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T5X
Modular JAX framework for training and serving large language models
Apache-2.0
ABOUT
Training large language models at scale requires complex distributed infrastructure — sharding model parameters across TPU pods, managing training checkpoints, implementing mixture-of-experts routing, and supporting diverse evaluation protocols. T5X solves this by providing a modular, production-tested JAX framework that handles TPU-parallel training, checkpointing, few-shot evaluation, and mixture-of-experts architectures with standardized configuration.
INTEGRATION GUIDE
1. Pre-train large language models at scale across hundreds of TPU accelerators with data and model parallelism
2. Fine-tune pre-trained LLMs on downstream tasks with few-shot evaluation and chain-of-thought prompting support
3. Train mixture-of-experts models with sparse routing for efficient capacity scaling
4. Evaluate model performance on standard benchmarks with configurable in-context learning and prompting strategies
5. Serve trained models for production inference with optimized TPU-compatible serving pipelines
TAGS
llm-trainingjaxtpularge-language-modelsgoogle-researchdistributed-training