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    📙 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 |
|---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|-------------------------------------------------

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