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CosmicRaisins/GLM-5.2-MTP-INT4-15pct
GLM-5.2-MTP-INT4-15pct is a text generation model from CosmicRaisins. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Separate-draft MTP (multi-token-prediction) head for speculative decoding with CosmicRaisins/GLM-5.2-AWQ-INT4-15pct. cyankiwi/GLM-5.2-AWQ-INT4 drops GLM-5.2's native MTP layer, so this reconstructs it as a standalone…
Downloads · 30 days
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How the weights are stored.
I328.7B · 96%
From the Hugging Face model README
Separate-draft MTP (multi-token-prediction) head for speculative decoding with
CosmicRaisins/GLM-5.2-AWQ-INT4-15pct. cyankiwi/GLM-5.2-AWQ-INT4 drops GLM-5.2's
native MTP layer, so this reconstructs it as a standalone INT4 draft that vLLM
loads via --speculative-config (which also sidesteps vLLM #35041 / #38494). INT4
compressed-tensors, single MTP layer, 218 experts to match the pruned target. ~4.8 GB.
--speculative-config '{"model":"CosmicRaisins/GLM-5.2-MTP-INT4-aligned","method":"mtp","num_speculative_tokens":3,"attention_backend":"FLASHMLA_SPARSE"}'
k=3 benched best for me on a synthetic corpus; Z.ai recommends k=5, and I haven't compared them in real-world use yet. Serving stack: github.com/CosmicRaisins/glm-5.2-gb10.
The MTP weights trace to GLM-5.2's native MTP head (Z.ai, MIT), sourced via
0xSero's NVFP4 layer-78 (0xSero/GLM-5.2-NVFP4-REAP-469B), then dequantized,
re-quantized to INT4, expert-pruned to 218, and aligned to the DeepSeekMTP
loader layout. The reconstruction and alignment are mine.
License MIT, inherited from the GLM-5.2 base: the MTP weights carry GLM-5.2's MIT grant through 0xSero's quantization (0xSero's repo declares no explicit license). Attributed to Z.ai and 0xSero; not affiliated with either.