v0.9.2
π¨ Breaking Changes
No breaking changes in this release! π
π New Features
Activation Checkpointing with Disk Offloading
You can now significantly reduce VRAM usage by offloading activation checkpoints to disk with prefetching. This makes it possible to train larger models or use larger batch sizes on memory-constrained hardware. This is only recommended on resource constrained systems like Google Colab.
Support for Atropos
Atropos is a framework for Reinforcement Learning by NousResearch. The Atropos plugin for Axolotl adds Atropos' RL environments into Axolotl's training pipelines. This allows you to leverage Atropos for reinforcement learning while utilizing Axolotl's extensive features for model fine-tuning.
π§ Major Fixes
- LoRA Kernel Stability: The LoRA kernel is now disabled when
lora_dropoutis non-zero instead of being auto-enabled. (by @NanoCode012 in #2655) - RunPod Stability: Improved handling of environment variables for RunPod serverless deployments to prevent accidental deletion of secrets. (by @winglian in #2653)
- General Bug Fixes: Pass
save_only_modelto RL trainer and improve mistral fft tests. (by @winglian in #2661)
Other Improvements
Full Changelog: https://github.com/axolotl-ai-cloud/axolotl/compare/v0.9.1.post1...v0.9.2