Repository History
5 repositories tagged with Fine-tuning
Ludwig: Low-Code Declarative Deep Learning for LLMs and AI Models
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.
xTuring: Build, Personalize, and Control Your Own LLMs
xTuring is an open-source framework designed to simplify the process of building, personalizing, and controlling Large Language Models (LLMs). It provides an easy way to fine-tune open-source LLMs on your own data, offering features from data pre-processing to efficient training and inference. This tool empowers developers to create private, personalized LLMs locally or in their private cloud environments.
DataDreamer: Streamlining Synthetic Data Generation and LLM Workflows
DataDreamer is an open-source Python library designed for efficient prompting, synthetic data generation, and model training workflows. It simplifies the process of creating complex LLM workflows, generating high-quality synthetic datasets, and aligning or fine-tuning models. Built to be simple, efficient, and research-grade, DataDreamer empowers users to build reproducible and shareable AI solutions.
index-tts-lora: High-Quality Speech Synthesis with LoRA Fine-tuning
index-tts-lora offers a robust solution for high-quality speech synthesis, leveraging LoRA fine-tuning on the index-tts framework. It significantly enhances prosody and naturalness for both single and multi-speaker voices. This project provides practical methods for training and inference, making advanced voice synthesis more accessible.
NUDGE: Lightweight Non-Parametric Embedding Fine-Tuning for Retrieval
NUDGE is a lightweight, non-parametric tool designed to fine-tune pre-trained embeddings, significantly enhancing retrieval and RAG pipelines. It operates by adjusting data embeddings directly, rather than modifying model parameters, to maximize accuracy. This approach often leads to over 10% improvement in retrieval accuracy and runs in minutes.