Qwen3.5-27B-Claude-Opus-4.6-High-Reasoning-MLX-4bit

A distilled version of Qwen3.5-27B, enhanced with Claude Opus 4.6 reasoning patterns through knowledge distillation.

Model Details

• Original Model: Qwen/Qwen3.5-27B
• Quantization: 4-bit (~4.501 bits per weight)
• Group Size: 64
• Format: MLX SafeTensors
• Framework: mlx-lm

Evaluation Results

• Evaluator: Qwen3-Coder-Next
• Test Samples: 260 (based on personal dataset, for reference only)
• Date: 2026-03-29

Overall Performance

Metric	Base Model	Distilled Model	Improvement
Win Rate	25.77%	73.85%	+48.08%
Avg Latency (s)	71.70	68.47	-4.5%

Score Breakdown (10-point scale)

Dimension	Base Model	Distilled Model	Improvement
Accuracy	6.35	8.59	+35.3%
Logic	6.47	8.69	+34.3%
Completeness	5.70	8.82	+54.7%
Clarity	6.51	8.43	+29.5%
Actionability	5.85	8.56	+46.3%

Performance by Category

Category	Base Win Rate	Distilled Win Rate	Samples
debug	22.67%	76.00%	75
design	51.32%	48.68%	76
prompt	5.80%	94.20%	69
reasoning	17.50%	82.50%	40

Quick Start with oMLX

Installation

brew tap jundot/omlx https://github.com/jundot/omlx
brew install omlx

Alternatively, you can download the DMG version here:

https://github.com/jundot/omlx/releases/tag/v0.3.0

In another terminal, use the API

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "default",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "What is the capital of France?"}
        ],
        "temperature": 0.7,
        "max_tokens": 256
    }'

License

This model is licensed under Apache-2.0.

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This model was distilled to capture Claude Opus 4.6's reasoning capabilities while maintaining Qwen3.5-27B's efficiency and multilingual support. Recommended for inference with vLLM for optimal performance.