Create Custom LLMs

Mastering Applied AI, One Concept at a Time

End to End Generative AI Industry Projects on LLM Models with Deployment_Awesome LLM Projects

Auto Data is a library designed for quick and effortless creation of datasets tailored for fine-tuning Large Language Models (LLMs).

🚀 Easy, open-source LLM finetuning with one-line commands, seamless cloud integration, and popular optimization frameworks. ✨

On Memorization of Large Language Models in Logical Reasoning

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

MediNotes: SOAP Note Generation through Ambient Listening, Large Language Model Fine-Tuning, and RAG

A Gradio web UI for Large Language Models. Supports LoRA/QLoRA finetuning,RAG(Retrieval-augmented generation) and Chat

Fine-tune Mistral 7B to generate fashion style suggestions

the small distributed language model toolkit; fine-tune state-of-the-art LLMs anywhere, rapidly

[NeurIPS 2023] Make Your Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning

Medical Language Model fine-tuned using pretraining, instruction tuning, and Direct Preference Optimization (DPO). Progresses from general medical knowledge to specific instruction following, with experiments in preference alignment for improved medical text generation and understanding.

GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

SEIKO is a novel reinforcement learning method to efficiently fine-tune diffusion models in an online setting. Our methods outperform all baselines (PPO, classifier-based guidance, direct reward backpropagation) for fine-tuning Stable Diffusion.

A open-source framework designed to adapt pre-trained Language Models (LLMs), such as Llama, Mistral, and Mixtral, to a wide array of domains and languages.

[EMNLP 2024] Quantize LLM to extremely low-bit, and finetune the quantized LLMs

Fine-Tuning LLMs (Gemma, LLaMA, Mistral, etc.) A practical guide to fine-tuning various large language models using popular frameworks. Includes examples, scripts, and tips for efficient training on custom datasets.

A curated list of Parameter Efficient Fine-tuning papers with a TL;DR

IndexTTS Fine-tuning notebooks