# Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models

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Ludwig is a powerful, low-code declarative deep learning framework designed for building custom LLMs, neural networks, and other AI models. It simplifies the process of training, fine-tuning, and deploying models, from LLM fine-tuning to tabular classification, using a simple YAML configuration without boilerplate Python code. This makes advanced AI development accessible and efficient for a wide range of applications.

GitHub: https://github.com/ludwig-ai/ludwig
OSRepos URL: https://osrepos.com/repo/ludwig-ai-ludwig

## Summary

Ludwig is a powerful, low-code declarative deep learning framework designed for building custom LLMs, neural networks, and other AI models. It simplifies the process of training, fine-tuning, and deploying models, from LLM fine-tuning to tabular classification, using a simple YAML configuration without boilerplate Python code. This makes advanced AI development accessible and efficient for a wide range of applications.

## Topics

- Python
- AI Framework
- Deep Learning
- LLM
- Fine-tuning
- Machine Learning
- Computer Vision
- PyTorch

## Repository Information

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## Content

## Introduction

Ludwig is a powerful, low-code declarative deep learning framework that simplifies the process of building custom LLMs, neural networks, and other AI models. It enables users to train, fine-tune, and deploy a wide range of models, from large language models to multimodal and tabular AI, using a simple YAML configuration. This approach eliminates the need for boilerplate Python code, making advanced AI development accessible and efficient for researchers and developers alike.

## Installation

Ludwig requires Python 3.12 or newer. You can install the core library or specific sets of dependencies based on your needs:

bash
pip install ludwig           # core
pip install ludwig[full]     # all optional dependencies
pip install ludwig[llm]      # LLM fine-tuning only


## Examples

Ludwig's declarative nature shines through its straightforward configuration and command-line interface.

### Fine-tune an LLM (instruction tuning)

Fine-tune a large language model with just a YAML file and a simple command:

yaml
model_type: llm
base_model: meta-llama/Llama-3.1-8B

quantization:
  bits: 4

adapter:
  type: lora

prompt:
  template: |
    ### Instruction: {instruction}
    ### Input: {input}
    ### Response:

input_features:
  - name: prompt
    type: text

output_features:
  - name: output
    type: text

trainer:
  type: finetune
  learning_rate: 0.0001
  batch_size: 1
  gradient_accumulation_steps: 16
  epochs: 3
  learning_rate_scheduler:
    decay: cosine
    warmup_fraction: 0.01

backend:
  type: local


bash
export HUGGING_FACE_HUB_TOKEN="<your_token>"
ludwig train --config model.yaml --dataset "ludwig://alpaca"


### Train a multimodal classifier

Combine different data types, like text and images, to build powerful multimodal models:

yaml
input_features:
  - name: review_text
    type: text
    encoder:
      type: bert
  - name: star_rating
    type: number
  - name: product_image
    type: image
    encoder:
      type: dinov2

output_features:
  - name: recommended
    type: binary


bash
ludwig train --config model.yaml --dataset reviews.csv


### Generate a config from natural language

Leverage Ludwig's AI capabilities to generate model configurations directly from a natural language description:

bash
ludwig generate_config "I have a CSV with age, income, education level, and I want to predict loan default"


## Why use Ludwig?

Ludwig offers several compelling advantages for AI development:

*   **Zero boilerplate**: No need for training loops, data pipelines, or evaluation code, the YAML config defines everything.
*   **Best-in-class LLM support**: Comprehensive features for LLM fine-tuning, including LoRA, GRPO alignment, torchao QAT, and VLM fine-tuning, all configurable.
*   **Multimodal out of the box**: Easily integrate and combine text, images, numbers, audio, and timeseries data with minimal configuration changes.
*   **Scale without code changes**: Seamlessly transition from local development to multi-GPU setups or Ray clusters by simply adjusting the `backend.type` parameter.
*   **Expert control when you need it**: Every aspect, from activation functions to schedulers and optimizers, is fully configurable for advanced users.
*   **Reproducible research**: Every experiment run is logged, and the full configuration is saved, facilitating easy comparison and reproduction with `ludwig visualize`.

## Links

*   [Official Documentation](https://ludwig.ai "Ludwig Documentation")
*   [GitHub Repository](https://github.com/ludwig-ai/ludwig "Ludwig GitHub Repository")
*   [Discord Community](https://discord.gg/CBgdrGnZjy "Ludwig Discord Community")