LLMOps: Deploying LLMs and Scaling using Modal, LangChain and Huggingface

In this video, you'll learn about LLMOps, the practice of deploying and scaling LLMs using Modal, Langchain and Huggingface.
In the rapidly evolving domain of Large Language Models (LLMs), businesses and researchers grapple with the challenges of efficiently deploying, monitoring and scaling these models. The operational complexities, from infrastructure management to ensuring context-aware responses and efficient token streaming, pose significant obstacles.
Addressing the complexities of deploying, monitoring, and scaling Large Language Models requires a blend of specialized tools and methodologies.
Our approach is built upon the following tools:
🤗 Hugging Face: Tapping into their expansive repository of pre-trained models to establish a robust foundational base for LLM deployment.
🚀 vLLM: An open-source toolkit designed specifically for accelerated inference and seamless serving of LLMs, ensuring both speed and reliability. ☁️ Modal: This cloud code execution platform is key for abstracting away infrastructure complexities, enabling streamlined deployment and scaling of a wide range of LLMs, encompassing both proprietary and open-source variants.
🦜🔗LangChain: A dynamic framework tailored for the development of LLM-driven applications. With its modular components and readily available chains, it empowers LLMs to be both context-aware and adept at reasoning.
By integrating these tools and techniques, we manage to deliver a comprehensive solution that simplifies LLM operations, from deployment to scaling, while ensuring consistent exceptional performance without compromising on functionality.
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Resources:
💻 Code: github.com/Blaizzy/LLMOps
📋 Presentation Slides: docs.google.com/presentation/...