Practical course about Large Language Models.

Official repository of my book "A Hands-On Guide to Fine-Tuning LLMs with PyTorch and Hugging Face"

Repo for Qwen Image Finetune

[SIGIR'24] The official implementation code of MOELoRA.

An Efficient LLM Fine-Tuning Factory Optimized for MoE PEFT

Official implementation of "DoRA: Weight-Decomposed Low-Rank Adaptation"

Code for NOLA, an implementation of "nola: Compressing LoRA using Linear Combination of Random Basis"

【AIGC 实战入门笔记 —— AIGC 摩天大楼】分享 大语言模型（LLMs），大模型高效微调（SFT）,检索增强生成（RAG），智能体（Agent），PPT自动生成, 角色扮演，文生图（Stable Diffusion） ，图像文字识别（OCR），语音识别（ASR），语音合成（TTS），人像分割（SA），多模态（VLM），Ai 换脸(Face Swapping), 文生视频(VD)，图生视频（SVD），Ai 动作迁移，Ai 虚拟试衣，数字人，全模态理解（Omni），Ai音乐生成 干货学习 等 实战与经验。

[ICML'24 Oral] APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference

Official code implemtation of paper AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

CRE-LLM: A Domain-Specific Chinese Relation Extraction Framework with Fine-tuned Large Language Model

memory-efficient fine-tuning; support 24G GPU memory fine-tuning 7B

[ICML 2025] Fast and Low-Cost Genomic Foundation Models via Outlier Removal.

AI Community Tutorial, including: LoRA/Qlora LLM fine-tuning, Training GPT-2 from scratch, Generative Model Architecture, Content safety and control implementation, Model distillation techniques, Dreambooth techniques, Transfer learning, etc for practice with real project!

High Quality Image Generation Model - Powered with NVIDIA A100

An ultra-lightweight neural machine translation model fine-tuned specifically for Persian-to-English tasks, leveraging efficient PEFT (LoRA) techniques to deliver strong performance while staying fast and highly resource-efficient for real-world deployment.

A no-code toolkit to finetune LLMs on your local GPU—just upload data, pick a task, and deploy later. Perfect for hackathons or prototyping, with automatic hardware detection and a guided React interface.

A Python library for efficient and flexible cycle-consistency training of transformer models via iteratie back-translation. Memory and compute efficient techniques such as PEFT adapter switching allow for 7.5x larger models to be trained on the same hardware.

Addestra il tuo Mini Language Model!

Mistral and Mixtral (MoE) from scratch

EDoRA: Efficient Weight-Decomposed Low-Rank Adaptation via Singular Value Decomposition

Official repository of FEVER@ACL 2025 paper "When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verification"

FineTuning LLMs on conversational medical dataset.

Fine-tune StarCoder2-3b for SQL tasks on limited resources with LORA. LORA reduces model size for faster training on smaller datasets. StarCoder2 is a family of code generation models (3B, 7B, and 15B), trained on 600+ programming languages from The Stack v2 and some natural language text such as Wikipedia, Arxiv, and GitHub issues.

LLMs fine-tuned with social media comments on cheap hardware

PEFT Customer Support Chatbot

On-device DPO + QLoRA fine-tuning pipeline to ground a SLM in oil-and-gas jargons. Cloudera AMP: https://cloudera.github.io/Applied-ML-Prototypes/#/community

모두에게 쉬운 파인튜닝 툴

Parameter-efficient fine-tuning toolkit for LLMs using LoRA and QLoRA. Colab-ready notebooks with detailed theory, code explanations, and production deployment guides. Supports Llama-2, Mistral, and other open-source models.

Fine-Tuning Llama 3-8B for Structured Math Reasoning: Fine-tuning Llama3 8b to generate JSON formats for arithmetic questions and process the output to perform calculations.