!License !Model Size !Training Time !Hardware !Context Length Stentor-30M is a highly compact, efficient language model built on the Llama architecture. Designed for speed and low-resource environments, this ~30.4M parameter checkpoint utilizes a mixed-precision training pipeline and is best treated as a base next-token predictor (not a chat assistant). It does not «understand» text in a human sense and is not trained to reliably follow instructions. While the tokenizer may include special tokens/templates that resemble instruction or tool formats, the model itself is not instruction-tuned and will often generate plausible but off-topic text. It serves as an accessible entry point for researching attention mechanisms and testing training pipelines on consumer hardware. > ⚠️ Important Limitations > > — Context Window: Maximum 512 tokens (very short) > — Not Instruction-Tuned: May ignore prompts or respond off-topic > — Stopping / EOS: Sometimes stops on its own, but it’s rare; always set maxnewtokens > — Tokenizer ≠ Capability: «tool/function» tokens do not imply real tool use > — No Safety Tuning: Base model without RLHF or safety alignment > — Limited Knowledge: 30M parameters =…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: StentorLabs
Теги: llama, small-language-model, efficient, edge-deployment, speculative-decoding, tiny-model, 30m-parameters, kaggle-trained
Лайков: 3 | Загрузок: 188
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
