OsaurusAI/Qwen3.8-27B-JANG_2D

The smallest tier — a 2-bit-class mixed allocation for tight memory budgets.

A JANG bundle of Qwen/Qwen3.8-27B — Qwen's 27B dense hybrid (GatedDeltaNet + gated attention) vision-language model with flexible thinking control — quantized for Apple Silicon / MLX and runnable with stock mlx_vlm. Text, image and video paths are all present in this exact bundle, with the model's native multi-token-prediction head preserved.

This is the aggressive tier. It is meaningfully lossier than JANG_4D — the numbers below are stated plainly so you can choose with your eyes open. If you have the memory, take 4D.

Why this quant

• Measured allocation, not name rules — every quantized module got its bit width from a Hessian-trace sensitivity capture (tr(H)·‖W‖²_F) over a 164,105-token, 8-domain calibration corpus, so bits go where this model actually needs them.
• AWQ applied (α=0.25), folded into the producing RMSNorm using this family's zero-centered (+1) convention. Measured with an identical bit map and pipeline, AWQ is worth ~20 % lower KL at identical bits (0.01002 vs 0.01245).
• imatrix refit on every module ≤ 8 bits — activation-weighted least squares against the same capture, at zero size cost. The refit re-applies the AWQ scales, so it does not silently revert them.
• fp16 where quantization would lie — the 27 vision-block linear_fc2 projections (in_features 4304, indivisible by any MLX quant group) pass through in fp16 rather than being force-fit.
• The full serving contract is stamped, not documented-elsewhere — sampling presets, reasoning-effort tiers, thinking defaults, EOS pair and context guidance are all machine-readable in the bundle.

Measured quality

Scored against the bf16 source (not against another quant) on 24 held-out prompts that are disjoint from the calibration corpus, teacher-forced on the reference's greedy continuation.

Median KL is reported rather than mean: against a near-deterministic reference continuation KL is unbounded, so a single low-entropy prompt dominates a mean.

The lineup

JANG_2D (10.6 GiB) · JANG_4D (16.6 GiB) · JANG_6D (23.6 GiB) · MXFP8 (26.4 GiB)

Model + bundle facts

Serving contract (stamped in the bundle)

Read these from generation_config.json + jang_config.json rather than re-deriving them:

• Thinking ON by default — temperature=1.0, top_p=0.95, top_k=20. This is the agentic preset and the correct preset for coding agents. Instruct / non-thinking preset: temperature=0.7, top_p=0.80, top_k=20, presence_penalty=1.5.
• reasoning_effort: low / medium / xhigh (default xhigh), carried as a chat-template kwarg.
• preserve_thinking ON by default — Qwen3.8 retains reasoning context across turns, and it is prefix-cache friendly.
• Reasoning OFF = prefilled closed <think>\n\n</think>\n\n, never plain omission. Reasoning parser qwen3; tool-call parser qwen3_coder (XML function dialect).
• Stop on both EOS ids 248046 and 248044.
• Recommended output budget: up to 262,144 reasoning + 131,072 final tokens.

Use it

pip install -U mlx-vlm

from mlx_vlm import load, generate

model, processor = load("OsaurusAI/Qwen3.8-27B-JANG_2D")

out = generate(model, processor, "Describe this image.", image=["photo.png"],
               max_tokens=512, temperature=1.0, top_p=0.95)

Video note: render video prompts through the bundle's own chat template ({"type": "video"} → <|vision_start|><|video_pad|><|vision_end|>); mlx_vlm.prompt_utils.apply_chat_template silently drops video items.

MTP head

The native MTP head is preserved as its own shard (31 tensors). Depth-1 draft acceptance was measured at 85.9 % on the model's own generated span — i.e. the span speculative decoding actually drafts, not the prompt.

Speculation depth. Measured against a warm KV cache: best_depth is 1 at 48.5 tok/s (depth 1 = 48.5 tok/s). Deeper speculation stops paying fast — each extra verified token adds ~20% to the target decode step, so depth 3 is a net loss on every tier. The bundle stamps the measured depth in vmlx_mtp_tuning.json with its baseline/best/speedup evidence.

Head width was measured to be irrelevant to acceptance between 4-bit gs64 and 8-bit gs128 (spread inside one standard error, and non-monotonic), so the head is stored at the cheapest width that costs nothing.

Acceptance is strongly shape-dependent — it moves ~17 points across reasoning/tool/turn-shape changes, and structured tool output drafts far better than prose. Treat a single acceptance figure as one slice, not a guarantee.

Credits

Quantized by Jinho Jang — eric@osaurus.ai