Merge pull request #118 from FederatedAI/dev-2.2.0

Merge v2.2.0's  Updates

README.md

@@ -13,12 +13,13 @@ FATE-LLM is a framework to support federated learning for large language models(
   <img src="./doc/images/fate-llm-plan.png">
 </div>
 
-## Deployment
-
 ### Standalone deployment
-Please refer to [FATE-Standalone deployment](https://github.com/FederatedAI/FATE#standalone-deployment).  
-* 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` 
+* To deploy FATE-LLM v2.2.0 or higher version, three ways are provided, please refer [deploy tutorial](./doc/standalone_deploy.md) for more details:
+  * deploy with FATE only from pypi then using Launcher to run tasks
+  * deploy with FATE、FATE-Flow、FATE-Client from pypi, user can run tasks with Pipeline  
+* To deploy lower versions: please refer to [FATE-Standalone deployment](https://github.com/FederatedAI/FATE#standalone-deployment).   
+  * To deploy FATE-LLM v2.0.* - FATE-LLM v2.1.*, 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.
@@ -33,6 +34,8 @@ with Communication Cost under 18 Kilobytes](./doc/tutorial/fedkseed/)
 - [InferDPT: Privacy-preserving Inference for Black-box Large Language Models](./doc/tutorial/inferdpt/inferdpt_tutorial.ipynb)
 - [FedMKT: Federated Mutual Knowledge Transfer for Large and Small
 Language Models](./doc/tutorial/fedmkt/)
+- [PDSS: A Privacy-Preserving Framework for Step-by-Step Distillation of Large Language Models](./doc/tutorial/pdss)
+- [FDKT: Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data](./doc/tutorial/fdkt)
 
 ## FATE-LLM Evaluate
 

RELEASE.md

@@ -1,3 +1,13 @@
+## Release 2.2.0
+### Major Features and Improvements
+* Integrate the PDSS algorithm, a novel framework that enhances local small language models (SLMs) using differentially private protected Chain of Thoughts (Cot) generated by remote LLMs:
+  * Implement InferDPT for privacy-preserving Cot generation.
+  * Support an encoder-decoder mechanism for privacy-preserving Cot generation.
+  * Add prefix trainers for step-by-step distillation and text encoder-decoder training.
+* Integrate the FDKT algorithm, a framework that enables domain-specific knowledge transfer from LLMs to SLMs while preserving SLM data privacy
+* Deployment Optimization: support installation of FATE-LLM by PyPi
+
+
 ## Release 2.1.0
 ### Major Features and Improvements
 * New FedMKT Federated Tuning Algorithms: Federated Mutual Knowledge Transfer for Large and Small Language Models

doc/standalone_deploy.md

@@ -0,0 +1,78 @@
+# FATE-LLM Single-Node Deployment Guide
+
+## 1. Introduction
+
+**Server Configuration:**
+
+- **Quantity:** 1
+- **Configuration:** 8 cores / 16GB memory / 500GB hard disk / GPU Machine
+- **Operating System:** CentOS Linux release 7
+- **User:** User: app owner:apps
+
+The single-node version provides 3 deployment methods, which can be selected based on your needs:
+- Install FATE-LLM from PyPI With FATE
+- Install FATE-LLM from PyPI with FATE, FATE-Flow, FATE-Client
+
+## 2. Install FATE-LLM from PyPI With FATE
+In this way, user can run tasks with Launcher, a convenient way for fast experimental using.
+
+### 2.1 Installing Python Environment
+- Prepare and install [conda](https://docs.conda.io/projects/miniconda/en/latest/) environment.
+- Create a virtual environment:
+
+```shell
+# FATE-LLM requires Python >= 3.10
+conda create -n fate_env python=3.10
+conda activate fate_env
+```
+
+### 2.2 Installing FATE-LLM
+This section introduces how to install FATE-LLM from pypi with FATE, execute the following command to install FATE-LLM. 
+
+```shell
+pip install fate_llm[fate]==2.2.0
+```
+
+### 2.3 Usage
+After installing successfully, please refer to [tutorials](../README.md#quick-start) to run tasks, tasks describe in the tutorials running will Launcher are all supported.
+
+
+## 3. Install FATE-LLM from PyPI with FATE, FATE-Flow, FATE-Client
+In this way, user can run tasks with Pipeline or Launcher. 
+
+### 3.1 Installing Python Environment
+Please refer to section-2.1
+
+### 3.2 Installing FATE-LLM with FATE, FATE-Flow, FATE-Client
+
+```shell
+pip install fate_client[fate,fate_flow,fate_client]==2.2.0
+```
+
+### 3.3 Service Initialization
+
+```shell
+mkdir fate_workspace
+fate_flow init --ip 127.0.0.1 --port 9380 --home $(pwd)/fate_workspace
+pipeline init --ip 127.0.0.1 --port 9380
+```
+- `ip`: The IP address where the service runs.
+- `port`: The HTTP port the service runs on.
+- `home`: The data storage directory, including data, models, logs, job configurations, and SQLite databases.
+
+### 3.4 Start Fate-Flow Service
+
+```shell
+fate_flow start
+fate_flow status # make sure fate_flow service is started
+```
+
+FATE-Flow also provides other instructions like stop and restart, use only if users want to stop/restart fate_flow services.
+```shell
+# Warning: normal installing process does not need to execute stop/restart instructions.
+fate_flow stop
+fate_flow restart
+```
+
+### 3.5 Usage
+Please refer to [tutorials](../README.md#quick-start) for more usage guides, tasks describe in the tutorials running will Pipeline or Launcher are all supported.

doc/tutorial/fdkt/README.md

@@ -0,0 +1,14 @@
+# FATE-LLM: FDKT
+The algorithm is based on paper [Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data](https://arxiv.org/pdf/2405.14212), 
+a novel framework that enables domain-specific knowledge transfer from LLMs to SLMs while preserving SLM data privacy.
+
+## Citation
+If you publish work that uses FDKT, please cite FDKT as follows:
+```
+@article{li2024federated,
+  title={Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data},
+  author={Li, Haoran and Zhao, Xinyuan and Guo, Dadi and Gu, Hanlin and Zeng, Ziqian and Han, Yuxing and Song, Yangqiu and Fan, Lixin and Yang, Qiang},
+  journal={arXiv preprint arXiv:2405.14212},
+  year={2024}
+}
+```