Llama 3.2 From Scratch
| Property | Value |
|---|---|
| Author | rasbt |
| Model Variants | 1B and 3B parameters |
| Repository | HuggingFace |
| Dependencies | PyTorch, tiktoken, blobfile |
What is llama-3.2-from-scratch?
Llama 3.2 From Scratch is an educational implementation of Meta's Llama language model, built with PyTorch and designed for maximum readability and learning purposes. It offers both base and instruction-tuned variants in 1B and 3B parameter sizes, making it accessible for research and educational purposes.
Implementation Details
The implementation features a minimal-dependency architecture that supports context lengths up to 131,072 tokens, though the recommended context size is 8,192 tokens requiring approximately 3GB of VRAM. The codebase includes dedicated model.py and tokenizer.py files, with optional installation via the llms-from-scratch PyPI package.
- Supports both base and instruction-tuned models
- Includes custom tokenizer implementation
- Offers memory-optimized architecture
- Features compilation support for 4x speed-up
Core Capabilities
- Text generation with configurable parameters
- Support for long context windows
- Custom chat formatting for instruction models
- Efficient memory management
- Compatible with CPU, CUDA, and MPS devices
Frequently Asked Questions
Q: What makes this model unique?
This implementation stands out for its educational value and transparency, offering a from-scratch implementation that helps users understand the internal workings of Llama models while maintaining practical usability.
Q: What are the recommended use cases?
The model is ideal for educational purposes, research experiments, and learning about large language model architectures. It's particularly suited for environments where understanding the implementation details is as important as the model's performance.
