license: apache-2.0
base_model: AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16
library_name: mlx
tags:
- mlx
- mlx-vlm
- qwen3_5_mtp
- qwen3_5
- qwen3.8
- mtp
- multi-token-prediction
- speculative-decoding
- self-speculation
- draft-model
- apple-silicon
Qwen3.8-27B-AEON MTP Drafter (MLX)
The native multi-token-prediction head of AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16 (revision 8f76e82), split into the standalone MLX drafter format that mlx-vlm expects for mtp speculative decoding. 15 tensors, BF16, 810 MB, block_size: 3.
AEON's BF16 grafts the MTP head back from stock Qwen3.8 (15 tensors, hash-matched), so this drafter is the original Qwen-trained head — not retrained.
Companion to intheblue/Qwen3.8-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-6bit; works with any MLX quantization of the same base.
How it was made
python -m mlx_vlm.speculative.drafters.qwen3_5_mtp.split \
--model AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-BF16 \
--output Qwen3.8-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter
(Use the official split tool — hand-extracting mtp.* tensors produces a broken drafter with 0% acceptance: the tool applies the RMSNorm weight-convention shift and stamps format: mlx metadata.)
Usage
python -m mlx_vlm generate \
--model intheblue/Qwen3.8-27B-AEON-Ultimate-Uncensored-Multimodal-MLX-6bit \
--draft-model intheblue/Qwen3.8-27B-AEON-Ultimate-Uncensored-MLX-MTP-Drafter \
--draft-kind mtp --draft-block-size 3 \
--prompt "..."
Speculative decoding is lossless — rejected drafts fall back to the target model's own tokens.
Measured effect
Test machine: Mac mini M4 Pro, 48 GB, with the companion 6-bit target model. Speedups carry across chips since the drafter's relative overhead is small.
| Workload | Serial | With drafter |
|---|---|---|
| Coding (temp 0.2, block 4) | 11.4 tok/s | 21.5 tok/s (1.90×) |
| Doc QA @ 13k ctx (block 3) | ~11 tok/s | 16.9 tok/s |
| Creative prose (temp 0.7, block 3) | 11.4 tok/s | 15.9 tok/s (1.4×) |
Block size 3 is the all-round sweet spot; block ≥5 regresses (draft accuracy decays past 2–3 tokens). Acceptance ~46% on open prose, higher on structured output.