{"name":"Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models","description":"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","url":"https://osrepos.com/repo/ludwig-ai-ludwig","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/ludwig-ai-ludwig","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/ludwig-ai-ludwig.md","json":"https://osrepos.com/repo/ludwig-ai-ludwig.json","topics":["Python","AI Framework","Deep Learning","LLM","Fine-tuning","Machine Learning","Computer Vision","PyTorch"],"keywords":["Python","AI Framework","Deep Learning","LLM","Fine-tuning","Machine Learning","Computer Vision","PyTorch"],"stars":null,"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.","content":"## Introduction\n\nLudwig 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.\n\n## Installation\n\nLudwig requires Python 3.12 or newer. You can install the core library or specific sets of dependencies based on your needs:\n\nbash\npip install ludwig           # core\npip install ludwig[full]     # all optional dependencies\npip install ludwig[llm]      # LLM fine-tuning only\n\n\n## Examples\n\nLudwig's declarative nature shines through its straightforward configuration and command-line interface.\n\n### Fine-tune an LLM (instruction tuning)\n\nFine-tune a large language model with just a YAML file and a simple command:\n\nyaml\nmodel_type: llm\nbase_model: meta-llama/Llama-3.1-8B\n\nquantization:\n  bits: 4\n\nadapter:\n  type: lora\n\nprompt:\n  template: |\n    ### Instruction: {instruction}\n    ### Input: {input}\n    ### Response:\n\ninput_features:\n  - name: prompt\n    type: text\n\noutput_features:\n  - name: output\n    type: text\n\ntrainer:\n  type: finetune\n  learning_rate: 0.0001\n  batch_size: 1\n  gradient_accumulation_steps: 16\n  epochs: 3\n  learning_rate_scheduler:\n    decay: cosine\n    warmup_fraction: 0.01\n\nbackend:\n  type: local\n\n\nbash\nexport HUGGING_FACE_HUB_TOKEN=\"<your_token>\"\nludwig train --config model.yaml --dataset \"ludwig://alpaca\"\n\n\n### Train a multimodal classifier\n\nCombine different data types, like text and images, to build powerful multimodal models:\n\nyaml\ninput_features:\n  - name: review_text\n    type: text\n    encoder:\n      type: bert\n  - name: star_rating\n    type: number\n  - name: product_image\n    type: image\n    encoder:\n      type: dinov2\n\noutput_features:\n  - name: recommended\n    type: binary\n\n\nbash\nludwig train --config model.yaml --dataset reviews.csv\n\n\n### Generate a config from natural language\n\nLeverage Ludwig's AI capabilities to generate model configurations directly from a natural language description:\n\nbash\nludwig generate_config \"I have a CSV with age, income, education level, and I want to predict loan default\"\n\n\n## Why use Ludwig?\n\nLudwig offers several compelling advantages for AI development:\n\n*   **Zero boilerplate**: No need for training loops, data pipelines, or evaluation code, the YAML config defines everything.\n*   **Best-in-class LLM support**: Comprehensive features for LLM fine-tuning, including LoRA, GRPO alignment, torchao QAT, and VLM fine-tuning, all configurable.\n*   **Multimodal out of the box**: Easily integrate and combine text, images, numbers, audio, and timeseries data with minimal configuration changes.\n*   **Scale without code changes**: Seamlessly transition from local development to multi-GPU setups or Ray clusters by simply adjusting the `backend.type` parameter.\n*   **Expert control when you need it**: Every aspect, from activation functions to schedulers and optimizers, is fully configurable for advanced users.\n*   **Reproducible research**: Every experiment run is logged, and the full configuration is saved, facilitating easy comparison and reproduction with `ludwig visualize`.\n\n## Links\n\n*   [Official Documentation](https://ludwig.ai \"Ludwig Documentation\")\n*   [GitHub Repository](https://github.com/ludwig-ai/ludwig \"Ludwig GitHub Repository\")\n*   [Discord Community](https://discord.gg/CBgdrGnZjy \"Ludwig Discord Community\")","metrics":{"detailViews":2,"githubClicks":1},"dates":{"published":null,"modified":"2026-07-06T20:01:30.000Z"}}