3.9 KiB
An Open-Source Framework for Paramter Efficient Tuning (Delta Tuning).
Overview • Installation • Basic Usage • Docs • Performance •
Overview
OpenDelta is a toolkit for parameter efficient methods (we dub it as delta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most paramters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora, or any other types of delta tuning with preferred PTMs.
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Our repo is tested on Python 3.8 and PyTorch 1.9.0. Lower version may also be supported.
Updates
- 2022.02.16 support regular expression in named-based addressing.
Installation
create a virtualenv (optional)
conda create -n opendelta_env python=3.8
conda activate opendelta_env
Using Pip
Install OpenDelta using pip as follows:
pip install opendelta
To play with the latest features, you can also install OpenDelta from the source.
Build from Source
git clone https://github.com/thunlp/OpenDelta.git
cd OpenDelta
Option 1: If you won't modify the code, run
python setup.py install
Option 2: If you want to modify the code or keep the repo updated by git clone, run
python setup.py develop
Must Try
from transformers import AutoModelForSeq2SeqLM
t5 = AutoModelForSeq2SeqLM.from_pretrained("t5-base")
from opendelta import AutoDeltaModel
delta = AutoDeltaModel.from_finetuned("DeltaHub/lora_t5-base_mrpc", backbone_model=t5)
delta.log()
Verified Supported Models
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You can try to use OpenDelta on any backbone models based on PyTorch.
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However, with small chances thatThe interface of the submodules of the backbone model is not supported. Therefore we verified some commonly used models that OpenDelta are sure to support.
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We will keep testing more and more emerging models.
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Pull requests are welcomed when you successfully apply OpenDelta on your own backbone model.
Lora | Bias Tuning |
Adapter Houstbly |
Adapter Preffier |
Adapter Drop |
Adapater Low-Rank |
Compactor | Prefix Tuning |
Prompt Tuning |
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T5 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
BART | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | |
BERT | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
T5-3b(parallel) | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
Deberta-v2 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
CTRL | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ||
ViT | ✅ |
Performance Checked Combination
Google sheet here
Subject to change at any moment.