The GRaPE Family was trained on about 14 billion tokens of data after pre-training. About half was code related tasks, with the rest being heavy on STEAM. Ensuring the model has a sound logical basis. > GRaPE Nano does not have thinking capabilities, primarily in favor of instant responses. GRaPE Flash and Nano are monomodal models, only accepting text. GRaPE Mini being trained most recently supports image and video inputs. I recommend using LM Studio for running GRaPE Models, and have generally found these sampling parameters to work best: Recently there has been a push for smaller and smaller models. GRaPE Nano explores this by performing full finetuning on a 700M model, adapting it to the GRaPE style of outputs. Like GRaPE Flash, GRaPE Nano does not have thinking capabilities. Edge devices are often slow, and it would be worse to make it even slower. GRaPE Flash: Built on the OlMoE` Architecture, allowing for incredibly fast speeds where it matters. Allows for retaining factual information, but lacks in logical tasks. GRaPE Mini: Built on the Qwen3 VL` Architecture, allowing for edge case deployments, where logic cannot be sacrificed. GRaPE Nano: Built on the LFM 2`…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: SL-AI
Теги: lfm2, conversational, en, endpoints_compatible
Лайков: 3 | Загрузок: 26
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
