🦙 LLaMA2-from-Scratch (JAX)

This project implements Meta’s LLaMA2 language model architecture from scratch using JAX, with a focus on clarity and modularity. It's intended as a learning resource and a base for further experimentation.

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📁 Project Structure

llama2-from-scratch-jax/
├── Model/                         # Saved trained model checkpoints
├── Tokenized/                    # Tokenized dataset used for training
├── Tokenizer/                    # Trained tokenizer model and vocab files
├── .gitattributes
├── .gitignore
├── Tokenizer_for_llama2.ipynb    # Notebook for training/preparing tokenizer
├── configuration.py              # Model/training hyperparameters and config
├── export.py                     # Utility for exporting model checkpoints
├── model.py                      # Core model implementation (LLaMA2 in JAX)
├── train.py                      # Training loop

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/your-username/llama2-from-scratch-jax.git
cd llama2-from-scratch-jax

2. Install dependencies

Make sure you have Python 3.10+ and install necessary packages:

pip install -r requirements.txt

Required packages typically include:

• jax, flax, optax
• numpy
• tokenizers or Hugging Face tokenizers
• tqdm, matplotlib, etc. (optional)

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🧠 Overview

• Model: Implements LLaMA2 transformer architecture using JAX and Flax.
• Tokenizer: Prepares and stores the vocabulary and tokenizer model.
• Training: Modular and easy-to-read training loop with checkpoint support.
• Export: Utilities to save/export trained model weights.

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📊 Workflow

1. Tokenizer Preparation
   Use Tokenizer_for_llama2.ipynb to train a tokenizer or load a pretrained one. Output is saved to the Tokenizer/ directory.

2. Tokenize Dataset
   Store your processed/tokenized data in the Tokenized/ directory.

3. Training the Model
   Edit configs in configuration.py as needed, then run:

   python train.py

   Checkpoints will be saved to the Model/ directory.

4. Exporting
   To convert model weights to a usable format, use:

   python export.py

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❗ Notes

• This is an experimental implementation; not optimized for large-scale training.
• No license

Let me know if you want to add example outputs or Colab instructions!