👋 Welcome to the Support Repository for the DeepLearningAI Event: Building with Instruction-Tuned LLMs: A Step-by-Step Guide

Here are a collection of resources you can use to help fine-tune your LLMs, as well as create a few simple LLM powered applications!

🗣️ Slides:

Here are the slides used for the event: Building with Instruction-Tuned LLMs

🪡 Fine-tuning Approaches:

Instruct-tuning OpenLM's OpenLLaMA on the Dolly-15k Dataset Notebooks:

Notebook	Purpose	Link
Instruct-tuning Leveraging QLoRA	Supervised fine-tuning!	Here
Instruct-tuning Leveraging Lit-LLaMA	Using Lightning-AI's Lit-LLaMA frame for Supervised fine-tuning	Here
Natural Language to SQL fine-tuning using Lit-LLaMA	Using Lightning-AI's Lit-LLaMA frame for Supervised fine-tuning on the Natural Language to SQL task	Here

MarketMail Using BLOOMz Resources:

Notebook	Purpose	Link
BLOOMz-LoRA Unsupervised Fine-tuning Notebook	Fine-tuning BLOOMz with an unsupervised approach using Sythetic Data!	Here
Creating Synthetic Data with GPT-3.5-turbo	Generate Data for Your Model!	Here

🏗️ Converting Models into Applications:

Notebook	Purpose	Link
Open-source LangChain Example	Leveraging LangChain to build a Hugging Face 🤗 Powered Application	Here
Open AI LangChain Example	Building an Open AI Powered Application	Here

🖥️ Demos:

Demo	Info	Link
Instruct-tuned Chatbot Leveraging QLoRA	This demo is currently powered by the Guanaco Model - will be updated once our instruct-tuned model finishes training!	Here
TalkToMyDoc	Query the first Hitch Hiker's Guide book!	Here