Qwen3.5 9B, 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. — 9.7B parameters: the weights this repo quantizes. — Context length: 262,144 tokens (256K), as published by Qwen. — 32 layers: Dense decoder. — 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. — Unified Vision-Language Foundation: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks. — Efficient Hybrid Architecture: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost 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 Qwen3.5 9B chat template is applied. Without it the model…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: AtomicChat
Теги: gguf, atomic-chat, qwen3.5, qwen, llama.cpp, quantized, endpoints_compatible, imatrix
Лайков: 3 | Загрузок: 14,299
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
