This is a deep comparison of 106B-A12B MoE models, all quantized differently, trained on different data (original, synthetic, RP), and with varying architectural tuning. The goal is to understand: — Which model performs best across benchmarks? — How does quantization affect performance and context? — What’s the trade-off between accuracy, context length, and RAM usage? The LIMI-Air-qx86-hi-mlx quant metrics were not available for this test, but should perform well above all the models shown here. The GLM-Steam models are very close, with qx65g-hi slightly better than qx65g — but both are behind LIMI-Air. The unsloth-GLM-4.5-Air models are the baseline, with qx64 being best among them — but still behind LIMI-Air. The naming convention is critical: — qx5: 5-bit quantization for content with some paths enhanced to 6 bit — g: “enhanced attention paths” — specific to GLM architecture (likely more attention layers enhanced). — hi: high resolution quantization — group size 32. This is a highly optimized quantization for GLM — preserving attention fidelity while compressing embeddings. The key insight: LIMI-Air was trained on synthetic data, which likely: — Boosted generalization —…
Модальности:
Генерация текста
Области применения:
Диалог / чат Вызов функций (Tool use)
Задача: Генерация текста
Автор: nightmedia
Теги: mlx, glm4_moe, agent, tool-use, long-context, conversational, en, 8-bit
Лайков: 3 | Загрузок: 35
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
