Merge pull request #33 from huggingface/smangrul/fixes

fixes, docs and version bump up

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:
 

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",

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.

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 (