LearnFlat · AI, Tech & Engineering · Generative AI & Deep Learning

Fine-Tuning Language Models

Go beyond prompting by learning to fine-tune pre-trained language models on custom datasets. Adapt models like GPT, Llama, and BERT for specific domains and tasks to improve performance and accuracy.

60 courses

Foundations of Large Language Models: From Transformers to Fine-Tuning

Learn how transformer architectures work and how to fine-tune, optimize, and deploy modern generative AI models using parameter-efficient methods.

★ 4.4 (8,765)

Fine-Tuning OpenAI Models: Customize LLMs with Your Own Data

Learn how to prepare custom datasets, estimate costs, and fine-tune OpenAI language models to deliver highly accurate, domain-specific responses for your industry.

★ 4.4 (1,619)

Fine-Tuning Transformers and LLMs: From BERT to LLaMA

Learn to adapt, optimize, and deploy powerful language models like BERT, Phi-2, and LLaMA using Hugging Face through step-by-step written explanations and code.

Generative AI Fundamentals with Hugging Face

Learn to find, fine-tune, and deploy Large Language Models for your own text-based applications.

Build, Align, and Fine-Tune LLMs from Scratch with PyTorch

Master large language models by building them from scratch, applying QLoRA fine-tuning, and understanding attention mechanisms through intuitive conceptual analogies.

LLM Fine-Tuning Guide: Customize GPT-3.5 and AI Models with Your Own Data

Learn how to format custom datasets, train GPT-3.5 and foundation models, and deploy domain-specific AI models using OpenAI and modern developer platforms.

Fine-Tuning Large Language Models for Generative AI

Learn how to adapt pre-trained language models to specific business domains using parameter-efficient fine-tuning techniques like LoRA and instruction dataset preparation.

Generative AI Engineering: Fine-Tuning Transformers

Master the fundamentals of transformer models, Hugging Face, and PyTorch to customize large language models for specialized tasks.

Strategic Implementation and Fine-Tuning of Large Language Models

Learn to strategically select, customize, and evaluate large language models to build reliable AI-driven solutions for modern business challenges.

Custom LLM Development: Fine-tuning with LoRA and QLoRA

Master the fundamentals of parameter-efficient fine-tuning to build specialized AI models without needing massive computing resources.

Running Local Large Language Models: A Practical Guide

Set up, run, and query open-source AI models entirely on your own machine using web interfaces, APIs, and lightweight tools like llamafile.

LLMs in Business: Strategy, Applications, and Implementation

Learn how to strategically deploy large language models in your organization, from automating workflows to managing data privacy and implementation risks.

Foundations of Large Language Models: Fine-Tuning and RAG

Learn to build, fine-tune, and optimize large language models while implementing modern retrieval-augmented generation patterns for real-world applications.

Language Model Evaluation with Azure Databricks

Learn how to systematically measure, compare, and optimize large language model performance using Azure Databricks and modern evaluation workflows.

Transformer Architecture and Large Language Models in Azure Machine Learning

Gain a solid understanding of Transformer models and learn how to deploy and experiment with foundational large language models in Azure Machine Learning.

Fine-Tune & Optimize Generative AI Models

Learn to adapt and enhance Large Language Models for domain-specific applications, improving their performance and efficiency for real-world use.

Fine-Tuning Language Models with Foundry

Learn to customize large language models using supervised fine-tuning, direct preference optimization, and synthetic data generation on the Foundry platform.

DeepSeek AI Fundamentals: Understanding Large Language Models

Master the core concepts of DeepSeek AI and large language models to effectively prompt, evaluate, and integrate generative AI solutions into your workflow.

Evaluating LLM Outputs: A Practical Introduction

Learn to systematically assess the quality and relevance of Large Language Model responses to build more effective AI applications.

Optimize LLM Application Performance with Benchmarking

Develop the skills to identify performance bottlenecks and optimize Large Language Model applications for greater efficiency.

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