Qwen3.5-9B-MLX-4bit

<center> <div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;"> <a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a> <a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a> <a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a> </div> <br/> <img src="https://huggingface.co/AtomicChat/Qwen3.5-9B-MLX-4bit/resolve/main/hero.png" alt="Qwen3.5 9B" 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/Qwen3.5-9B"><strong>Base model: Qwen/Qwen3.5-9B</strong></a> </div> </center>

Qwen3.5 9B, 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

• 9.7B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by Qwen.
• 32 layers: Dense decoder.
• Modalities: Text, Image.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
• Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost 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/Qwen3.5-9B-MLX-4bit and hit Use this model.
• mlx-lm: mlx_lm.generate --model AtomicChat/Qwen3.5-9B-MLX-4bit --prompt "Hello" --max-tokens 512
• Server: mlx_lm.server --model AtomicChat/Qwen3.5-9B-MLX-4bit --port 8080

Best practices

Qwen's recommended sampling configuration for Qwen/Qwen3.5-9B.

How these were made

1. Download Qwen/Qwen3.5-9B (original weights).
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.