Learning plan cover

Fine tuning LLMs

LEARNING PLAN

Fine tuning LLMs

As organizations move beyond generic AI, the ability to customize models for specific industries is becoming a critical engineering skill. This plan is ideal for data scientists and software engineers looking to transition from using pre-trained APIs to building and deploying specialized, high-performance LLMs.

ByCommunity User
2 h 30 m
12 coursesUpdated 7 days ago
Make your own neural networkAttention Is All You Need: The AI RevolutionLLM Fundamentals: Attention Is All You Need12 sources

How This Learning Plan Was Made

This plan was crafted by BeFreed's proprietary AI to help you learn Fine tuning LLMs with ease. It is curated from in-depth research on the topic and structured around the most effective learning journeys proven by BeFreed users.

Each episode delivers bite-sized, high-impact lessons distilled from world-class sources — bestselling books, research papers, and expert insights. Together, they form a sophisticated yet accessible path to mastering Fine tuning LLMs.

What You'll Learn

  • Master the fundamentals of neural networks and the transformer architecture.
  • Understand internal LLM mechanics, including attention mechanisms and training cycles.
  • Apply parameter-efficient fine-tuning techniques like LoRA to specific tasks.
  • Build robust evaluation pipelines to measure model performance and accuracy.
  • Implement optimization strategies for deploying fine-tuned models in production.
  • Adapt large-scale models to specialized domains using professional best practices.

Build a solid understanding of neural network fundamentals and the revolutionary transformer architecture that powers all modern LLMs.

Make your own neural network cover
Make your own neural network

Step-by-step guide to building neural networks for beginners.

8 m
podcast cover
Attention Is All You Need - A Deep Dive into the Revolutionary ...Attention Is All You Need: Complete Guide to the Transformer Paper ...Attention Is All You Need: The Original Transformer ArchitectureHow RNNs Were Replaced by Transformers - And Why
6 sources
Attention Is All You Need: The AI Revolution

Discover how a 2017 paper with 8 authors and 173K citations transformed AI forever. From Google Translate to ChatGPT, explore the Transformer architecture that powers every modern AI system you use daily.

10 m
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source 1source 2source 3source 4
6 sources
LLM Fundamentals: Attention Is All You Need

Deep dive into how ChatGPT and large language models actually work, from the revolutionary attention mechanism to probabilistic text generation. Perfect for understanding the core concepts behind modern AI.

9 m

Dive deep into how large language models work internally, from attention mechanisms to the complete training lifecycle.

What Is ChatGPT Doing ... and Why Does It Work? cover
What Is ChatGPT Doing ... and Why Does It Work?

In-depth analysis of ChatGPT's AI mechanisms and effectiveness

9 m
podcast cover
Artificial Intelligence and Generative AI for BeginnersWhat Is ChatGPT Doing ... and Why Does It Work?ChatGPT For DummiesPython Cookbook
17 sources
Under the Hood: The Life Cycle of LLMs

Explore the evolution of Large Language Models from raw pre-training to human-aligned tools. This deep dive covers transformer architecture, fine-tuning, and the ethical governance required for production-ready AI.

14 m
podcast cover
LLM Interviews | EngineersOfAI — Technical Education for AI EngineersLLM Interview Questions | EngineersOfAI — The Engineering Curriculum for the AI EraTransformer Internals for LLMs | EngineersOfAI — The Engineering Curriculum for the AI EraThe LLM Engineering Field Guide: 45 Concepts Every Practitioner Needs - Edge of Context: Practical AI Engineering
7 sources
The AI Architect’s Playbook

Generic AI experience is no longer enough to stand out. Go inside the Transformer architecture to master the technical logic of the modern LLM.

18 m

Master parameter-efficient fine-tuning methods like LoRA and learn practical approaches for adapting models to specific domains and tasks.

Hands-on Machine Learning With Scikit-learn And Tensorflow cover
Hands-on Machine Learning With Scikit-learn And Tensorflow

Step-by-step guide to machine learning with Python

8 m
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A Survey on Post-training of Large Language ModelsFine-tuning large language models for domain adaptation - NatureFine-tuning and Utilization Methods of Domain-specific LLMsPEFT, LoRA & QLoRA: Smarter, Faster Fine-Tuning for Domain LLMs
6 sources
AI Post-Training for Real-World Applications

Discover how businesses are transforming AI models for specific needs through parameter-efficient fine-tuning, domain adaptation, and practical implementation strategies that deliver measurable results.

10 m
podcast cover
Keras Reinforcement Learning ProjectsPython CookbookWhat Is ChatGPT Doing ... and Why Does It Work?ChatGPT for Dummies
27 sources
Build an LLM from scratch on your laptop

Building AI feels impossible without a supercomputer, but you only need eight building blocks. Learn how to train your own model in under ten minutes.

31 m

Learn cutting-edge optimization techniques, evaluation pipelines, and best practices for deploying fine-tuned models in production environments.

Refactoring cover
Refactoring

Transform code safely with small, behavior-preserving steps.

9 m
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[url_c4a047d5:c0000] mljourney.com/how-to-evaluate-llms-with-lm-evaluation-harness/ p1-1[url_c4a047d5:c0001] mljourney.com/how-to-evaluate-llms-with-lm-evaluation-harness/ p1-1[url_c4a047d5:c0002] mljourney.com/how-to-evaluate-llms-with-lm-evaluation-harness/ p1-1[url_ea771b33:c0000] github.com/eleutherAI/lm-evaluation-harness p1-1
20 sources
High-Throughput Evaluation with vLLM

Standard model evaluation is often slowed by memory bottlenecks. Learn to use continuous batching and parallelism to maximize GPU throughput.

12 m
podcast cover
[url_c7db54c6:c0003] github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/api/task.py p1-1[url_c7db54c6:c0005] github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/api/task.py p1-1[url_c7db54c6:c0006] github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/api/task.py p1-1[url_c7db54c6:c0008] github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/api/task.py p1-1
20 sources
Filter Ensembles and Self-Consistency

Raw model outputs often require complex extraction and voting to be useful. Learn to build multi-step filter pipelines for more accurate evaluations.

12 m

Discover more

LLM personalization and memory

LLM personalization and memory

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LLM personalization and memory

This learning plan is essential for AI engineers, ML practitioners, and developers who want to move beyond basic LLM usage to create truly intelligent, personalized applications. As businesses demand AI systems that understand context, remember user preferences, and adapt over time, the ability to implement memory systems and personalization techniques has become a critical competitive advantage in the AI space.

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Python programming for LLMs and evals

Python programming for LLMs and evals

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Python programming for LLMs and evals

As AI integration becomes standard, the ability to both build and critically evaluate models is a vital technical differentiator. This path is ideal for developers and data scientists looking to transition from general programming to specialized LLM engineering and rigorous model benchmarking.

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Master AI: LLMs, RAG & Prompt Engineering

Master AI: LLMs, RAG & Prompt Engineering

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Master AI: LLMs, RAG & Prompt Engineering

This learning plan is essential for developers and tech enthusiasts looking to bridge the gap between basic AI usage and professional system design. It provides the technical depth needed to build robust, data-driven applications using the latest LLM and RAG frameworks.

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AI Myths: LLMs vs. True Sentience

AI Myths: LLMs vs. True Sentience

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This learning plan is essential for anyone looking to look past the headlines and understand the actual capabilities of modern AI. It is particularly valuable for tech enthusiasts, students, and professionals who want to ground their understanding of machine intelligence in both science and philosophy.

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large language models

large language models

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large language models

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Learn ML Basics 1767952269

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Learn ML Basics 1767952269

Machine learning is transforming every industry from healthcare to finance, making it one of the most valuable skills in today's tech landscape. This learning plan is ideal for aspiring data scientists, software engineers looking to transition into AI, and technical professionals who want to build intelligent systems that solve real-world problems.

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Master AI efficiency and stay current.

Master AI efficiency and stay current.

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Master AI efficiency and stay current.

As AI reshapes the professional landscape, mastering these tools is no longer optional but a competitive necessity. This plan is ideal for professionals and creators looking to transition from basic AI users to advanced engineers who stay ahead of the curve.

3 h 20 m4 Sections
Master AI for work (17 chars)

Master AI for work (17 chars)

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Master AI for work (17 chars)

As AI transforms the modern workplace, professionals who can effectively leverage these technologies gain significant competitive advantages. This learning plan equips both technical and non-technical professionals with practical AI knowledge and skills to enhance productivity, solve complex problems, and advance their careers.

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