Tags: #fine-tuning

Unsloth Studio is a web UI that enables efficient local training and inference of open-source large language models and other AI models with significant VRAM and speed optimizations.

A scalable and lightweight infrastructure for fine-tuning, inference, and deployment of over 1000 large language models (LLMs) and multimodal large language models (MLLMs) using advanced techniques.

A state-of-the-art library for Parameter-Efficient Fine-Tuning (PEFT) of large pretrained models, drastically reducing computational and storage costs.

A unified and efficient framework for fine-tuning over 100 large language models (LLMs) and vision-language models (VLMs) with both CLI and Web UI.

A free and open-source framework designed for efficient fine-tuning of large language models.

LazyLLM simplifies the creation and iterative optimization of multi-agent large language model (LLM) applications with a low-code approach.

A free, all-in-one platform for building, evaluating, and optimizing AI systems, offering tools for RAG, agents, fine-tuning, and synthetic data generation.

A free, hands-on course to build a production-ready LLM & RAG system, including a personalized AI replica, applying LLMOps best practices.

A user-friendly, versatile fine-tuning kit for image, video, and audio diffusion models, emphasizing simplicity and cutting-edge features.

A comprehensive, one-stop solution for training various diffusion models with advanced features and a user-friendly interface.

Easy Dataset is a powerful application for creating high-quality datasets for LLM fine-tuning, RAG, and model evaluation, featuring intelligent document processing and a comprehensive evaluation system.

A no-code GUI and framework for easily fine-tuning state-of-the-art large language models (LLMs).

A streamlined tool to accelerate the fine-tuning of popular multimodal models like Florence-2, PaliGemma 2, and Qwen2.5-VL.

Enables fast and efficient multi-concept customization of text-to-image diffusion models like Stable Diffusion using a few images.

A specialized hub providing models, datasets, and fine-tuning techniques to enhance Large Language Models' performance in Text-to-SQL, Text-to-NLU, and Text-to-GQL tasks.

Firefly is an open-source, all-in-one tool designed for efficient pre-training, instruction fine-tuning, and DPO of a wide range of mainstream large language models, optimized for resource-constrained environments.

A comprehensive code repository accompanying a hands-on course for mastering Huggingface Transformers, covering fundamental concepts to advanced fine-tuning and deployment techniques.

An efficient framework for fine-tuning ChatGLM-6B and ChatGLM2-6B models using PEFT methods, including LoRA, QLoRA, and RLHF, with a Web UI.

A comprehensive Linux-based guide for beginners to quickly fine-tune and deploy open-source LLMs and MLLMs, tailored for Chinese learners.

An open-source project providing Chinese LLaMA and instruction-tuned Alpaca large language models, optimized for Chinese NLP and local deployment on CPU/GPU.

A Python library implementing LoRA (Low-Rank Adaptation) to efficiently fine-tune large language models by significantly reducing trainable parameters and storage requirements.

LongLoRA is an efficient fine-tuning method and associated models/datasets designed to extend the context window of Large Language Models (LLMs) for processing longer inputs.

Enables rapid and efficient fine-tuning of diffusion models, particularly Stable Diffusion, using Low-rank Adaptation (LoRA) to generate high-quality, custom images with significantly smaller model sizes.

YaYi is an open-source Chinese Large Language Model, built on LLaMA 2 & BLOOM, designed for secure, reliable, and domain-specific applications through extensive instruction tuning.

Provides robust training recipes and scripts to align large language models with human and AI preferences, enhancing helpfulness and safety.

A modular framework for aligning any-modality large models with human intentions and values using various fine-tuning and reinforcement learning methods.

An open-source project providing Chinese LLaMA-2 and Alpaca-2 large language models with expanded Chinese vocabulary, enhanced capabilities, and support for ultra-long contexts up to 64K.

This project implements Google's Dreambooth technique on Stable Diffusion, enabling users to fine-tune a text-to-image model with a few custom examples for personalized image generation.

A framework for embedding secret cryptographic fingerprints into Large Language Models (LLMs) via fine-tuning to verify ownership and prevent unauthorized use.

An open-source PyTorch-based framework for NLP pre-training and fine-tuning, offering modularity, reproducibility, and a comprehensive model zoo for various downstream tasks.

VLM-R1 is a stable and generalizable R1-style Large Vision-Language Model that leverages reinforcement learning to significantly improve visual understanding tasks.

LyCORIS is a library implementing various parameter-efficient fine-tuning (PEFT) algorithms for Stable Diffusion, extending beyond conventional LoRA methods to enhance model adaptation.