Skip to main content

A framework for federated fine-tuning of retrieval-augmented generation (RAG) systems.

Project description

FedRAG


Linting Unit Testing and Upload Coverage codecov GitHub License GitHub Release DOI

FedRAG is a framework for federated fine-tuning of Retrieval-Augmented Generation (RAG) systems, wherein a server and potentially several client nodes share an overall model architecture. Since the model is common to all participants of the system, fine-tuning can be done collaboratively without any of the raw data leaving any of the client nodes. Instead only the model weight updates are shared between the client and the server.

./fed_rag
├── __init__.py
├── base
│   ├── __init__.py
│   ├── loss.py
│   └── models
│       ├── __init__.py
│       ├── generator.py # BaseGeneratorModel       ├── rag.py # BaseRAGModel       └── retriever.py # BaseRetrieverModel
├── loss
│   ├── __init__.py
│   ├── generator  # Losses for generation task      └── __init__.py
│   └── retriever # Losses for retrieval task       └── __init__.py
├── models
│   ├── __init__.py
│   ├── generators # Generator models      └── __init__.py
│   └── retrievers # Retrieval models       └── __init__.py
├── ops # module for running fed system   └── __init__.py
└── types
    ├── __init__.py
    ├── client.py # Wrapper for flwr.Client
    └── server.py # Wrapper for flwr.Server

Getting Started

Contributing

Install the project's dev dependencies:

# while in root directory of project `fed-rag/`
uv sync --all-extras --dev

Install the pre-commit hooks:

pre-commit install

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fed_rag-0.0.6.post2.tar.gz (33.6 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fed_rag-0.0.6.post2-py3-none-any.whl (37.0 kB view details)

Uploaded Python 3

File details

Details for the file fed_rag-0.0.6.post2.tar.gz.

File metadata

  • Download URL: fed_rag-0.0.6.post2.tar.gz
  • Upload date:
  • Size: 33.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for fed_rag-0.0.6.post2.tar.gz
Algorithm Hash digest
SHA256 4b05c43e6c29c6cf91ddbf861e669b638cb3bd42b4732f3faca04a243951dadb
MD5 fb81ffcd7cfda7e8307e1deaebdd2a47
BLAKE2b-256 8ab05dd5b99ed16871be4e943b1ad5e501e6a3ef8cb2bfc80d275c1a568fbd0c

See more details on using hashes here.

File details

Details for the file fed_rag-0.0.6.post2-py3-none-any.whl.

File metadata

  • Download URL: fed_rag-0.0.6.post2-py3-none-any.whl
  • Upload date:
  • Size: 37.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.8

File hashes

Hashes for fed_rag-0.0.6.post2-py3-none-any.whl
Algorithm Hash digest
SHA256 614626cd0977cfad82cace146c7388c28a9a9308740c74e9220293d6ed9ede28
MD5 6a1c281559a908134120f51a8d566518
BLAKE2b-256 1899610c63e7c58e5a2608d11563a267fec2e67c4606c5565f7bcc300049153b

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page