Update 2.0.0

README.md

@@ -17,14 +17,14 @@ FATE-LLM is a framework to support federated learning for large language models(
 
 ### Standalone deployment
 Please refer to [FATE-Standalone deployment](https://github.com/FederatedAI/FATE#standalone-deployment).  
-Deploy FATE-Standalone version with 1.11.3 <= version < 2.0, then copy directory `python/fate_llm` to `{fate_install}/fate/python/fate_llm`
+* To deploy FATE-LLM v2.0, deploy FATE-Standalone with version >= 2.1, then make a new directory `{fate_install}/fate_llm` and clone the code into it, install the python requirements, and add `{fate_install}/fate_llm/python` to `PYTHONPATH` 
+* To deploy FATE-LLM v1.x, deploy FATE-Standalone with 1.11.3 <= version < 2.0, then copy directory `python/fate_llm` to `{fate_install}/fate/python/fate_llm` 
 
 ### Cluster deployment
 Use [FATE-LLM deployment packages](https://github.com/FederatedAI/FATE/wiki/Download#llm%E9%83%A8%E7%BD%B2%E5%8C%85) to deploy,  refer to [FATE-Cluster deployment](https://github.com/FederatedAI/FATE#cluster-deployment) for more deployment details.
 
 ## Quick Start
-- [Offsite-tuning Tutorial: Model Definition and Job Submission](./doc/tutorial/offsite_tuning/Offsite_tuning_tutorial.ipynb)
-- [FedIPR Tutorial: Add Watermarks to Your Model](./doc/tutorial/fed_ipr/FedIPR-tutorial.ipynb)
-- [Federated ChatGLM-6B Training](./doc/tutorial/parameter_efficient_llm/ChatGLM-6B_ds.ipynb)
-- [GPT-2 Training](./doc/tutorial/parameter_efficient_llm/GPT2-example.ipynb)
-- [Builtin Models In PELLM](./doc/tutorial/builtin_models.md)
+- [Federated ChatGLM3-6B Training](./doc/tutorial/parameter_efficient_llm/ChatGLM3-6B_ds.ipynb)
+- [Builtin Models In PELLM](./doc/tutorial/builtin_pellm_models.md)
+- [Offsite Tuning Tutorial](./doc/tutorial/offsite_tuning/Offsite_tuning_tutorial.ipynb)
+- [FedKSeed](./doc/tutorial/fedkseed/fedkseed-example.ipynb)

RELEASE.md

@@ -1,3 +1,11 @@
+## Release 2.0.0
+### Major Features and Improvements
+* Adapt to fate-v2.0 framework:
+  * Migrate parameter-efficient fine-tuning training methods and models. 
+  * Migrate Standard Offsite-Tuning and Extended Offsite-Tuning（Federated Offsite-Tuning+)
+  * Newly trainer，dataset, data_processing function design
+* New FedKSeed Federated Tuning Algorithm: train large language models in a federated learning setting with extremely low communication cost
+
 ## Release 1.3.0
 ### Major Features and Improvements
 * FTL-LLM（Fedrated Learning + Transfer Learning + LLM）

doc/tutorial/builtin_pellm_models.md

@@ -0,0 +1,21 @@
+## Builtin PELLM Models
+FATE-LLM provide some builtin pellm models, users can use them simply to efficiently train their language models.
+To use these models, please read the using tutorial of [ChatGLM-6B Training Guide](./ChatGLM-6B_ds.ipynb).   
+After reading the training tutorial above, it's easy to use other models listing in the following tabular by changing `module_name`, `class_name`, `dataset` list below.
+
+
+
+| Model          | ModuleName        | ClassName     | DataSetName     | 
+| -------------- | ----------------- | --------------| --------------- |                 
+| Qwen2          | pellm.qwen        | Qwen          | prompt_dataset  |                              
+| Bloom-7B1      | pellm.bloom       | Bloom         | prompt_dataset  |                              
+| LLaMA-2-7B     | pellm.llama       | LLaMa         | prompt_dataset  |                              
+| LLaMA-7B       | pellm.llama       | LLaMa         | prompt_dataset  |                              
+| ChatGLM3-6B    | pellm.chatglm     | ChatGLM       | prompt_dataset  |                              
+| GPT-2          | pellm.gpt2        | GPT2          | seq_cls_dataset |                              
+| ALBERT         | pellm.albert      | Albert        | seq_cls_dataset |                              
+| BART           | pellm.bart        | Bart          | seq_cls_dataset |                              
+| BERT           | pellm.bert        | Bert          | seq_cls_dataset |                              
+| DeBERTa        | pellm.deberta     | Deberta       | seq_cls_dataset |                              
+| DistilBERT     | pellm.distilbert  | DistilBert    | seq_cls_dataset |                              
+| RoBERTa        | pellm.roberta     | Roberta       | seq_cls_dataset |