criterion is REAP: Lasby et al., REAP the Experts: Why Pruning Prevails for One-Shot MoE Compression, arXiv:2510.13999.

Citation

@misc{devaney2026glm53reap50,
  title        = {GLM-5.3-Flash-REAP50: 50\% expert-pruned GLM-5.3-Flash in FP8 and NVFP4},
  author       = {Patrick Devaney},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/patrickbdevaney/GLM-5.3-Flash-REAP50-NVFP4}}
}