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avlp12/GLM-5.3-Flash-Alis-MTP-Drafter
GLM-5.3-Flash-Alis-MTP-Drafter is a machine learning model from avlp12. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as mit.
The standalone native MTP (nextn) drafter of GLM-5.3-Flash, extracted from the official FP8 release (84c6a6aa) for self-speculative decoding with mlx-vlm PR 2044. To our knowledge the first standalone GLM-5.3-Flash MT…
Downloads · 30 days
335
23% of all-time downloads
All-time downloads
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7.4B
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.safetensors14.9 GB · 100%
How the weights are stored.
BF167.4B · 100%
From the Hugging Face model README
The standalone native MTP (nextn) drafter of GLM-5.3-Flash,
extracted from the official FP8 release (84c6a6aa) for self-speculative decoding with
mlx-vlm PR #2044. To our knowledge the first
standalone GLM-5.3-Flash MTP drafter on the Hub — normal MLX conversions drop layer 45, so
quantized targets otherwise have no MTP path.
mtp.*), bf16 (+ fp32 router correction bias), model_type: glm5_next_mtp, block size 2.Glm5NextMTPDraftModel via load_drafter(path, kind="mtp") on the PR #2044/#2074 branches.The PR branch's splitter currently writes raw FP8 + unstacked weight_scale_inv tensors from the
FP8 source and drops the quantization config, producing an unloadable artifact (reported upstream:
#2044 comment). This repo
applies the reference blockwise dequant (block 128, mx.from_fp8, per-expert scale grids rejoined
across the stacked switch_mlp) to publish a clean bf16 drafter. Receipts in-repo:
SPLIT_RECEIPT.json (extraction) and DEQUANT_RECEIPT.json (post-process).
from mlx_vlm.speculative.drafters import load_drafter
drafter, kind = load_drafter("avlp12/GLM-5.3-Flash-Alis-MTP-Drafter", kind="mtp")
MIT, derived from the MIT base release.