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Cataloged from JetBrains/Qwen3.8-3.6-27B-blend-MTP-MLX-4bit
All models by JetBrains →Unofficial JetBrains derivative. This model was prepared by JetBrains using Qwen models developed by Alibaba Cloud. This release is not affiliated with, sponsored by, or endorsed by Alibaba Cloud or Alibaba Group.
Tasks completed versus output tokens on JetBrains’ 100-task internal coding benchmark. Reasoning settings differ between configurations; see the article for methodology and context.
Read Making Local AI Smarter and Faster for the Junie Local release story, coding evaluations, token-efficiency results, and MTP runtime experiments.
Available formats: BF16 · GGUF · MLX 4-bit · MTP MLX 4-bit.
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: JetBrains/Qwen3.8-3.6-27B-blend, revision f1a19acf58aa8caf7a0a507c9083245f79944dcc.
The base model is a 50/50 combination of Qwen3.6-27B and Qwen3.8-27B, created by linearly interpolating their checkpoint parameters. The configuration, tokenizer, processor, and chat template are retained from Qwen3.8-27B. Exact source revisions and merge settings are recorded in merge-manifest.json [blocked]. See conversion-manifest.json [blocked] for conversion settings and runtime versions.
Matching pair:
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.
Distributed under the Apache License, Version 2.0. The original Qwen LICENSE [blocked], including Copyright 2026 Alibaba Cloud, is retained unchanged.
Copyright © 2026 JetBrains s.r.o. This notice applies only to original material contributed by JetBrains. Upstream Qwen material remains subject to its original copyright and attribution notices.
See NOTICE [blocked] for attribution and CHANGES.md [blocked] for a description of the modifications and the affected files.
This model is distributed under the Apache License, Version 2.0. This notice does not modify the License. Users are reminded that using this model, or its outputs, to infringe the copyright or other intellectual property rights of third parties may violate applicable law, independent of the terms of this License.
No additional training or fine-tuning was performed, and no additional training data was used to create this derivative; its preparation consisted of the checkpoint merge and, where applicable, the extraction, format conversion, and quantization described above and in CHANGES.md [blocked].
For upstream information, see the official Qwen3.6-27B model card and Qwen3.8-27B model card. Qwen also publishes a Training Data Summary, which describes training data for models powering qwen.ai generally; it does not identify the exact training datasets for these two checkpoints.
python -m mlx_vlm generate \ --model JetBrains/Qwen3.8-3.6-27B-blend-MLX-4bit \ --draft-model JetBrains/Qwen3.8-3.6-27B-blend-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}'