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Artificial Intelligence (AI) Training
Natural Language Processing (NLP) Training
Large Language Models (LLMs) Training
Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Treningskurs

Fine-Tuning Open-Source LLMs (LLaMA, Mistral, Qwen, etc.) Treningskurs

Fine-tuning open-source LLMs is an emerging best practice for organizations that seek to customize AI capabilities in secure, cost-efficient, and private environments.

This instructor-led, live training (online or onsite) is aimed at intermediate-level ML practitioners and AI developers who wish to fine-tune and deploy open-weight models like LLaMA, Mistral, and Qwen for specific business or internal applications.

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

Understand the ecosystem and differences between open-source LLMs.
Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
Evaluate, save, and deploy fine-tuned models in secure environments.

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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Kursplan

Introduction to Open-Source LLMs

What are open-weight models and why they're important
Overview of LLaMA, Mistral, Qwen, and other community models
Use cases for private, on-premise, or secure deployments

Environment Setup and Tools

Installing and configuring Transformers, Datasets, and PEFT libraries
Choosing appropriate hardware for fine-tuning
Loading pre-trained models from Hugging Face or other repositories

Data Preparation and Preprocessing

Dataset formats (instruction tuning, chat data, text-only)
Tokenization and sequence management
Creating custom datasets and data loaders

Fine-Tuning Techniques

Standard full fine-tuning vs. parameter-efficient methods
Applying LoRA and QLoRA for efficient fine-tuning
Using Trainer API for quick experimentation

Model Evaluation and Optimization

Assessing fine-tuned models with generation and accuracy metrics
Managing overfitting, generalization, and validation sets
Performance tuning tips and logging

Deployment and Private Use

Saving and loading models for inference
Deploying fine-tuned models in secure enterprise environments
On-premise vs. cloud deployment strategies

Case Studies and Use Cases

Examples of enterprise use of LLaMA, Mistral, and Qwen
Handling multilingual and domain-specific fine-tuning
Discussion: Trade-offs between open and closed models

Summary and Next Steps

Krav

An understanding of large language models (LLMs) and their architecture
Experience with Python and PyTorch
Basic familiarity with the Hugging Face ecosystem

Audience

ML practitioners
AI developers

14 timer

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Open Training Courses require 5+ participants.

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