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RAG-FiT

IntelLabs/RAG-FiT

Framework for enhancing LLMs for RAG tasks using fine-tuning

GitHub Website

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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768 stars62 forksLast push Jun 8, 2026 Python Apache-2.0

Decision brief

RAG-FiT is a Python framework that enables developers to fine-tune large language models specifically for Retriever-Augmented Generation (RAG) tasks, with strengths in evaluation and information retrieval.

Good fit when

When seeking to improve performance of LLMs in NLP tasks requiring RAG capabilities, like question-answering or semantic search
For developers who require a solution based on the Apache-2.0 license for flexibility and permissive use conditions

Avoid when

If project needs are more aligned with traditional fine-tuning methods that do not specifically enhance RAG capabilities, another tool might be more suitable
In scenarios where the development team lacks proficiency in Python, as RAG-FiT is Python-based and may have a steeper learning curve for non-Python developers

Requirements:: This framework requires proficiency in Python and an understanding of RAG tasks to be effectively utilized.

Observed Jul 15, 2026 · Source: enrich:decision_facts

RAG-FiT alternatives Trust report

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Maintenance and security

Full trust report

Maintenance: Slowing (102d since push); As of Sep 19, 2026
Provenance: Not a fork · Organization account; As of Sep 19, 2026
Security (OSV): No lockfile; As of Jul 11, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install RAG-FiT

PyPI

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

IntelLabs/RAG-FiT is a Python-based repository that provides a framework to enhance large language models (LLMs) specifically for Retriever-Augmented Generation (RAG) tasks through methods of fine-tuning. It caters to areas like evaluation, information retrieval, and semantic search, aiming to improve performance in NLP tasks such as question-answering.

Capability facts

Languages: python
Source: github.language+pyproject.toml · Sep 19, 2026

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 19, 2026)

pip install -e .

Source link

README

Installation Clone and run: Optional packages can be installed: Quick Start For a simple, end to end example, see the PubmedQA Tutorial. License The code is licensed under the Apache 2.0 License.

For agents

This page has a .md twin and JSON over the API.

/tools/intellabs-rag-fit.md GET /api/graphcanon/tools/intellabs-rag-fit GET /api/graphcanon/graph?tool=intellabs-rag-fit