/Building-with-Instruction-Tuned-LLMs-A-Step-by-Step-Guide

👋 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!

🪡 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