Qwen3.6 35B A3B, self-quantized to GGUF by Atomic Chat. Built straight from Qwen’s original weights with a per-tensor importance matrix, so this is not a repack of somebody else’s files. Runs fully offline. — 36.0B parameters: the weights this repo quantizes. — Context length: 262,144 tokens (256K), as published by Qwen. — 40 layers: Mixture-of-Experts. — Modalities: the base model handles Text, Image; this repo ships text-only quants, it carries no vision projector. — Full imatrix ladder: every quant is calibrated with an importance matrix. — Agentic Coding:: the model now handles frontend workflows and repository-level reasoning with greater fluency and precision. — Thinking Preservation:: we’ve introduced a new option to retain reasoning context from historical messages, streamlining iterative development and reducing overhead. > [!NOTE] > These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model. > [!IMPORTANT] > Always pass —jinja so the Qwen3.6 35B A3B chat template is applied. Without it the model can emit malformed turns. Scores are Qwen’s published results for the base…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: AtomicChat
Теги: gguf, atomic-chat, qwen3.6, qwen, llama.cpp, quantized, endpoints_compatible, imatrix
Лайков: 3 | Загрузок: 5,956
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
