Finetune Pipelines is a Python-based project that provides structured workflows for fine-tuning machine learning models using reusable recipes.

It is designed to move fine-tuning from ad hoc scripts toward a more consistent and repeatable process. The system separates training logic into clearly defined components so experiments can be reproduced, compared, and extended without reworking the entire setup.

Purpose

Fine-tuning is often implemented as one-off code tied to a specific dataset or model. This project introduces a pipeline-oriented approach where each step is explicit and configurable.

The goal is to make fine-tuning easier to manage over time, especially as datasets evolve, models change, or evaluation requirements become more complex.

Recipes and Pipelines

The core of the system is built around recipes.

Recipes define how a model is trained, including dataset handling, training configuration, and optimization strategy. Pipelines handle execution, orchestration, and output.

Current recipes include:

  • Supervised Fine-Tuning (SFT)
  • Direct Preference Optimization (DPO)

This structure allows new methods to be added without changing the overall system design.

Modular Design

Finetune Pipelines is intentionally modular.

Datasets, trainers, and evaluation steps are designed as interchangeable components. This makes it possible to:

  • test different training strategies.
  • swap model backends.
  • extend evaluation without rewriting pipelines.

The result is a system that supports experimentation without becoming difficult to maintain.

Command-Line Interface

The project includes a simple CLI for running pipelines.

This allows users to execute recipes, manage configurations, and run experiments in a consistent way, whether locally or in automated environments.

Positioning

Finetune Pipelines sits within a broader set of AI system tools focused on structure, reliability, and developer clarity.

It complements projects such as:

Together, these projects support a more disciplined approach to building and maintaining AI systems.

Roadmap

Planned areas of expansion include:

  • additional fine-tuning methods and optimization strategies.
  • improved dataset tooling and validation.
  • standardized evaluation pipelines.
  • better experiment tracking and reporting.

The long-term direction is to make fine-tuning workflows easier to understand, reuse, and operate as part of larger AI systems.