大模型技术文章精选合集

<div align="center"> <h1>🗣️ Large Language Model Course</h1> <p align="center"> 🐦 <a href="https://twitter.com/maximelabonne">Follow me on X</a> • 🤗 <a href="https://huggingface.co/mlabonne">Hugging Face</a> • 💻 <a href="https://mlabonne.github.io/blog">Blog</a> • 📙 <a href="https://github.com/PacktPublishing/Hands-On-Graph-Neural-Networks-Using-Python">Hands-on GNN</a> • 🗣️ <a href="https://chat.openai.com/g/g-yviLuLqvI-llm-course">Interactive GPT</a> </p> </div> <br/> The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential 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. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) | Automatically evaluate your LLMs using RunPod | <a href="https://colab.research.google.com/drive/1Igs3WZuXAIv9X0vwqiE90QlEPys8e8Oa?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | 🥱 LazyMergekit | Easily merge models using mergekit in one click. | <a href="https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | ⚡ AutoGGUF | Quantize LLMs in GGUF format in one click. | <a href="https://colab.research.google.com/drive/1P646NEg33BZy4BfLDNpTz0V0lwIU3CHu?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | 🌳 Model Family Tree | Visualize the family tree of merged models. | <a href="https://colab.research.google.com/drive/1s2eQlolcI1VGgDhqWIANfkfKvcKrMyNr?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | ### Fine-tuning | Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | Fine-tune Llama 2 in Google Colab | Step-by-step guide to fine-tune your first Llama 2 model. | [Article](https://mlabonne.github.io/blog/posts/Fine_Tune_Your_Own_Llama_2_Model_in_a_Colab_Notebook.html) | <a href="https://colab.research.google.com/drive/1PEQyJO1-f6j0S_XJ8DV50NkpzasXkrzd?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | Fine-tune LLMs with Axolotl | End-to-end guide to the state-of-the-art tool for fine-tuning. | [Article](https://mlabonne.github.io/blog/posts/A_Beginners_Guide_to_LLM_Finetuning.html) | <a href="https://colab.research.google.com/drive/1Xu0BrCB7IShwSWKVcfAfhehwjDrDMH5m?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | Fine-tune Mistral-7b with DPO | Boost the performance of supervised fine-tuned models with DPO. | [Article](https://medium.com/towards-data-science/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) | <a href="https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | ### Quantization | Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | 1. Introduction to Quantization | Large language model optimization using 8-bit quantization. | [Article](https://mlabonne.github.io/blog/posts/Introduction_to_Weight_Quantization.html) | <a href="https://colab.research.google.com/drive/1DPr4mUQ92Cc-xf4GgAaB6dFcFnWIvqYi?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | 2. 4-bit Quantization using GPTQ | Quantize your own open-source LLMs to run them on consumer hardware. | [Article](https://mlabonne.github.io/blog/4bit_quantization/) | <a href="https://colab.research.google.com/drive/1lSvVDaRgqQp_mWK_jC9gydz6_-y6Aq4A?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | 3. Quantization with GGUF and llama.cpp | Quantize Llama 2 models with llama.cpp and upload GGUF versions to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html) | <a href="https://colab.research.google.com/drive/1pL8k7m04mgE5jo2NrjGi8atB0j_37aDD?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | 4. ExLlamaV2: The Fastest Library to Run LLMs | Quantize and run EXL2 models and upload them to the HF Hub. | [Article](https://mlabonne.github.io/blog/posts/ExLlamaV2_The_Fastest_Library_to_Run%C2%A0LLMs.html) | <a href="https://colab.research.google.com/drive/1yrq4XBlxiA0fALtMoT2dwiACVc77PHou?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | ### Other | Notebook | Description | Article | Notebook | |---------------------------------------|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------| | Decoding Strategies in Large Language Models | A guide to text generation from beam search to nucleus sampling | [Article](https://mlabonne.github.io/blog/posts/2022-06-07-Decoding_strategies.html) | <a href="https://colab.research.google.com/drive/19CJlOS5lI29g-B3dziNn93Enez1yiHk2?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | Visualizing GPT-2's Loss Landscape | 3D plot of the loss landscape based on weight perturbations. | [Tweet](https://twitter.com/maximelabonne/status/1667618081844219904) | <a href="https://colab.research.google.com/drive/1Fu1jikJzFxnSPzR_V2JJyDVWWJNXssaL?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | Improve ChatGPT with Knowledge Graphs | Augment ChatGPT's answers with knowledge graphs. | [Article](https://mlabonne.github.io/blog/posts/Article_Improve_ChatGPT_with_Knowledge_Graphs.html) | <a href="https://colab.research.google.com/drive/1mwhOSw9Y9bgEaIFKT4CLi0n18pXRM4cj?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | | Merge LLMs with mergekit | Create your own models easily, no GPU required! | [Article](https://towardsdatascience.com/merge-large-language-models-with-mergekit-2118fb392b54) | <a href="https://colab.research.google.com/drive/1_JS7JKJAQozD48-LhYdegcuuZ2ddgXfr?usp=sharing"><img src="img/colab.svg" alt="Open In Colab"></a> | ## 🧩 LLM Fundamentals  ### 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, varia