Concise monthly summary for 2026-03 focusing on business value and technical achievements for AI-Hypercomputer/maxtext. Key features delivered: - Chat Template Token Extraction Compatibility: Implemented robust extraction of completion tokens from chat templates that works with both legacy and modern Hugging Face tokenizers. Added a dedicated extraction function to improve robustness and reduce tokenization errors. Commit: d99f227d139c051004dd320705bd41057cb5b60d. - Distillation Training Improvements and Testing: Added soft distillation (SFT) support to the distillation pipeline; refined handling of target tokens and segmentation masks; strengthened test coverage with unit tests for the distillation step and updated test parameters. Commits: bf2ead81f415fa5eda8abb02a93e43a7773e9716 (sft support in the distill pipeline); 0d80d3d2178376ab9af5afe2389cbdf3343569e3 (fix sft after recent distillation train code refactor); b6bea94d5aca38fb1d89a0b4d680c3e4fb57d772 (added a unit test + format); 5d3683587aa4df7f9a1a6f65963e676a3c4e4c75 (fixed related test). Major bugs fixed: - Fixed completion token extraction when using chat templates, addressing tokenizer compatibility gaps and preventing mis-tokenization across popular Hugging Face tokenizers. - Stabilized distillation tests following a code refactor: updated tests and formats to ensure SFT-related changes are correctly exercised and verified. Overall impact and accomplishments: - Improved model reliability and interoperability across tokenizers, enabling broader use of chat-template based prompts in production. - Enhanced distillation workflow with soft distillation (SFT), leading to better alignment and generalization, plus stronger test coverage to reduce regressions. - Reduced production risk and accelerated experimentation cycles by delivering robust, well-tested features in a single monthly release. Technologies/skills demonstrated: - Python tooling and scripting for tokenizer interoperability and distillation pipelines. - Integration of soft distillation (SFT) into the distillation workflow. - Unit testing and test maintenance in response to code refactors; improved test parametrization. - Code quality, documentation alignment, and change traceability through commit history.

Concise monthly summary for 2025-08 focusing on delivering the Flash Attention kernel for Torchax and JAX with reference implementation and tests, highlighting business value and technical achievements.