— Development: Code assistance and generation — Writing: Content creation and editing — Analysis: Document summarization — Chat: Conversational AI applications RuvLTRA models are fully compatible with TurboQuant — 2-4 bit KV-cache quantization that reduces inference memory by 6-8x with — Hybrid Search — Sparse + dense vectors with RRF fusion (20-49% better retrieval) — Graph RAG — Knowledge graph + community detection for multi-hop queries — DiskANN — Billion-scale SSD-backed
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: ruv
Теги: gguf, ruvltra, sona, adaptive-learning, quantized, turboquant, kv-cache-compression, flash-attention
Лайков: 4 | Загрузок: 58
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
