The first iMatrix GGUF for Qwable-3.6-27b — Q2K through Q80 with importance matrix calibration. Qwable-3.6-27b is a fine-tune of Qwen 27B trained on Fable 5-style reasoning traces — it thinks before answering, with structured deliberate responses optimized for code and technical tasks. These GGUFs are produced from the F16 source using importance matrix (iMatrix) calibration on 2M tokens of wikitext-103. iMatrix identifies which weights matter most during inference and protects them during quantization — the result is noticeably better coherence at Q2/Q3/Q4. Standard quantization rounds all weights equally. iMatrix: 1. Runs calibration text through the full-precision model 2. Measures which weights activate most (the «importance matrix») 3. Allocates more precision to important weights, less to unimportant ones — Original: Mia-AiLab/Qwable-3.6-27b — 22.9K downloads — Architecture: Qwen3.5 27B fine-tuned on Fable 5 reasoning traces — Strengths: Code, debugging, technical reasoning, structured tasks — License: MIT
Модальности:
Генерация текста
Области применения:
Генерация кода Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: liodon-ai
Теги: gguf, ollama, imatrix, qwen3, qwable, local-llm, llama.cpp, lm-studio
Лайков: 3 | Загрузок: 289
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
