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

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<img src="https://huggingface.co/AtomicChat/qwen36-27b-MLX-4bit/resolve/main/hero.png" alt="Qwen3.6 27B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

<div style="display:flex; justify-content:center; gap:0.5em;"> <a href="https://huggingface.co/Qwen/Qwen3.6-27B"><strong>Base model: Qwen/Qwen3.6-27B</strong></a> </div> </center>

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](https://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

1. 1.Download Qwen/Qwen3.6-27B (original weights).
2. 2.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.