Memory Operator is a specialized language model developed for MemOS, designed to handle memory-related operations. Its core capabilities include memory extraction, integration, and update. The primary objectives for developing the Memory Operator sub-model are: 1. Support local-only deployment, enabling the use of MemOS in restricted environments where internet connectivity is unavailable. 2. Achieve memory operations at lower cost and higher speed, while maintaining high system performance. We are releasing the MemOperator model series in three sizes: 4B, 1.7B, and 0.6B parameters. These models are fine-tuned from the Qwen3 series, trained using supervised fine-tuning (SFT) on a combination of human-annotated and model-generated data. They demonstrate excellent performance in tasks such as memory extraction and reorganization. Currently, the memory operation model supports memory extraction and clustering-based memory reorganization within the MemOS system. Conflict resolution and relational reasoning are under active development (WIP). — Type: Causal Language Model (Decoder-only) — Training Stage: Supervised Fine-tuning (SFT) — Supported Languages: English (en), Chinese (zh) -…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: MemTensor
Теги: qwen3, conversational, en, zh, text-generation-inference, endpoints_compatible
Лайков: 3 | Загрузок: 238
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
