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Alfredo Deza
and
Noah Gift
Experience Open Source Large Language Models (LLMs)
Read more
Experience Open Source Large Language Models (LLMs)
Read more
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 SkyPilot's scalable training platform
- Deploy efficiently with model servers like LoRAX and vLLM
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 Project:
- Fine-tune LLaMA, Mistral or other LLMs on your custom dataset
- Leverage SkyPilot to scale training across cloud providers
- Containerize your fine-tuned model for production deployment
- Serve models efficiently with LoRAX, vLLM and other open servers
- Build powerful AI solutions leveraging state-of-the-art open source language models. Gain practical LLMOps skills through code-first learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/0i5dko/open
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What's inside
Learning objectives
- Run local large language models
- Fine-tune llms
- Use open-source generative ai
- Run local large language models
- Fine-tune llms
- Use open-source generative ai
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops professional skills in LLMOps through code-first learning
Taught by Noah Gift and Alfredo Deza, who are recognized authors in the field
Offers hands-on labs and interactive materials, providing practical experience
Provides a comprehensive study of cutting-edge LLM architectures and applications
Recommended for learners with a background in machine learning and natural language processing
Requires access to a computer with GPU capabilities for accelerated compute, which may not be readily available to all learners
Save this course
Save Open Source LLMOps to your list so you can find it easily later:
Save
Save
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 Open Source LLMOps with these
activities:
Review Python Programming Fundamentals
Show steps
Brush up on Python programming basics to ensure a strong foundation for working with LLMs.
Browse courses on
Python Programming
Show steps
-
Review basic Python syntax, data types, and control flow.
-
Practice writing simple Python scripts.
-
Take online tutorials or courses on Python programming.
Read 'Natural Language Processing with Transformers' by Hugging Face
Show steps
Gain a deep understanding of the inner workings of LLM architectures and how Transformer models are used in NLP.
View
Natural Language Processing with Transformers,...
on Amazon
Show steps
-
Read and understand the concepts presented in the book.
-
Work through the code examples and exercises.
-
Apply the knowledge gained to your LLM projects.
Join a Study Group on Open Source LLMs
Show steps
Join a study group with other learners to discuss LLM concepts, share knowledge, and support each other's understanding.
Browse courses on
Open Source LLMs
Show steps
-
Find or create a study group dedicated to Open Source LLMs.
-
Regularly attend study sessions and actively participate in discussions.
-
Share resources, ask questions, and collaborate on projects with group members.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Coding LLM Transformers with Hugging Face
Show steps
Practice coding LLM Transformers using Hugging Face to solidify your understanding of their architecture and functionality.
Browse courses on
Transformers
Show steps
-
Set up a Hugging Face account and install the Transformers library.
-
Load a pre-trained LLM Transformer model, such as GPT-2 or BERT.
-
Write code to generate text, translate languages, or perform other LLM tasks.
-
Experiment with different model parameters and hyperparameters to optimize performance.
Follow Tutorials on Deploying LLMs with LoRAX and vLLM
Show steps
Follow guided tutorials to learn how to deploy your fine-tuned LLMs using LoRAX and vLLM, ensuring efficient and scalable production.
Show steps
-
Find reputable tutorials on LoRAX and vLLM deployment.
-
Follow the tutorials step-by-step, setting up the necessary infrastructure.
-
Deploy your fine-tuned LLM and evaluate its performance.
Fine-tune a LLM for a Specific Task
Show steps
Choose a specific task, such as sentiment analysis or QA, and fine-tune a LLM to enhance its performance on that task.
Show steps
-
Select a pre-trained LLM and a suitable dataset for your task.
-
Prepare and clean the dataset for fine-tuning.
-
Use a fine-tuning framework like Transformers or PyTorch Lightning to customize the LLM.
-
Train and evaluate the fine-tuned LLM on your dataset.
-
Deploy and integrate the fine-tuned LLM in a real-world application.
Contribute to SkyPilot's Training Platform
Show steps
Make contributions to SkyPilot's open-source training platform to gain hands-on experience in LLM training and contribute to the community.
Browse courses on
SkyPilot
Show steps
-
Fork the SkyPilot repository on GitHub.
-
Identify an area for improvement or a new feature to add.
-
Create a feature branch and implement your changes.
-
Write unit tests to validate your changes.
-
Submit a pull request to the SkyPilot repository.
Review Python Programming Fundamentals
Show steps
Brush up on Python programming basics to ensure a strong foundation for working with LLMs.
Browse courses on
Python Programming
Show steps
Show steps
Show steps
- Review basic Python syntax, data types, and control flow.
- Practice writing simple Python scripts.
- Take online tutorials or courses on Python programming.
Read 'Natural Language Processing with Transformers' by Hugging Face
Show steps
Gain a deep understanding of the inner workings of LLM architectures and how Transformer models are used in NLP.
View
Natural Language Processing with Transformers,...
on Amazon
Show steps
Show steps
Show steps
- Read and understand the concepts presented in the book.
- Work through the code examples and exercises.
- Apply the knowledge gained to your LLM projects.
Join a Study Group on Open Source LLMs
Show steps
Join a study group with other learners to discuss LLM concepts, share knowledge, and support each other's understanding.
Browse courses on
Open Source LLMs
Show steps
Show steps
Show steps
- Find or create a study group dedicated to Open Source LLMs.
- Regularly attend study sessions and actively participate in discussions.
- Share resources, ask questions, and collaborate on projects with group members.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Practice Coding LLM Transformers with Hugging Face
Show steps
Practice coding LLM Transformers using Hugging Face to solidify your understanding of their architecture and functionality.
Browse courses on
Transformers
Show steps
Show steps
Show steps
- Set up a Hugging Face account and install the Transformers library.
- Load a pre-trained LLM Transformer model, such as GPT-2 or BERT.
- Write code to generate text, translate languages, or perform other LLM tasks.
- Experiment with different model parameters and hyperparameters to optimize performance.
Follow Tutorials on Deploying LLMs with LoRAX and vLLM
Show steps
Follow guided tutorials to learn how to deploy your fine-tuned LLMs using LoRAX and vLLM, ensuring efficient and scalable production.
Show steps
Show steps
Show steps
- Find reputable tutorials on LoRAX and vLLM deployment.
- Follow the tutorials step-by-step, setting up the necessary infrastructure.
- Deploy your fine-tuned LLM and evaluate its performance.
Fine-tune a LLM for a Specific Task
Show steps
Choose a specific task, such as sentiment analysis or QA, and fine-tune a LLM to enhance its performance on that task.
Show steps
Show steps
Show steps
- Select a pre-trained LLM and a suitable dataset for your task.
- Prepare and clean the dataset for fine-tuning.
- Use a fine-tuning framework like Transformers or PyTorch Lightning to customize the LLM.
- Train and evaluate the fine-tuned LLM on your dataset.
- Deploy and integrate the fine-tuned LLM in a real-world application.
Contribute to SkyPilot's Training Platform
Show steps
Make contributions to SkyPilot's open-source training platform to gain hands-on experience in LLM training and contribute to the community.
Browse courses on
SkyPilot
Show steps
Show steps
Show steps
- Fork the SkyPilot repository on GitHub.
- Identify an area for improvement or a new feature to add.
- Create a feature branch and implement your changes.
- Write unit tests to validate your changes.
- Submit a pull request to the SkyPilot repository.
Career center
Learners who complete Open Source LLMOps will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models. After completing a course on Open Source LLMOps, you will have gained the skills and knowledge needed to fine-tune pre-trained large language models on your own datasets. This is a valuable skill for Machine Learning Engineers to have, as it allows them to customize models for specific tasks and domains. Additionally, this course may help a Machine Learning Engineer build a foundation in cutting-edge LLM architectures like Transformers.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. After completing a course on Open Source LLMOps, you will have gained valuable skills that Data Scientists can use to improve their work, such as how to fine-tune and deploy large language models. Being able to analyze large datasets and identify patterns is a key skill for Data Scientists, and this course can help build a foundation in using cutting-edge LLM architectures like Transformers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. After completing a course on Open Source LLMOps, you will have gained skills that are highly relevant to Software Engineering, such as deploying large language models with model servers like LoRAX and vLLM. Additionally, you will have learned how to run pre-trained models like Code Llama, Mistral and Stable Diffusion.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing (NLP) Engineers design and develop systems that understand and generate human language. This course can be useful for those who wish to enter this career field, as it will help build a foundation in cutting-edge LLM architectures like Transformers as well as how to run pre-trained models like Code Llama and Mistral.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design and develop AI systems. A course on Open Source LLMOps can be useful to those interested in this career field because it teaches the skills needed to fine-tune pre-trained large language models. Additionally, this course will help build a foundation in cutting-edge LLM architectures like Transformers.
Research Scientist
Research Scientist
Research Scientists conduct research in various scientific fields. A course on Open Source LLMOps can be beneficial to those who wish to conduct research in the field of natural language processing or artificial intelligence. This course will teach the skills needed to fine-tune pre-trained large language models and deploy them efficiently with model servers.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. After completing a course on Open Source LLMOps, you will have gained skills that are valuable to Data Analysts, such as how to run pre-trained models like Code Llama, Mistral, and Stable Diffusion.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers conduct research in the field of machine learning. After completing a course on Open Source LLMOps, you will have gained skills that are valuable to Machine Learning Researchers, such as how to fine-tune pre-trained models and deploy them efficiently with model servers.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical techniques to analyze financial data. A course on Open Source LLMOps can provide foundational knowledge in natural language processing and artificial intelligence, which are becoming increasingly important in the financial industry.
NLP Researcher
NLP Researcher
NLP Researchers conduct research in the field of natural language processing. After completing a course on Open Source LLMOps, you will have gained skills that are valuable to NLP Researchers, such as how to fine-tune pre-trained models and deploy them efficiently with model servers.
Linguist
Linguist
Linguists study language and its structure. A course on Open Source LLMOps may be useful to those who wish to enter this career field, as it will help build a foundation in natural language processing and artificial intelligence.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. A course on Open Source LLMOps can be useful to those who wish to enter this career field, as it will help build a foundation in machine learning.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. A course on Open Source LLMOps can provide a good overview of machine learning and natural language processing, which are becoming increasingly important for businesses of all sizes.
Computer Scientist
Computer Scientist
Computer Scientists research and develop computer systems and applications. A course on Open Source LLMOps can provide a good overview of machine learning and natural language processing, which are becoming increasingly important in the field of computer science.
Software Developer
Software Developer
Software Developers design, develop, and maintain software systems. A course on Open Source LLMOps can provide a good overview of machine learning and natural language processing, which are becoming increasingly important for software developers to have.
For more career information including salaries, visit:
OpenCourser.com/course/0i5dko/open
Reading list
We've selected five books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Open Source LLMOps.
Provides a comprehensive guide to LLMs, including their history, architecture, applications, and ethical implications. It valuable resource for anyone who wants to learn more about LLMs.
Provides a comprehensive overview of NLP with Transformers, including a chapter on LLMs. It valuable resource for those who want to learn how to use LLMs for NLP tasks.
Provides a policy guide to using LLMs in government, including recommendations for ethical use and responsible development. It valuable resource for government officials who want to understand the potential benefits and risks of LLMs.
Provides a comprehensive introduction to deep learning, including a chapter on LLMs. It helpful resource for those who want to understand the broader context of LLMs within the field of deep learning.
Provides a glimpse into the future of LLMs, including potential applications and challenges. It valuable resource for those who want to stay up-to-date on the latest developments in the field of LLMs.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0i5dko/open
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Effort
3 to 6 hours per week for 4 weeks
Level
Introductory
Via
edX
Institution
Pragmatic AI Labs
Instructors
Alfredo Deza
Noah Gift
Session
March 13, 2024
- March 14, 2025
Language
English
Transcript
English
Good to know
Develops professional skills in LLMOps through code-first learning
Taught by Noah Gift and Alfredo Deza, who are recognized authors in the field
Offers hands-on labs and interactive materials, providing practical experience
Provides a comprehensive study of cutting-edge LLM architectures and applications
Recommended for learners with a background in machine learning and natural language processing
Requires access to a computer with GPU capabilities for accelerated compute, which may not be readily available to all learners
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