AI2 Unveils Open-Source Tülu 3 Models, Setting New Standards in Post-Training AI

The Allen Institute for AI releases Tülu 3, a family of instruction-tuned models with fully open data, code, and methodologies aimed at advancing transparency and accessibility in AI development.

• Tülu 3 models, based on Meta’s Llama 3.1 framework, excel in tasks like mathematics, reasoning, and conversational AI, outperforming competitors in key benchmarks.
• The release includes two model sizes (8B and 70B) trained using advanced methodologies such as Sequential Fine-Tuning, Direct Preference Optimization, and Reinforcement Learning with Value Regularization.
• AI2 emphasizes openness by making Tülu 3’s training datasets, evaluation code, and methodologies fully accessible, enabling researchers to replicate or build upon the models.
• The models demonstrate strong safety performance and adaptability for diverse applications, including education, coding, and technical problem-solving, though limitations in safety training are acknowledged.
• This initiative aims to democratize the post-training process, providing tools and insights for creating high-quality, task-specific AI models without reliance on proprietary systems.

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