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Create a Large Language Model from Scratch with Python – Tutorial


Curriculum for the course Create a Large Language Model from Scratch with Python – Tutorial

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python. ✏️ Course developed by @elliotarledge 💻 Code and course resources: https://github.com/Infatoshi/fcc-intro-to-llms ⭐️ Contents ⭐️ (0:00:00) Intro (0:03:25) Install Libraries (0:06:24) Pylzma build tools (0:08:58) Jupyter Notebook (0:12:11) Download wizard of oz (0:14:51) Experimenting with text file (0:17:58) Character-level tokenizer (0:19:44) Types of tokenizers (0:20:58) Tensors instead of Arrays (0:22:37) Linear Algebra heads up (0:23:29) Train and validation splits (0:25:30) Premise of Bigram Model (0:26:41) Inputs and Targets (0:29:29) Inputs and Targets Implementation (0:30:10) Batch size hyperparameter (0:32:13) Switching from CPU to CUDA (0:33:28) PyTorch Overview (0:42:49) CPU vs GPU performance in PyTorch (0:47:49) More PyTorch Functions (1:06:03) Embedding Vectors (1:11:33) Embedding Implementation (1:13:06) Dot Product and Matrix Multiplication (1:25:42) Matmul Implementation (1:26:56) Int vs Float (1:29:52) Recap and get_batch (1:35:07) nnModule subclass (1:37:05) Gradient Descent (1:50:53) Logits and Reshaping (1:59:28) Generate function and giving the model some context (2:03:58) Logits Dimensionality (2:05:17) Training loop + Optimizer + Zerograd explanation (2:13:56) Optimizers Overview (2:17:04) Applications of Optimizers (2:18:11) Loss reporting + Train VS Eval mode (2:32:54) Normalization Overview (2:35:45) ReLU, Sigmoid, Tanh Activations (2:45:15) Transformer and Self-Attention (2:46:55) Transformer Architecture (3:17:54) Building a GPT, not Transformer model (3:19:46) Self-Attention Deep Dive (3:25:05) GPT architecture (3:27:07) Switching to Macbook (3:31:42) Implementing Positional Encoding (3:36:57) GPTLanguageModel initalization (3:40:52) GPTLanguageModel forward pass (3:46:56) Standard Deviation for model parameters (4:00:50) Transformer Blocks (4:04:54) FeedForward network (4:07:53) Multi-head Attention (4:12:49) Dot product attention (4:19:43) Why we scale by 1/sqrt(dk) (4:26:45) Sequential VS ModuleList Processing (4:30:47) Overview Hyperparameters (4:32:14) Fixing errors, refining (4:34:01) Begin training (4:35:46) OpenWebText download and Survey of LLMs paper (4:37:56) How the dataloader/batch getter will have to change (4:41:20) Extract corpus with winrar (4:43:44) Python data extractor (4:49:23) Adjusting for train and val splits (4:57:55) Adding dataloader (4:59:04) Training on OpenWebText (5:02:22) Training works well, model loading/saving (5:04:18) Pickling (5:05:32) Fixing errors + GPU Memory in task manager (5:14:05) Command line argument parsing (5:18:11) Porting code to script (5:22:04) Prompt: Completion feature + more errors (5:24:23) nnModule inheritance + generation cropping (5:27:54) Pretraining vs Finetuning (5:33:07) R&D pointers (5:44:38) Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

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