Instructions to use avlp12/GLM-5.3-Flash-Alis-MTP-Drafter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use avlp12/GLM-5.3-Flash-Alis-MTP-Drafter with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir GLM-5.3-Flash-Alis-MTP-Drafter avlp12/GLM-5.3-Flash-Alis-MTP-Drafter
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
GLM-5.3-Flash-Alis-MTP-Drafter (bf16)
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.
- 14 GB, 29 tensors (
mtp.*), bf16 (+ fp32 router correction bias),model_type: glm5_next_mtp, block size 2. - Loads as
Glm5NextMTPDraftModelviaload_drafter(path, kind="mtp")on the PR #2044/#2074 branches. - Measured (M3 Ultra 512GB, 4-bit-class target, ctx 512, greedy): 1.12× decode, bit-identical to plain decode, ≈89% single-draft acceptance.
Why a dequantized extraction
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.
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Quantized
Model tree for avlp12/GLM-5.3-Flash-Alis-MTP-Drafter
Base model
zai-org/GLM-5.3-Flash