Tags: #fine-tuning

Unsloth Studio provides a unified web interface for efficiently running and training open-source AI models locally across various operating systems and hardware.

PEFT is a state-of-the-art library for Parameter-Efficient Fine-Tuning, drastically reducing the computational and storage costs of adapting large pretrained models.

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 and flexible fine-tuning of large language models.

An open-source LLM observability platform and AI gateway that enables monitoring, evaluation, and experimentation with a single line of code integration.

A low-code development tool for building and iteratively optimizing multi-agent LLM applications with agility and efficiency.

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

Ludwig is a low-code, declarative deep learning framework designed to simplify the building, training, and deployment of custom AI models, including LLMs and neural networks.

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, offering extensive features for fine-tuning, dataset preparation, and model management.

An application for generating high-quality datasets for LLM fine-tuning, RAG, and 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 process for 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 toolkit for efficient large language model training, supporting pre-training, instruction fine-tuning, and DPO with methods like LoRA and QLoRA.

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 toolkit for fine-tuning ChatGLM-6B models using PEFT methods, enabling customization and deployment of large language models.

A comprehensive Linux-based tutorial for deploying and fine-tuning open-source LLMs/MLLMs, tailored for Chinese beginners.

A unified library for parameter-efficient and modular transfer learning, extending HuggingFace Transformers with various adapter methods.

An open-source project providing Chinese LLaMA and Alpaca large language models, enhanced with Chinese vocabulary and data for improved understanding and local deployment on CPU/GPU.

A PyTorch 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.

A tool for fast and efficient fine-tuning of diffusion models using Low-rank Adaptation (LoRA), producing small, shareable models.

YaYi is an open-source Chinese large language model series, built on LLaMA 2 & BLOOM, designed to provide secure, reliable, and domain-specific AI capabilities for enterprise customers through extensive multi-domain instruction tuning.

Provides robust recipes and training code to align 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 diverse fine-tuning and reinforcement learning methods.

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

An implementation of Google's Dreambooth technique on Stable Diffusion, enabling personalized text-to-image model fine-tuning with limited examples.

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

UER-py is an open-source PyTorch-based framework for pre-training and fine-tuning NLP models, offering modularity, extensibility, and a comprehensive model zoo.

A stable and generalizable R1-style Large Vision-Language Model (VLM) framework that enhances visual understanding tasks through reinforced learning, outperforming SFT models in generalization.