Ludwig
Declarative deep learning framework for building and fine-tuning models with YAML configuration
Ludwig is a declarative, low-code framework for building, training, and fine-tuning AI models using YAML configuration instead of writing training code. Originally created at Uber AI, it is now maintained under the Linux Foundation. Supports LLM fine-tuning with QLoRA, LoRA, and other parameter-efficient methods. Handles multi-modal inputs (tabular, text, images, audio), distributed training via DDP and DeepSpeed, and larger-than-memory datasets. Works with models like Llama, Mistral, Mixtral, and Gemma.
Pricing: Free
Ludwig Alternatives
Explore 21 products in the Fine-tuning category. View all Ludwig alternatives.
Hugging Face
The open-source AI platform with 500K+ models, inference endpoints, and fine-tuning tools
torchtune
PyTorch-native library for fine-tuning LLMs on consumer and enterprise GPUs
LLaMA-Factory
Open-source fine-tuning framework for 100+ LLMs with a web UI
OpenAI
API access to GPT, o-series reasoning, DALL-E, and Whisper models
Unsloth
Fine-tune LLMs up to 30x faster with 90% less memory usage
Is your product missing?