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vvsotnikov/Qwen3.8-27B-test-MTP-MLX-4bit
Qwen3.8-27B-test-MTP-MLX-4bit is a machine learning model from vvsotnikov. 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-vlm. The card lists the license as apache-2.0.
Native MTP drafter extracted from the original BF16 checkpoint and quantized to 4-bit. It requires the matching main model and cannot generate independently. All 7 RMSNorm offsets were verified against the source.
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From the Hugging Face model README
Native MTP drafter extracted from the original BF16 checkpoint and quantized to 4-bit. It requires the matching main model and cannot generate independently. All 7 RMSNorm offsets were verified against the source.
Source: vvsotnikov/Qwen3.8-27B-test, revision 13171b198fbeb3a2ecc5aaabe0d3ffd2fc8a39fa.
The source is a 50/50 linear merge of Qwen3.6-27B and Qwen3.8-27B. See merge-manifest.json for the original merge provenance and conversion-manifest.json for conversion settings and runtime versions.
Matching pair:
python -m mlx_vlm generate \
--model vvsotnikov/Qwen3.8-27B-test-MLX-4bit \
--draft-model vvsotnikov/Qwen3.8-27B-test-MTP-MLX-4bit \
--draft-kind mtp \
--prompt "Write a Python function that merges two sorted lists." \
--max-tokens 2048 --enable-thinking --temperature 1.0 \
--gen-kwargs '{"top_p":0.95,"top_k":20}'
Validated with MLX-VLM 0.6.15 (revision 20eec6cb5564c6a196b046d869d2081c29e3ff92), MLX 0.32.0, and Transformers 5.14.0 on an Apple M5 Max.
Structural checks passed for quantization, tensor inventory, main/drafter compatibility, and MTP norm offsets. A text smoke test generated a complete Python answer (1,863 tokens, 82.1% draft acceptance). An image smoke test correctly identified a red square and blue circle (234 tokens, 83.9% draft acceptance). Both used thinking enabled, temperature 1.0, top-p 0.95, top-k 20, and seed 42. These are smoke checks, not a quality benchmark or a comparison against BF16.