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torchtune is a PyTorch-native library for easily authoring, fine-tuning and experimenting with LLMs. We're excited to announce our alpha release! torchtune provides: Native-PyTorch implementations of popular LLMs using composable and modular building blocks Easy-to-use and hackable training recipes for popular fine-tuning techniques (LoRA, QLoRA) - no trainers, no frameworks, just PyTorch! YAML co
Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel, table-wise, row-wise, table-wise-row-wise, column-wise, table-wise-column-wise sharding. The TorchRec planner can automatically generate opti
PyTorch 2.3 Release notes Highlights Backwards Incompatible Changes Deprecations New Features Improvements Bug fixes Performance Documentation Highlights We are excited to announce the release of PyTorch® 2.3! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance complications
PyTorch 1.6.0 Release Notes Highlights Backwards Incompatible Changes Deprecations New Features Improvements Bug Fixes Performance Documentation Highlights The PyTorch 1.6 release includes a number of new APIs, tools for performance improvement and profiling, as well as major updates to both distributed data parallel (DDP) and remote procedure call (RPC) based distributed training. A few of the hi
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