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. — 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. — Higher reasoning quality — Better coding performance — Lower memory usage — Faster inference — Excellent Apple Silicon efficiency — Software Engineering — AI Coding Assistants — Long Context Analysis — Scientific Research — Mathematical Reasoning — Cybersecurity — Biomedical Analysis — Local AI Agents — Tool Calling Applications — Research & Education oQ is a sensitivity-aware mixed-precision quantization technique designed to maximize model quality while significantly reducing memory usage. Instead…
Модальности:
Генерация текста Анализ изображений Компьютерное зрение
Области применения:
Логика и рассуждение Диалог / чат Вызов функций (Tool use)
Задача: Генерация текста
Автор: yugeshkarunamurthy
Теги: mlx, qwen3_5, qwen3.5, reasoning, uncensored, long-context, 1M-context, function-calling
Лайков: 3 | Загрузок: 177
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
