Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-VLM-MLX-oQ8

Mixed-precision quantization for Apple Silicon, with vision-language (VLM) capabilities preserved. Highest quality version in the series — closest to BF16 baseline.

Quantized from lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled using oMLX's oQ8 algorithm (sensitivity-aware mixed-precision quantization).

📊 Specs

Field	Value
Base model	Qwen/Qwen3.6-35B-A3B (35B params, 128 experts MoE, A3B activation)
Fine-tune	LoRA distilled from Claude 4.7 Opus reasoning outputs (lordx64 dataset)
Quantization	oMLX oQ8 (mixed-precision, ~8.7 bpw average)
Modality	Vision + Text (VLM)
Format	MLX safetensors
Model size	~35 GB
Inference memory	~39 GB (incl. KV cache and runtime overhead)
Recommended hardware	Apple Silicon M2 Ultra 64GB+ / M3 Max / M5 Max

🚀 Quick Start

Install

pip install mlx-vlm
# Or with uv:
uv tool install mlx-vlm --with torch --with torchvision

Inference (image + text)

mlx_vlm.generate \
  --model wangkezun/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-VLM-MLX-oQ8 \
  --image /path/to/image.jpg \
  --prompt "Describe this image in detail." \
  --max-tokens 256

Python API

from mlx_vlm import load, generate

model, processor = load(
    "wangkezun/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-VLM-MLX-oQ8"
)

output = generate(
    model,
    processor,
    image="/path/to/image.jpg",
    prompt="What's in this image?",
    max_tokens=512,
)
print(output)

OpenAI-compatible Server

mlx_vlm.server \
  --model wangkezun/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-VLM-MLX-oQ8 \
  --port 8080

Drop-in compatible with OpenAI clients including AstrBot, Open WebUI, LibreChat, and Continue.dev.

📈 Measured Performance

Benchmarked on MacBook Pro M5 Max 128GB:

Metric	Value
Prompt processing	~637 tokens/s
Generation speed	~96 tokens/s
Peak memory	38.7 GB
Model load time	~14 sec

When to Choose oQ8 over oQ6

oQ8 retains slightly more precision than oQ6, but the observable quality difference is small for most tasks. Use oQ8 when:

• You need the absolute highest fidelity quantization
• Running quality benchmarks against the BF16 reference
• Memory budget is generous (39+ GB free)

For most users, oQ6 is the better choice — comparable quality at 8 GB less footprint.

🧠 Model Behavior

Inherits the Claude reasoning distillation: the model uses <think>...</think> tags to structure its chain-of-thought before producing the final response.

Best for:

• Multimodal reasoning tasks (image analysis with complex thinking)
• Agentic workflows / tool use
• Quality-sensitive applications where size is not a constraint
• Quantization quality reference / baseline comparisons

🔬 Quantization Details

• Source model: Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled (BF16 MLX-converted)
• Sensitivity model: 8-bit quantization of the same distilled model (self-referenced sens for tight distribution alignment)
• Non-quant weight dtype: bfloat16 (M3+ optimal)
• Text-Only mode: OFF (vision tower preserved)
• Quantizer: oMLX with mlx-vlm conversion path

The vision processor configurations (preprocessor_config.json, video_preprocessor_config.json, processor_config.json) were sourced from the official Qwen base model to ensure proper image input handling — these were missing from the upstream distilled checkpoint.

📦 Other Versions in This Series

Version	Size	Best for
VLM-MLX-oQ4	19.6 GB	Memory-constrained inference
VLM-MLX-oQ6	27 GB	Recommended: best quality/size ratio
VLM-MLX-oQ8	35 GB	Quality reference baseline
Text-MLX-oQ4	19 GB	Text-only, fastest
Text-MLX-oQ6	27 GB	Text-only, balanced
Text-MLX-oQ8	34 GB	Text-only, max quality

Choosing a version:

• Text-only workflows (coding, agents, dialogue) → Text variants are faster and lighter
• Image input needed (OCR, visual analysis, screenshot understanding) → VLM variants
• oQ6 is the sweet spot for most use cases. oQ8 yields diminishing returns relative to its size.

⚠️ Disclaimer

This model derives from a chain of upstream work:

1. Base model Qwen/Qwen3.6-35B-A3B by Alibaba's Qwen team (Apache-2.0)
2. Distilled variant lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled by lordx64 using Claude 4.7 Opus reasoning outputs (Apache-2.0)
3. This quantization by @wangkezun using oMLX on Apple Silicon

This model is not affiliated with or endorsed by Anthropic, PBC. "Claude" is a trademark of Anthropic, PBC. The use of "Claude" in this model name is purely descriptive (nominative fair use) to indicate the upstream training data lineage.

By using this model, you agree to comply with:

• The Apache-2.0 license inherited from the base model
• Any applicable license terms of the upstream distillation dataset
• Local laws and regulations governing AI model usage in your jurisdiction

🙏 Acknowledgments

• Alibaba Qwen Team — for the Qwen3.6-35B-A3B base model
• lordx64 — for the reasoning-focused LoRA distillation
• Jundot (oMLX team) — for the oQ mixed-precision quantization algorithm
• Apple MLX team — for the MLX framework and tooling
• mlx-vlm contributors — for the VLM conversion path

📜 License

Apache-2.0 (inherited from base model).

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Generated: 2026-04-26
Quantizer: @wangkezun