qwen3_5_0_8b_optiq_4bit

mlx-community/Qwen3.5-0.8B-OptiQ-4bit

Optimized for Apple Silicon with mlx-optiq — sensitivity-aware mixed-precision quantization, reusable at inference, fine-tuning, and serving time.

A 4-bit mixed-precision MLX quant of Qwen/Qwen3.5-0.8B. Per-layer bit-widths come from a KL-divergence sensitivity pass on the bundled optiq.jsonl five-domain calibration mix (prose · reasoning · code · agent · tool-call). Sensitive layers go to 8-bit; robust ones stay at 4-bit. The on-disk size is within ~5 % of a stock uniform 4-bit MLX quant.

Quantization details

We follow the same naming convention llama.cpp uses for Q4_K_M and similar mixed-precision quants: the "4-bit" label is for the predominant precision, not the weighted average. The mixed allocation is what lets this build beat stock uniform-4-bit at the same disk size — see the benchmark deltas below.

Usage

Load it with mlx-lm and use it as usual:

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Qwen3.5-0.8B-OptiQ-4bit")
response = generate(
    model, tokenizer,
    prompt="Explain quantum computing in simple terms.",
    max_tokens=200,
)

For more — mixed-precision KV-cache serving, sensitivity-aware LoRA fine-tuning, the OpenAI + Anthropic-compatible inference server, hot-swap mounted adapters, sandboxed Python execution for agent workflows — install mlx-optiq:

pip install mlx-optiq

See the Qwen3.5 family guide on mlx-optiq.com for sampling defaults, training recipes, and family-specific caveats.

Benchmarks

Five-metric suite that drives the Capability Score:

The Capability Score is the simple unweighted mean of the five benchmarks — every metric gets one equal vote. Disk size is reported next to it as an honest second axis instead of being folded into the score. See the eval-framework writeup for the full methodology.

Links

• Project website: mlx-optiq.com
• Qwen3.5 family guide: mlx-optiq.com/docs/qwen3.5
• PyPI: pypi.org/project/mlx-optiq
• Calibration mix: mlx-optiq.com/blog/calibration-mix
• Eval framework: mlx-optiq.com/blog/eval-framework
• Base model: Qwen/Qwen3.5-0.8B

License

Apache 2.0 (inherits from base model).