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samithaj/GLM-4.7-Flash-MTP-bf16
GLM-4.7-Flash-MTP-bf16 is a machine learning model from samithaj. 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 trained multi-token-prediction (MTP / nextn) layer of zai-org/GLM-4.7-Flash, split into a standalone MLX drafter checkpoint. This is not a standalone language model — it is a single-layer draft head that predicts…
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.safetensors2.6 GB · 99%
How the weights are stored.
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From the Hugging Face model README
The trained multi-token-prediction (MTP / nextn) layer of zai-org/GLM-4.7-Flash, split into a standalone MLX drafter checkpoint. This is not a standalone language model — it is a single-layer draft head that predicts one token ahead from a target model's hidden states, for speculative decoding against GLM-4.7-Flash.
GLM-4.7-Flash ships this layer inside the full checkpoint at
model.layers.47.*; MLX conversions of the base model strip it (sanitize
drops layers past num_hidden_layers), so quantized community conversions do
not carry it. This repo preserves it in bf16, revision-pinned.
Source: zai-org/GLM-4.7-Flash, revision
7dd20894a642a0aa287e9827cb1a1f7f91386b67 (MIT). All weights are Z.ai's
trained parameters, unmodified except for the layout transforms below.
Tool: the glm4_moe_lite_mtp drafter split from mlx-vlm's
speculative/drafters convention
(Blaizzy/mlx-vlm#1570):
python -m mlx_vlm.speculative.drafters.glm4_moe_lite_mtp.split \
--model zai-org/GLM-4.7-Flash \
--revision 7dd20894a642a0aa287e9827cb1a1f7f91386b67 \
--output GLM-4.7-Flash-MTP-bf16
Only the 3 (of 48) source shards holding the nextn tensors are read.
Checksum:
| file | sha256 |
|---|---|
model.safetensors | bdb948cbec1810d89910cc99dfbc85864acde45241337702b555e2a721254e7b |
model_type: glm4_moe_lite_mtp, block_size: 2
(num_nextn_predict_layers + 1), untied embeddings; the source text config is
nested under text_config. 24 tensors, flat post-sanitize layout:
embed_tokens and untied lm_head (GLM's nextn head is not
tied to the target, unlike DeepSeek/Qwen MTP)enorm / hnorm / eh_proj projectionskv_b_proj split into
embed_q / unembed_out)switch_mlp + shared expert; the noaux_tc
router correction bias is kept fp32 and the router gate full precision
(casting them breaks routing)glm4_moe_lite backbone
lands thereMeasured offline acceptance of this head replaying real target hidden states: ~0.806 mean over chat prompts (methodology and end-to-end results in the vllm-metal links above).
A 4-bit variant is at samithaj/GLM-4.7-Flash-MTP-4bit.