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