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
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
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.