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Beginning Llamafile

Alfredo Deza and Noah Gift

In this course, you will:

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In this course, you will:

  • Gain the skills to expose large language models through REST API endpoints
  • Learn how to configure the llama.cpp server to customize model behavior
  • Understand how to efficiently handle requests and integrate language model capabilities into applications
  • Reinforce concepts through hands-on exercises and code examples using tools like curl and Python
  • Be equipped to deploy robust language model APIs for various NLP tasks

The course empowers you to harness state-of-the-art NLP models in your projects through a convenient and performant API interface, focusing on the practical aspects of serving large language models in production environments using the efficient and flexible llama.cpp framework.

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What's inside

Learning objectives

  • Installing and using the cosmopolitan libc toolkit
  • Running language models locally with llamafile
  • Understanding the mixtral model license and llamafile packaging
  • Developing portable command-line interfaces with cosmopolitan
  • Interacting with the llamafile api for nlp tasks

Syllabus

Module 1: Getting Started with Mozilla Llamafile (2 hours)
****
- Video: Meet your instructor: Alfredo Deza (1 minute) [Preview module]
- Reading: Meet your instructor: Noah Gift (1 minute)
Read more
- Reading: Connect with your instructors (1 minute)
- Reading: Course structure and etiquette (1 minute)
- Reading: Key Terms (5 minutes)
- Reading: What is Llamafile? (5 minutes)
- Video: Llamafile overview by Mozilla (5 minutes)
- Video: Using the Llamafile API (2 minutes)
- Video: Creating a Llamafile (5 minutes)
- Reading: Cosmopolitan (5 minutes)
- Video: Building portable binaries with Cosmopolitan (4 minutes)
- Video: Building a phrase generator with cosmopolitan (3 minutes)
- Reading: Lesson Reflection (5 minutes)
- Assignment: Quiz-Key Components of Llamafile (10 minutes)
- Reading: Key Terms (1 minute)
- Reading: Bash Phrase Generator (5 minutes)
- Ungraded Lab: Cosmopolitan (10 minutes)
- Assignment: Quiz-Portable CLI with Cosmopolitan (10 minutes)
- Reading: What are LLMs? (5 minutes)
- Video: Getting Started with Llamafile (3 minutes)
- Video: Llamafile local system metrics (3 minutes)
- Ungraded Lab: Portable CLI (10 minutes)
- Assignment: Quiz-Running Llamafile (10 minutes)
- Reading: Key Terms (1 minute)
- Reading: Llamafile server (5 minutes)
- Ungraded Lab: Local Llamafile API (10 minutes)
- Reading: Course Conclusion (5 minutes)
- Reading: Next Steps (1 minute)
- Assignment: Final Quiz-Llamafile (10 minutes)
- Discussion Prompt: Meet and Greet (optional) (1 minute)

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Utilizes the Mozilla Llamafile framework, which is a standard in LLM deployment and execution
Taught by Alfredo Deza and Noah Gift, who are recognized researchers and experts in AI and NLP
Provides a practical focus on serving large language models (LLMs) in production environments
Covers topics such as deploying robust language model APIs, handling requests efficiently, and integrating NLP capabilities into applications
Incorporates hands-on exercises and code examples to reinforce concepts and ensure practical understanding
Assumes some prior knowledge in NLP, machine learning, and software development

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Beginning Llamafile with these activities:
Review Lambda calculus
Refamiliarize yourself with the basics of lambda calculus to strengthen your understanding of functional programming concepts.
Show steps
  • Review online resources on lambda calculus
  • Solve practice problems involving lambda expressions
Peer Review: Language Model API Design
Enhance your API design skills by engaging in peer reviews, where you exchange feedback on your language model API designs, identifying areas for improvement and best practices.
Browse courses on API Design
Show steps
  • Pair up with a peer who has a similar level of experience.
  • Share your API design with each other.
  • Provide constructive feedback on the clarity, completeness, and effectiveness of the design.
  • Discuss alternative approaches and explore potential improvements.
Guided Practice: Developing Robust API Endpoints
Reinforce your understanding of the hands-on exercises by practicing on your own, focusing on refining your ability to create effective API endpoints for your language model.
Browse courses on API Development
Show steps
  • Set up your local environment using the provided materials.
  • Create a new API endpoint using the REST API framework.
  • Implement basic language model functionality, such as text generation or sentiment analysis.
  • Test your endpoint thoroughly to ensure it meets your requirements.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Blog Post: Use Cases of LLMs in NLP Applications
Solidify your understanding by creating a blog post that explores real-world use cases of large language models in various NLP applications, providing concrete examples and demonstrating their impact.
Browse courses on NLP Applications
Show steps
  • Research different NLP applications, such as chatbots, text summarization, and language translation.
  • Identify how LLMs are used in each application.
  • Provide specific examples and case studies to illustrate the benefits and challenges of using LLMs.
  • Discuss the ethical implications and limitations of using LLMs.
Presentation: Language Model Deployment Strategy
Demonstrate your understanding of language model deployment by creating a presentation that outlines your strategy for deploying a language model in a production environment, considering scalability, performance, and security.
Browse courses on Deployment Strategies
Show steps
  • Research different deployment options and architectures.
  • Design a deployment strategy that meets your specific requirements.
  • Consider factors such as cost, availability, and maintenance.
  • Develop a roadmap for implementing your deployment strategy.
Tutorial: Optimizing Llamafile for Production Deployment
Extend your knowledge by exploring advanced tutorials that focus on optimizing the llamafile configuration for production-grade deployment, ensuring efficient and scalable language model serving.
Browse courses on Performance Optimization
Show steps
  • Identify performance bottlenecks in your Llamafile.
  • Implement techniques for caching, batching, and parallelization.
  • Configure monitoring and logging for your deployed language model.
  • Test and evaluate your optimizations to measure improvements.
Challenge: Language Model Hackathon
Test your skills and push the boundaries of language model applications by participating in a hackathon that presents real-world challenges, encouraging innovation and collaboration.
Show steps
  • Form a team or participate individually.
  • Choose a challenge that aligns with your interests and expertise.
  • Develop a creative and effective solution using large language models.
  • Present your solution to a panel of judges.

Career center

Learners who complete Beginning Llamafile will develop knowledge and skills that may be useful to these careers:

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Course image
Effort
1 to 3 hours per week for 2 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Alfredo Deza
Noah Gift
Session
April 23, 2024 - April 23, 2025
Language
English
Transcript
English
Good to know
Utilizes the Mozilla Llamafile framework, which is a standard in LLM deployment and execution
Taught by Alfredo Deza and Noah Gift, who are recognized researchers and experts in AI and NLP
Provides a practical focus on serving large language models (LLMs) in production environments
Covers topics such as deploying robust language model APIs, handling requests efficiently, and integrating NLP capabilities into applications
Incorporates hands-on exercises and code examples to reinforce concepts and ensure practical understanding
Assumes some prior knowledge in NLP, machine learning, and software development
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