AtomicChat/Qwen3.6-27B-MLX-4bit

Base model: Qwen/Qwen3.6-27B

Qwen3.6 27B, self-quantized to MLX by Atomic Chat. Built straight from Qwen's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

27.8B parameters: the weights this repo quantizes.
Context length: 262,144 tokens (256K), as published by Qwen.
64 layers: Dense decoder.
Modalities: Text, Image.
Full imatrix ladder: every quant is calibrated with an importance matrix.
Agentic Coding:: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision.
Thinking Preservation:: we've introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead.

[!NOTE] These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Model Overview

Property	Value
Base model	`Qwen/Qwen3.6-27B`
Parameters	27.8B
Layers	64
Context length	262,144 tokens (256K)
Vocabulary	248,320
Modalities	Text, Image
Architecture	Dense decoder, 24 attention heads over 4 KV heads, `Qwen3_5ForConditionalGeneration`
This repo	MLX weights

Get started

Atomic Chat: search AtomicChat/qwen36-27b-MLX-4bit and hit Use this model.
mlx-lm: mlx_lm.generate --model AtomicChat/qwen36-27b-MLX-4bit --prompt "Hello" --max-tokens 512
Server: mlx_lm.server --model AtomicChat/qwen36-27b-MLX-4bit --port 8080

Best practices

Parameter	Value
temperature	1.0
top_p	0.95
top_k	20
min_p	0.0
repetition_penalty	1.0

Qwen's recommended sampling configuration for Qwen/Qwen3.6-27B.

How these were made

Download Qwen/Qwen3.6-27B (original weights).
Convert and quantize with mlx_lm.convert on our pipeline.

License

Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.

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