Open Source LLMOps

Pragmatic AI Labs via edX

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Overview

Experience Open Source Large Language Models (LLMs) Master cutting-edge LLM architectures like Transformers through hands-on labs Fine-tune models on your data with Sky Pilot's scalable training platform Deploy efficiently with model servers like Lo RAX and v LLM Explore the Open Source LLM Ecosystem: Gain in-depth understanding of how LLMs work under the hood Run pre-trained models like Code Llama, Mistral & Stable Diffusion Discover advanced architectures like Sparse Expert Models Launch cloud GPU instances for accelerated compute Guided LLM...

Syllabus

Week 1: Getting Started with Open Source Ecosystem

Introduction to popular open source natural language processing models and their capabilities
Accessing pre-trained NLP models using libraries like HuggingFace Transformers
Using large language models for synthetic data augmentation to enhance datasets
Building real-world NLP solutions using open source tools in Python and Rust

Week 2: Using Local LLMs from LLamaFile to Whisper.cpp

Key components of LLamaFile for packaging language models into portable files
Running local language models from LLamaFile on your own devices
Automating speech recognition workflows using Whisper.cpp
Integrating Whisper.cpp into GenAI building blocks and applications

Week 3: Applied Projects

Using language models in the browser with Transformers.js and ONNX
Exporting models to the ONNX format for enhanced portability
Developing portable command-line interfaces with the Cosmopolitan project
Building a phrase generator application as a native binary using Cosmopolitan

Week 4: Recap and Final Challenges

Connecting to local language models with APIs using Python
Retrieval augmented generation using local LLMs
Hands-on labs for GPU-accelerated MLOps workflows
Final project to build an interactive LLamaFile sandbox

By the end of this course, learners will have gained practical experience leveraging state-of-the-art open source language models to build AI applications. They will be able to deploy solutions on their own devices as well as integrate models into efficient MLOps pipelines.