MaxText Broadens Post-Training Features: SFT and RL Now Available on Single-Host TPUs – Google Developers Blog Suntem cei care susţin dezvoltarea Wei Wei. Weiren Yu Product Manager. In the fast-changing world of large language models (LLMs), pre-training represents just the initial stage. Post-training is crucial for converting a base model into a specialized assistant or a high-performing reasoning system. Today, we’re thrilled to introduce new capabilities in MaxText that simplify this workflow: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) are now supported on single-host TPU setups (including v5p-8 and v6e-143). Powered by JAX and the Tunix library, MaxText delivers a high-performance, scalable solution for developers to optimize their models with cutting-edge post-training methods. Explore the complete SFT and RL documentation to begin your post-training journey on TPUs right away. Supervised Fine-Tuning (SFT): Precision Tuning Made Simple. The main approach for tailoring a pre-trained model to adhere to particular instructions or perform exceptionally well on specialized tasks is Supervised Fine-Tuning.
Google Developers Blog