Axolotl: Fine Tuning for Beginners with Less Code

🔥 Dive deep into the world of AI fine-tuning with our latest video on Axolotl, the ultimate platform for customising a wide array of AI models like Mistral, Mixl, Falcon Fi, and more. Whether you're a beginner or an expert, Axolotl's low-code approach simplifies the process, allowing you to fine-tune models with just a YAML file. 🚀 Massed compute: https://bit.ly/mervin-praison Coupon: MervinPraison (50% Discount) Connect to Massed Compute after Deploy: https://www.cendio.com/thinlinc/download/ In this video, I'm thrilled to show you how to fine-tune the OpenLlama 3-billion-parameter model to improve its response quality. We'll go through each step, from setting up your instance with Massed compute (don't forget to use the code "MervinPraison" for a 50% discount!) to running your model on Hugging Face. 🎓 Don't miss out on my tips for efficient AI model training and customisation. Subscribe and hit the bell icon to stay updated on all things Artificial Intelligence. 💡 👉 What you'll learn: How to utilise Axolotl for AI model fine-tuning Setting up your environment for fine-tuning Step-by-step guide to training and deploying your AI model How to save and upload your model to Hugging Face 💬 Join the conversation! Share your thoughts in the comments below. If you found this video helpful, make sure to like and share it with others interested in AI fine-tuning. 🔗 Useful Links: Massed compute: https://bit.ly/mervin-praison Connect to Massed Compute: https://www.cendio.com/thinlinc/download/ Hugging Face: Axolotl Official Page: https://github.com/OpenAccess-AI-Collective/axolotl Code: https://mer.vin/2024/03/axolotl-fine-tuning/ Timestamps: 0:00 - Introduction to Axolotl and its capabilities 0:03 - Overview of supported AI models 0:17 - Benefits of low-code fine-tuning 0:41 - Fine-tuning OpenLlama 3-billion-parameter model 1:05 - Subscribing for more AI content 1:17 - Setting up your instance on Massed compute 2:02 - Preparing for fine-tuning: From git clone to