Curator provides an implementation of Retrieval Augmented Fine-Tuning (RAFT) to adapt Large Language Models (LLMs) to domain-specific tasks using Retrieval-Augmented Generation (RAG).
Enhances language models by integrating external knowledge during training, enabling more accurate and relevant outputs.
Allows fine-tuning of specific models like Llama-3.1-8B-Instruct to adapt to unique domain requirements.
Supports distributed training with DeepSpeed across multiple GPUs to handle large datasets and complex models efficiently.