update readme and setup

README.md

@@ -2,9 +2,6 @@
 
 [![OTO-bage](https://img.shields.io/badge/OTO-red?logo=atom&logoColor=white)](#) [![autoML-bage](https://img.shields.io/badge/autoML-blue?logo=dependabot&logoColor=white)](#) [![DNN-training-bage](https://img.shields.io/badge/DNN-training-yellow)](#) [![DNN-compress-bage](https://img.shields.io/badge/DNN-compress-purple)](#) [![Operator-pruning-bage](https://img.shields.io/badge/Operator-pruning-green)](#) [![Operator-erasing-bage](https://img.shields.io/badge/Operator-erasing-CornflowerBlue)](#) [![build-pytorchs-bage](https://img.shields.io/badge/build-pytorch-orange)](#) [![lincese-bage](https://img.shields.io/badge/license-MIT-blue.svg)](#) [![prs-bage](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](#)
 
-### Please note.
-
-The previous OTOv2 repo has been moved into [legacy_branch](https://github.com/tianyic/only_train_once/tree/otov2_legacy_backup) for academic replication.
 
 ![oto_overview](https://github.com/tianyic/only_train_once/assets/8930611/131bd6ba-3f94-4b46-8398-074ae311ccf0)
 
@@ -106,25 +103,29 @@ The [`visual_examples`](./visual_examples) provides the visualization of pruning
 
 - Release refactorized DHSPG and LHSPG.
 
-- Release the full pipeline of LoRAShear (upon commercial administration).
+- Release the full pipeline of LoRAShear (upon business administration).
 
-- Provide more tutorials to cover the experiments in OTOv3 pruning mode. 
+- Provide more tutorials to cover the experiments in the pruning mode. Main experiments in OTOv2 can be found at [otov2_branch](https://github.com/tianyic/only_train_once/tree/otov2_legacy_backup/tutorials).
 
 - Release official erasing mode after the review process of OTOv3.
 
 - Provide documentations of the OTO API.
 
 
-## Welcome Contributions
+## Welcome Contribution
 
 We would greatly appreciate the contributions in any form, such as bug fixes, new features and new tutorials, from our open-source community. 
 
 We are humble to provide benefits for the AI community. We look forward to working with the community together to make DNN's training and compression to be more automatic and convinient. 
 
 ## Open for collabration.
 
-Meanwhile, we are open to collabrations. Feel free to reach out <[email protected]> if have any interesting idea. 
+We are open and happy for collabrations. Feel free to reach out <[email protected]> if have any interesting idea.
+
 
+## Legacy OTOv2 repository
+
+The previous OTOv2 repo has been moved into [legacy_branch](https://github.com/tianyic/only_train_once/tree/otov2_legacy_backup) for academic replication.
 
 ## Citation
 

setup.py

@@ -6,9 +6,9 @@ def parse_requirements(filename):
 
 reqs = parse_requirements('requirements.txt')
 
-VERSION = '2.0.16'
+VERSION = '3.0.0'
 DESCRIPTION = 'Only Train Once (OTO): Automatic One-Shot General DNN Training and Compression Framework'
-LONG_DESCRIPTION = 'Only Train Once (OTO): Automatic One-Shot General DNN Training and Compression (via Structured Pruning) Framework'
+LONG_DESCRIPTION = 'Only Train Once (OTO): Automatic One-Shot General DNN Training and Compression Framework'
 
 setup(
     name="only_train_once",