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The paper introduces Instella, a family of fully open 3B parameter language models trained on publicly available data, addressing the lack of transparency in high-performing LLMs. Instella achieves state-of-the-art performance among fully open models of comparable size, despite using fewer pre-training tokens. The authors also release Instella-Long (128K context) and Instella-Math (reasoning-focused) variants, demonstrating the versatility of the base model.
Forget closed-source: Instella's new family of fully open 3B parameter language models rivals leading open-weight models, proving transparency doesn't have to sacrifice performance.
Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks, yet the majority of high-performing models remain closed-source or partially open, limiting transparency and reproducibility. In this work, we introduce Instella, a family of fully open three billion parameter language models trained entirely on openly available data and codebase. Powered by AMD Instinct MI300X GPUs, Instella is developed through large-scale pre-training, general-purpose instruction tuning, and alignment with human preferences. Despite using substantially fewer pre-training tokens than many contemporaries, Instella achieves state-of-the-art results among fully open models and is competitive with leading open-weight models of comparable size. We further release two specialized variants: Instella-Long, capable of handling context lengths up to 128K tokens, and Instella-Math, a reasoning-focused model enhanced through supervised fine-tuning and reinforcement learning on mathematical tasks. Together, these contributions establish Instella as a transparent, performant, and versatile alternative for the community, advancing the goal of open and reproducible language modeling research.
