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Beginning Llamafile for Local Large Language Models (LLMs)
Noah Gift
and
Alfredo Deza
Noah Gift
and
Alfredo Deza
Learners will gain the skills to serve powerful language models as practical and scalable web APIs. They will learn how to use the llama.cpp example server to expose a large language model through a set of REST API endpoints for tasks like text generation, tokenization, and embedding extraction.
Read more
Learners will gain the skills to serve powerful language models as practical and scalable web APIs. They will learn how to use the llama.cpp example server to expose a large language model through a set of REST API endpoints for tasks like text generation, tokenization, and embedding extraction.
Read more
Learners will gain the skills to serve powerful language models as practical and scalable web APIs. They will learn how to use the llama.cpp example server to expose a large language model through a set of REST API endpoints for tasks like text generation, tokenization, and embedding extraction.
The course dives into the technical details of running the llama.cpp server, configuring various options to customize model behavior, and efficiently handling requests. Learners will understand how to interact with the API using tools like curl and Python, allowing them to integrate language model capabilities into their own applications.
Throughout the course, hands-on exercises and code examples reinforce the concepts and provide learners with practical experience in setting up and using the llama.cpp server. By the end, participants will be equipped to deploy robust language model APIs for a variety of natural language processing tasks.
The course stands out by focusing on the practical aspects of serving large language models in production environments using the efficient and flexible llama.cpp framework. It empowers learners to harness the power of state-of-the-art NLP models in their projects through a convenient and performant API interface.
Register for this course and see more details by visiting:
OpenCourser.com/course/0dm658/beginning
What's inside
Syllabus
Getting Started with Mozilla Llamafile
This week, you run language models locally. Keep data private. Avoid latency and fees. Use Mixtral model and llamafile.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Demonstrates expertise in serving large language models through REST APIs, ideal for learners pursuing careers in NLP or related fields
Led by experienced instructors Noah Gift and Alfredo Deza, both recognized for their contributions in the field of NLP
Provides hands-on exercises and code examples for practical experience in setting up and using the llama.cpp server
Empowers learners to harness the power of state-of-the-art NLP models in their projects, providing an edge for those seeking to develop innovative solutions
Places emphasis on the practical implementation and deployment of large language models, catering to learners with a focus on real-world applications
May require learners to have prior experience with NLP and related technologies to fully grasp the concepts presented
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Activities
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We're preparing activities for
Beginning Llamafile for Local Large Language Models (LLMs).
These are activities you can do either before, during, or after a course.
Career center
Learners who complete Beginning Llamafile for Local Large Language Models (LLMs) will develop knowledge and skills
that may be useful to these careers:
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers specialize in developing algorithms and systems that can understand and process human language. This course may be useful to Natural Language Processing Engineers interested in using large language models to improve their work, such as by using them to develop new natural language processing applications or improve the performance of existing ones.
Data Scientist
Data Scientist
Data Scientists use their knowledge of statistics, mathematics, and computer science to extract insights from data. This course may be useful to Data Scientists interested in using large language models to improve their work, such as by using them to develop new data science applications or improve the performance of existing ones.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning systems. This course may be useful to Machine Learning Engineers interested in using large language models in their work, such as by using them to train new models or improve the performance of existing models.
Technical Writer
Technical Writer
Technical Writers create user manuals, how-to guides, and other documentation for software and other products. This course may be useful to Technical Writers interested in using large language models to improve their work, such as by using them to generate new documentation or improve the quality of existing documentation.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces for software and other products. This course may be useful to User Experience Designers interested in using large language models to improve their work, such as by using them to generate new user interface ideas or improve the usability of existing user interfaces.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course may be useful to Software Engineers interested in using large language models in their work, such as by using them to automate code generation or improve the quality of code.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course may be useful to Product Managers interested in using large language models to improve their work, such as by using them to generate new product ideas or improve the user experience of existing products.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with a company's products and services. This course may be useful to Customer Success Managers interested in using large language models to improve their work, such as by using them to generate new customer support content or improve the quality of existing customer support interactions.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. This course may be useful to Project Managers interested in using large language models to improve their work, such as by using them to generate new project plans or improve the communication of project status.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course may be useful to Marketing Managers interested in using large language models to improve their work, such as by using them to generate new marketing content or improve the targeting of existing marketing campaigns.
Data Analyst
Data Analyst
Data Analysts use their quantitative skills to turn raw data into information that organizations can use to make better decisions. This course may be useful to Data Analysts interested in using large language models to improve their data analysis processes, such as by automating data cleaning or generating insights from unstructured text data.
Sales Manager
Sales Manager
Sales Managers are responsible for leading sales teams and generating revenue. This course may be useful to Sales Managers interested in using large language models to improve their work, such as by using them to generate new sales leads or improve the effectiveness of existing sales pitches.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the recruitment, hiring, and development of employees. This course may be useful to Human Resources Managers interested in using large language models to improve their work, such as by using them to generate new recruiting content or improve the efficiency of existing hiring processes.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a company. This course may be useful to Operations Managers interested in using large language models to improve their work, such as by using them to generate new operational procedures or improve the efficiency of existing ones.
Financial Analyst
Financial Analyst
Financial Analysts provide financial advice to individuals and organizations. This course may be useful to Financial Analysts interested in using large language models to improve their work, such as by using them to generate new financial models or improve the accuracy of existing ones.
For more career information including salaries, visit:
OpenCourser.com/course/0dm658/beginning
Reading list
We've selected nine 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
Beginning Llamafile for Local Large Language Models (LLMs).
Provides a comprehensive overview of deep learning techniques for natural language processing. It covers a wide range of topics, including word embeddings, recurrent neural networks, and transformers.
Provides a comprehensive overview of speech and language processing. It covers a wide range of topics, including speech recognition, natural language understanding, and dialogue systems.
Provides a comprehensive overview of natural language understanding. It covers a wide range of topics, including information extraction, question answering, and machine translation.
Provides a comprehensive overview of deep learning in Python. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of the Natural Language Toolkit (NLTK), a popular natural language processing library. It covers a wide range of topics, including text preprocessing, feature engineering, and machine learning models.
Provides a comprehensive overview of Python for data analysis. It covers a wide range of topics, including data wrangling, data visualization, and machine learning.
Provides a comprehensive overview of data science from scratch. It covers a wide range of topics, including data wrangling, data visualization, and machine learning.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of reinforcement learning. It covers a wide range of topics, including Markov decision processes, value functions, and policy gradients.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/0dm658/beginning
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Effort
2 hours total
Level
Introductory
Via
Coursera
Institution
Duke University
Instructors
Noah Gift
Alfredo Deza
Language
English
Good to know
Demonstrates expertise in serving large language models through REST APIs, ideal for learners pursuing careers in NLP or related fields
Led by experienced instructors Noah Gift and Alfredo Deza, both recognized for their contributions in the field of NLP
Provides hands-on exercises and code examples for practical experience in setting up and using the llama.cpp server
Empowers learners to harness the power of state-of-the-art NLP models in their projects, providing an edge for those seeking to develop innovative solutions
Places emphasis on the practical implementation and deployment of large language models, catering to learners with a focus on real-world applications
May require learners to have prior experience with NLP and related technologies to fully grasp the concepts presented
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