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Fine-Tuning Models and Large Language Models (LLMs) Training Course

Fine-Tuning Models and Large Language Models (LLMs) Training Course

Fine-tuning models and LLMs is a key process in adapting pre-trained machine learning models to specific tasks and datasets. This course explores the techniques, tools, and best practices for fine-tuning, focusing on practical implementations and optimization strategies for achieving high performance.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to customize pre-trained models for specific tasks and datasets.

By the end of this training, participants will be able to:

Understand the principles of fine-tuning and its applications.
Prepare datasets for fine-tuning pre-trained models.
Fine-tune large language models (LLMs) for NLP tasks.
Optimize model performance and address common challenges.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

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Course Outline

Introduction to Fine-Tuning

What is fine-tuning?
Use cases and benefits of fine-tuning
Overview of pre-trained models and transfer learning

Preparing for Fine-Tuning

Collecting and cleaning datasets
Understanding task-specific data requirements
Exploratory data analysis and preprocessing

Fine-Tuning Techniques

Transfer learning and feature extraction
Fine-tuning transformers with Hugging Face
Fine-tuning for supervised vs unsupervised tasks

Fine-Tuning Large Language Models (LLMs)

Adapting LLMs for NLP tasks (e.g., text classification, summarization)
Training LLMs with custom datasets
Controlling LLM behavior with prompt engineering

Optimization and Evaluation

Hyperparameter tuning
Evaluating model performance
Addressing overfitting and underfitting

Scaling Fine-Tuning Efforts

Fine-tuning on distributed systems
Leveraging cloud-based solutions for scalability
Case studies: Large-scale fine-tuning projects

Best Practices and Challenges

Best practices for fine-tuning success
Common challenges and troubleshooting
Ethical considerations in fine-tuning AI models

Advanced Topics (Optional)

Fine-tuning multi-modal models
Zero-shot and few-shot learning
Exploring LoRA (Low-Rank Adaptation) techniques

Summary and Next Steps

Requirements

Understanding of machine learning fundamentals
Experience with Python programming
Familiarity with pre-trained models and their applications

Audience

Data scientists
Machine learning engineers
AI researchers

14 Hours

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Advanced Techniques in Transfer Learning

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level machine learning professionals who wish to master cutting-edge transfer learning techniques and apply them to complex real-world problems.

By the end of this training, participants will be able to:

Understand advanced concepts and methodologies in transfer learning.
Implement domain-specific adaptation techniques for pre-trained models.
Apply continual learning to manage evolving tasks and datasets.
Master multi-task fine-tuning to enhance model performance across tasks.

AI Automation with n8n and LangChain

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at developers and IT professionals of all skill levels who wish to automate tasks and processes using AI without writing extensive code.

By the end of this training, participants will be able to:

Design and implement complex workflows using n8n's visual programming interface.
Integrate AI capabilities into workflows using LangChain.
Build custom chatbots and virtual assistants for various use cases.
Perform advanced data analysis and processing with AI agents.

Automating Workflows with LangChain and APIs

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at beginner-level business analysts and automation engineers who wish to understand how to use LangChain and APIs for automating repetitive tasks and workflows.

By the end of this training, participants will be able to:

Understand the basics of API integration with LangChain.
Automate repetitive workflows using LangChain and Python.
Utilize LangChain to connect various APIs for efficient business processes.
Create and automate custom workflows using APIs and LangChain’s automation capabilities.

Building Conversational Agents with LangChain

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level professionals who wish to deepen their understanding of conversational agents and apply LangChain to real-world use cases.

By the end of this training, participants will be able to:

Understand the fundamentals of LangChain and its application in building conversational agents.
Develop and deploy conversational agents using LangChain.
Integrate conversational agents with APIs and external services.
Apply Natural Language Processing (NLP) techniques to improve the performance of conversational agents.

Building Private AI Workflows with Ollama

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level professionals who wish to implement secure and efficient AI-driven workflows using Ollama.

By the end of this training, participants will be able to:

Deploy and configure Ollama for private AI processing.
Integrate AI models into secure enterprise workflows.
Optimize AI performance while maintaining data privacy.
Automate business processes with on-premise AI capabilities.
Ensure compliance with enterprise security and governance policies.

Deploying Fine-Tuned Models in Production

21 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.

By the end of this training, participants will be able to:

Understand the challenges of deploying fine-tuned models into production.
Containerize and deploy models using tools like Docker and Kubernetes.
Implement monitoring and logging for deployed models.
Optimize models for latency and scalability in real-world scenarios.

Deploying and Optimizing LLMs with Ollama

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level professionals who wish to deploy, optimize, and integrate LLMs using Ollama.

By the end of this training, participants will be able to:

Set up and deploy LLMs using Ollama.
Optimize AI models for performance and efficiency.
Leverage GPU acceleration for improved inference speeds.
Integrate Ollama into workflows and applications.
Monitor and maintain AI model performance over time.

Ethical Considerations in AI Development with LangChain

21 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level AI researchers and policy makers who wish to explore the ethical implications of AI development and learn how to apply ethical guidelines when building AI solutions with LangChain.

By the end of this training, participants will be able to:

Identify key ethical issues in AI development with LangChain.
Understand the impact of AI on society and decision-making processes.
Develop strategies for building fair and transparent AI systems.
Implement ethical AI guidelines into LangChain-based projects.

Enhancing User Experience with LangChain in Web Apps

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level web developers and UX designers who wish to leverage LangChain to create intuitive and user-friendly web applications.

By the end of this training, participants will be able to:

Understand the fundamental concepts of LangChain and its role in enhancing web user experience.
Implement LangChain in web apps to create dynamic and responsive interfaces.
Integrate APIs into web apps to improve interactivity and user engagement.
Optimize user experience using LangChain’s advanced customization features.
Analyze user behavior data to fine-tune web app performance and experience.

Fine-Tuning and Customizing AI Models on Ollama

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level professionals who wish to fine-tune and customize AI models on Ollama for enhanced performance and domain-specific applications.

By the end of this training, participants will be able to:

Set up an efficient environment for fine-tuning AI models on Ollama.
Prepare datasets for supervised fine-tuning and reinforcement learning.
Optimize AI models for performance, accuracy, and efficiency.
Deploy customized models in production environments.
Evaluate model improvements and ensure robustness.

LangChain: Building AI-Powered Applications

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level developers and software engineers who wish to build AI-powered applications using the LangChain framework.

By the end of this training, participants will be able to:

Understand the fundamentals of LangChain and its components.
Integrate LangChain with large language models (LLMs) like GPT-4.
Build modular AI applications using LangChain.
Troubleshoot common issues in LangChain applications.

Integrating LangChain with Cloud Services

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at advanced-level data engineers and DevOps professionals who wish to leverage LangChain's capabilities by integrating it with various cloud services.

By the end of this training, participants will be able to:

Integrate LangChain with major cloud platforms such as AWS, Azure, and Google Cloud.
Utilize cloud-based APIs and services to enhance LangChain-powered applications.
Scale and deploy conversational agents to the cloud for real-time interaction.
Implement monitoring and security best practices in cloud environments.

LangChain for Data Analysis and Visualization

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at intermediate-level data professionals who wish to use LangChain to enhance their data analysis and visualization capabilities.

By the end of this training, participants will be able to:

Automate data retrieval and cleaning using LangChain.
Conduct advanced data analysis using Python and LangChain.
Create visualizations with Matplotlib and other Python libraries integrated with LangChain.
Leverage LangChain for generating natural language insights from data analysis.

LangChain Fundamentals

14 Hours

This instructor-led, live training in Hong Kong (online or onsite) is aimed at beginner-level to intermediate-level developers and software engineers who wish to learn the core concepts and architecture of LangChain and gain the practical skills for building AI-powered applications.

By the end of this training, participants will be able to:

Grasp the fundamental principles of LangChain.
Set up and configure the LangChain environment.
Understand the architecture and how LangChain interacts with large language models (LLMs).
Develop simple applications using LangChain.

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This instructor-led, live training in Hong Kong (online or onsite) is aimed at beginner-level professionals who wish to install, configure, and use Ollama for running AI models on their local machines.

By the end of this training, participants will be able to:

Understand the fundamentals of Ollama and its capabilities.
Set up Ollama for running local AI models.
Deploy and interact with LLMs using Ollama.
Optimize performance and resource usage for AI workloads.
Explore use cases for local AI deployment in various industries.

Fine-Tuning Models and Large Language Models (LLMs) Training Course

Course Outline

Requirements

Provisional Upcoming Courses (Require 5+ participants)

Fine-Tuning Models and Large Language Models (LLMs)

Fine-Tuning Models and Large Language Models (LLMs)

Fine-Tuning Models and Large Language Models (LLMs)

Fine-Tuning Models and Large Language Models (LLMs)

Fine-Tuning Models and Large Language Models (LLMs)

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