How to boost Mistral 7b with RAG (tutorial)

Welcome back, data enthusiasts! 🚀 Dive into the world of Retrieval Augmented Generation (RAG) with our latest tutorial video. In this comprehensive guide, we'll show you how to harness the power of RAG to elevate your Large Language Models (LLMs) without breaking the bank on extensive fine-tuning.

Join us as we walk you through each step of the process, from project creation to deployment, creating a RAG system with the Mistral 7b Instruct model on the UbiOps platform. Discover why RAG might be the game-changer you've been searching for to tailor your models to specific tasks efficiently and cost-effectively.

By the end of this tutorial, you'll be equipped to take your pre-trained LLMs to new heights, empowered by the robust capabilities of RAG. Say goodbye to the complexities of fine-tuning and hello to total control over your data!

🔍 Learn how to:

- Create a project and API token on UbiOps for seamless management.
- Set up a custom environment to ensure smooth operations.
- Initiate a training run with ease, leveraging environment variables for efficiency.
- Create a deployment effortlessly, containerizing your code and defining inputs/outputs.

⚙️ To successfully complete this guide, you will need:

- UbiOps account with training functionality enabled (see below)

🚀 UbiOps:

🎥 Don't miss out on this opportunity to level up your AI and machine learning game. Hit that like button, share with your fellow tech enthusiasts, and subscribe to stay updated on our latest tutorials and insights. Happy coding! 🚀

Chapters:
0:00 Introduction
1:20 Create project & API token
1:57 Create code environment
2:21 Generate embeddings
2:58 Deploy RAG-enabled LLM
3:40 Conclusion

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