peft 0.2.0

v0.2.0

on Python PyPI

Whisper large tuning using PEFT LoRA+INT-8 on T4 GPU in Colab notebooks

We tested PEFT on @OpenAI's Whisper Large model and got:
i) 5x larger batch sizes
ii) Less than 8GB GPU VRAM
iii) Best part? Almost no degredation to WER 🤯

Without PEFT:

• OOM on a T4 GPU ❌
• 6GB checkpoint ❌
• 13.64 WER ✅

With PEFT:

• Train on a T4 GPU ✅
• 60MB checkpoint ✅
• 14.01 WER ✅

• adding whisper large peft+int8 training example by @pacman100 in #95

prepare_for_int8_training utility

This utility enables preprocessing the base model to be ready for INT8 training.

• [core] add prepare_model_for_training by @younesbelkada in #85
• [core] Some changes with prepare_model_for_training & few fixes by @younesbelkada in #105

disable_adapter() context manager

Enables to disable adapter layers to get the outputs from the frozen base models.
An exciting application of this feature allows only a single model copy to be used for policy model and reference model generations in RLHF.

• add disable adapter context manager by @pacman100 in #106

What's Changed

• release v0.2.0.dev0 by @pacman100 in #69
• Update README.md by @sayakpaul in #72
• Fixed typo in Readme by @Muhtasham in #73
• Update README.md by @pacman100 in #77
• convert prompt tuning vocab to fp32 by @mayank31398 in #68
• [core] add prepare_model_for_training by @younesbelkada in #85
• [bnb] add flan-t5 example by @younesbelkada in #86
• making prepare_model_for_training flexible by @pacman100 in #90
• adding whisper large peft+int8 training example by @pacman100 in #95
• making bnb optional by @pacman100 in #97
• add support for regex target modules in lora by @pacman100 in #104
• [core] Some changes with prepare_model_for_training & few fixes by @younesbelkada in #105
• Fix typo by @mrm8488 in #107
• add disable adapter context manager by @pacman100 in #106
• add EleutherAI/gpt-neox-20b to support matrix by @pacman100 in #109
• fix merging lora weights for inference by @pacman100 in #117
• [core] Fix autocast issue by @younesbelkada in #121
• fixes prepare_for_int8_training by @pacman100 in #127
• issue#126: torch.load device issue. by @gabinguo in #134
• fix: count params when zero init'd by @zanussbaum in #140
• chore: update pyproject.toml by @SauravMaheshkar in #125
• support option for encoder only prompts by @mayank31398 in #150
• minor fixes to the examples by @pacman100 in #149
• Add local saving for whisper largev2 example notebook by @alvanli in #163
• fix count by @dumpmemory in #162
• Add Prefix Tuning citation by @zphang in #159
• lora fixes and adding 8bitMegredLinear lora by @pacman100 in #157
• Update README.md by @pacman100 in #164
• minor changes by @pacman100 in #165

New Contributors

• @Muhtasham made their first contribution in #73
• @mayank31398 made their first contribution in #68
• @mrm8488 made their first contribution in #107
• @gabinguo made their first contribution in #134
• @zanussbaum made their first contribution in #140
• @SauravMaheshkar made their first contribution in #125
• @alvanli made their first contribution in #163
• @dumpmemory made their first contribution in #162
• @zphang made their first contribution in #159

Significant community contributions

The following contributors have made significant changes to the library over the last release:

• @mayank31398
  • Prompt Tuning method enhancements and fixes (#68, #150)

Full Changelog: v0.1.0...v0.2.0