From d9e7d6cd22830429d0f8c38aec84f862fb24de06 Mon Sep 17 00:00:00 2001 From: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com> Date: Fri, 20 Jan 2023 15:34:11 +0530 Subject: [PATCH] fixes, docs and version bump up --- README.md | 2 +- examples/sequence_classification/Prompt_Tuning.ipynb | 2 +- setup.py | 6 ++---- src/peft/__init__.py | 2 +- 4 files changed, 5 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 1045379..b91023a 100644 --- a/README.md +++ b/README.md @@ -21,7 +21,7 @@ limitations under the License. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model's parameters. Fine-tuning large-scale PLMs is often prohibitively costly. In this regard, PEFT methods only fine-tune a small number of (extra) model parameters, thereby greatly decreasing the computational and storage costs. Recent State-of-the-Art PEFT techniques achieve performance comparable to that of full fine-tuning. -Seamlessly integrated with 🤗 Accelerate for large scale models leveraging PyTorch FSDP. +Seamlessly integrated with 🤗 Accelerate for large scale models leveraging DeepSpeed and Big Model Inference. Supported methods: diff --git a/examples/sequence_classification/Prompt_Tuning.ipynb b/examples/sequence_classification/Prompt_Tuning.ipynb index 6deb207..0c53808 100644 --- a/examples/sequence_classification/Prompt_Tuning.ipynb +++ b/examples/sequence_classification/Prompt_Tuning.ipynb @@ -13,7 +13,7 @@ "import torch\n", "from torch.optim import AdamW\n", "from torch.utils.data import DataLoader\n", - "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, LoRAConfig, PeftType, \\\n", + "from peft import get_peft_config,get_peft_model, get_peft_model_state_dict, set_peft_model_state_dict, PeftType, \\\n", "PrefixTuningConfig, PromptEncoderConfig, PromptTuningConfig\n", "\n", "import evaluate\n", diff --git a/setup.py b/setup.py index fa55d7d..b727d65 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ extras["dev"] = extras["quality"] + extras["docs_specific"] setup( name="peft", - version="0.0.2", + version="0.1.0.dev", description="Parameter-Efficient Fine-Tuning (PEFT)", long_description=open("README.md", "r", encoding="utf-8").read(), long_description_content_type="text/markdown", @@ -71,9 +71,7 @@ setup( # twine upload dist/* -r pypitest # twine upload dist/* -r pypitest --repository-url=https://test.pypi.org/legacy/ # 6. Check that you can install it in a virtualenv by running: -# pip install -i https://testpypi.python.org/pypi accelerate -# accelerate env -# accelerate test +# pip install -i https://testpypi.python.org/pypi peft # 7. Upload the final version to actual pypi: # twine upload dist/* -r pypi # 8. Add release notes to the tag in github once everything is looking hunky-dory. diff --git a/src/peft/__init__.py b/src/peft/__init__.py index fea5a32..e4bc9cb 100644 --- a/src/peft/__init__.py +++ b/src/peft/__init__.py @@ -17,7 +17,7 @@ # See the License for the specific language governing permissions and # limitations under the License. -__version__ = "0.0.2" +__version__ = "0.1.0.dev" from .mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING, PEFT_TYPE_TO_CONFIG_MAPPING, get_peft_config, get_peft_model from .peft_model import (