— KL-divergence: Measures the difference between probability distributions of quantized and original models. Lower is better (closer to original). — Perplexity: Indicates how well the model predicts the next token. Lower values mean better prediction quality. — Top-K agreement: Shows how often the quantized model’s top-K predictions exactly match the original model’s predictions. Higher values indicate better preservation of the original model’s behavior (1.0 = perfect match): — K=1: The fraction of times both models predict the exact same token as their top-1 choice — K=5: The fraction of times both models have the same 5 tokens in their top-5 predictions in the exact same ranking order The official tabbyAPI doesn’t support tool calls for Qwen and GLM models yet. If you’re using Qwen-Code, OpenClaw, or similar software that need tool call support, you can use my fork with the tools-support branch: This branch includes native tool calling support for Qwen and GLM model families.
Модальности:
Генерация текста
Задача: Генерация текста
Автор: NeuroSenko
Теги: exllamav3, exl3
Лайков: 3 | Загрузок: 40
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
