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-4bitand 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.converton our pipeline.
License
Original model by Qwen, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.