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Shepherd: A foundational framework enabling federated instruction tuning for large language models
| Date | Stars |
|---|---|
| 2026-07-31 | 252 |
| 2026-08-04 | 252 |
| 2026-08-06 | 252 |
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<h1 align="center"> <img src="assets/shepherd&llamas.png" width="75%"> <br> Shepherd <br> </h1> <h4 align="center"><em><span style="font-size:18pt"> A Platform Supporting Federated Instruction Tuning </span></em></h4> <p align="center"> <a href="#Overview">Overview</a> • <a href="https://arxiv.org/pdf/2305.05644.pdf">Paper</a> • <a href="#Installation">Installation</a> • <a href="#Data_Preparation">Data_Preparation</a> • <a href="#Federated_Finetuning">Federated_Finetuning</a> • <a href="#Inference">Inference</a> • <a href="#Citation">Citation</a> </p> [](https://github.com/JayZhang42/FederatedGPT-Shepherd/blob/main/LICENSE) \ **Usage and License Notices**:The data, code and checkpoints are intended and licensed for research use only. ## Overview Recent advancements in fine-tuning large language models (LLMs) have leveraged instructions created by humans or APIs (such as ChatGPT and GPT-4) to revolutionize NLP research and industry applications. However, the collection of instructions from a wide array of individuals presents challenges in cost and privacy. For instance, collecting vast amounts of daily conversations from users is a valuable means of providing guidance for LLMs, enabling them to generate authentic and genuine responses. However, privacy concerns may hinder users from sharing their conversations, resulting in a limited quantity of instructions that are not fully representative of the target population. Federated Learning, a well-studied and well-developed learning approach, provides a solution to addresses these challenges and paves the way for designing personalized LLMs tailored to individual users. This repository, *Shepherd*, offers a foundational framework for exploring federated finetuning of LLMs using heterogeneous instructions across diverse categories. The framework is designed for ease of use, adaptability, and scalability to accommodate large datasets. Additionally, it facilitates seamless integration of novel algorithms and configurations, making it a convenient tool for researchers and practitioners in both the FL and the NLP community. ## Paper We are pleased to share our [***FedIT***](https://arxiv.org/pdf/2305.05644.pdf) [Paper], "*Towards Building the Federated GPT: Federated Instruction Tuning.*" We kindly invite you to read the paper for an in-depth understanding of Federated Instruction Tuning for LLMs and further insights into our repository. <p align="center"> <img src="assets/FedIT.png" width="100%"> </p> ## Installation The code requires some dependencies (Python=3.8) as specified in `requirements.txt`. Please follow the relevant libraries to install or run: ```bash pip install -r requirements.txt ``` If `bitsandbytes` doesn't work, [install it from source](https://github.com/TimDettmers/bitsandbytes/blob/main/compile_from_source.md). Windows users can follow [these instructions](https://github.com/tloen/alpaca-lora/issues/17). ## Data_Preparation Prior to commencing the federated fine-tuning, make sure to create a data file for each individual client. ```bash num_client=10 # The number of clients diff_quantity=0 # Whether clients have different amounts of data python client_data_allocation.py $num_client $diff_quantity ``` Running this command will save the data files in the folder `./data/str(num_client)`. The data file `new-databricks-dolly-15k.json` for generating each client's local dataset is the first version of `databricks-dolly-15k` , which is a corpus of more than 15,000 records with 8 categeries generated by thousands of [Databricks Lab](https://www.databricks.com/learn/labs) employees. Please refer to their official repository [dolly](https://github.com/databrickslabs/dolly) for the latest version of data. ### Categories distribution and Heteogeneity The first version of `databricks-dolly-15k` contains 8 Categories, with the distribution of ea
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