llm-course

𝕏 Follow me on X • 🤗 Hugging Face • 💻 Blog • 📙 LLM Engineer's Handbook

The LLM course is divided into three parts:

1. 🧩 LLM Fundamentals is optional and covers fundamental knowledge about mathematics, Python, and neural networks.
2. 🧑‍🔬 The LLM Scientist focuses on building the best possible LLMs using the latest techniques.
3. 👷 The LLM Engineer focuses on creating LLM-based applications and deploying them.

[!NOTE]

Based on this course, I co-wrote the LLM Engineer's Handbook, a hands-on book that covers an end-to-end LLM application from design to deployment. The LLM course will always stay free, but you can support my work by purchasing this book.

For a more comprehensive version of this course, check out the DeepWiki.

📝 Notebooks

A list of notebooks and articles I wrote about LLMs.

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Tools

| Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod | | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. | | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. | | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. | | | 🌳 Model Family Tree | Visualize the family tree of merged models. | | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. | | | ✂️ AutoAbliteration | Automatically abliteration models with custom datasets. | | | 🧼 AutoDedup | Automatically deduplicate datasets using the Rensa library. | |

Fine-tuning

| Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | Fine-tune Llama 3.1 with Unsloth | Ultra-efficient supervised fine-tuning in Google Colab. | Article | | | Fine-tune Llama 3 with ORPO | Cheaper and faster fine-tuning in a single stage with ORPO. | Article | | | Fine-tune Mistral-7b with DPO | Boost the performance of supervised fine-tuned models with DPO. | Article | | | Fine-tune Mistral-7b with QLoRA | Supervised fine-tune Mistral-7b in a free-tier Google Colab with TRL. | | | | Fine-tune CodeLlama using Axolotl | End-to-end guide to the state-of-the-art tool for fine-tuning. | Article | | | Fine-tune Llama 2 with QLoRA | Step-by-step guide to supervised fine-tune Llama 2 in Google Colab. | Article | |

Quantization

| Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | Introduction to Quantization | Large language model optimization using 8-bit quantization. | Article | | | 4-bit Quantization using GPTQ | Quantize your own open-source LLMs to run them on consumer hardware. | Article | | | Quantization with GGUF and llama.cpp | Quantize Llama 2 models with llama.cpp and upload GGUF versions to the HF Hub. | Article | | | ExLlamaV2: The Fastest Library to Run LLMs | Quantize and run EXL2 models and upload them to the HF Hub. | Article | |

Other

| Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | Merge LLMs with MergeKit | Create your own models easily, no GPU required! | Article | | | Create MoEs with MergeKit | Combine multiple experts into a single frankenMoE | Article | | | Uncensor any LLM with abliteration | Fine-tuning without retraining | Article | | | Improve ChatGPT with Knowledge Graphs | Augment ChatGPT's answers with knowledge graphs. | Article | | | Decoding Strategies in Large Language Models | A guide to text generation from beam search to nucleus sampling | Article | |

🧩 LLM Fundamentals

This section introduces essential knowledge about mathematics, Python, and neural networks. You might not want to start here but refer to it as needed.

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1. Mathematics for Machine Learning

Before mastering machine learning, it is important to understand the fundamental mathematical concepts that power these algorithms.

• Linear Algebra: This is crucial for understanding many algorithms, especially those used in deep learning. Key concepts include vectors, matrices, determinants, eigenvalues and eigenvectors, vector spaces, and linear transformations.
• Calculus: Many machine learning algorithms involve the optimization of continuous functions, which requires an understanding of derivatives, integrals, limits, and series. Multivariable calculus and the concept of gradients are also important.
• Probability and Statistics: These are crucial for understanding how models learn from data and make predictions. Key concepts include probability theory, random variables, probability distributions, expectations, variance, covariance, correlation, hypothesis testing, confidence intervals, maximum likelihood estimation, and Bayesian inference.

📚 Resources:

• 3Blue1Brown - The Essence of Linear Algebra: Series of videos that give a geometric intuition to these concepts.
• StatQuest with Josh Starmer - Statistics Fundamentals: Offers simple and clear explanations for many statistical concepts.
• Seeing Theory: A visual introduction to probability and statistics from Brown University.
• [Immersive Linear Algebra](https://immersivemath.com/il