Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. — For text benchmarks, we report evaluations perforomed by a third party — Artificial Analysis. — For image benchmarks, we report evaluations obtained by https://github.com/open-compass/VLMEvalKit We are a small lab with big goals. While we are not GPU poor, our lab, in comparison has a tiny fraction of the compute available to other Frontier labs. Our goal with this work is to show that a SOTA model can be built with limited resources if you have the right data, design and solid methodology. We set out to build a small but powerful model, aiming for capabilities on par with frontier models. Developing a 15B model with this level of performance requires tradeoffs, so we prioritized getting SOTA-level performance first. Mid-training consists only of CPT and SFT; no RL has been applied. This model performs extensive reasoning by default, allocating extra internal effort to improve robustness and accuracy even on simpler queries. You may notice slightly higher token usage and longer response times, but we are actively working to make it more efficient and concise in future releases. As the upstream PR…
Модальности:
Генерация текста
Области применения:
Диалог / чат
Задача: Генерация текста
Автор: unsloth
Теги: llava, image-text-to-text, unsloth, conversational, endpoints_compatible
Лайков: 3 | Загрузок: 26
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
