Apple Silicon Optimized oQe6 MLX Quantized Release
This repository contains an oQe6 mixed-precision MLX quantized version of Qwythos-9B, optimized for efficient local inference on Apple Silicon devices.
The original Qwythos-9B model was developed by Empero AI. This repository contains an optimized MLX/oQe6 conversion only—no additional fine-tuning or retraining has been performed.
About Qwythos
Qwythos-9B is a full-parameter reasoning model built upon Qwen3.5-9B and trained on over 500 million tokens of carefully curated reasoning data.
The original model specializes in:
🧠 Advanced reasoning
💻 Programming
🛠 Native function calling
🤖 Tool use
🔐 Cybersecurity
🧬 Biomedical reasoning
➗ Mathematics
🔬 Scientific reasoning
📚 Long-context agent workflows
Key capabilities include:
1,048,576 token context window
Native function calling
Excellent coding performance
Strong mathematical reasoning
Tool-assisted self-correction
Long-context understanding
Uncensored technical reasoning
For complete benchmark results, training methodology, and evaluation details, please visit the original repository:
This release uses oQe6 mixed-precision quantization.
Specifications
Format: MLX
Quantization: oQe6
Method: Sensitivity-Aware Mixed Precision
Target Platform: Apple Silicon
Inference: MLX / oMLX
Unlike traditional uniform quantization, oQ dynamically allocates precision according to layer sensitivity, preserving higher precision for the most important weights while aggressively compressing less sensitive regions.
