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https://github.com/likejazz/llama3.np
llama3.np is a pure NumPy implementation for Llama 3 model.
https://github.com/likejazz/llama3.np
Last synced: 7 days ago
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llama3.np is a pure NumPy implementation for Llama 3 model.
- Host: GitHub
- URL: https://github.com/likejazz/llama3.np
- Owner: likejazz
- License: mit
- Created: 2024-05-13T09:16:30.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2024-06-02T23:26:22.000Z (5 months ago)
- Last Synced: 2024-10-01T19:10:54.275Z (about 1 month ago)
- Language: Python
- Homepage:
- Size: 83.7 MB
- Stars: 959
- Watchers: 13
- Forks: 73
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# llama3.np
`llama3.np` is a pure NumPy implementation for Llama 3 model. For an accurate implementation, I ran the [stories15M model](https://github.com/karpathy/llama2.c?tab=readme-ov-file#models) trained by Andrej Karpathy.
- For a detailed explanation in English, see [Llama 3 implemented in pure NumPy](https://docs.likejazz.com/llama3.np/).
- If you're interested in CUDA implementation, see [Llama 3 implemented in pure C/CUDA](https://github.com/likejazz/llama3.cuda).
## Usage
```shell
$ python llama3.py "I have a dream"
"""
I have a dream. He dream of a big, beautiful garden full of flower and tree. He dream of playing with hi friend and eating yummy snack.
One day, he wa walking in the garden when he saw
Token count: 50, elapsed: 1.53s, 33 tokens/s
"""
```
## Citing llama3.np
If you use or discuss `llama3.np` in your academic research, please cite the project to help spread awareness:
```
@misc{llama3.np,
title = {llama3.np: pure NumPy implementation for Llama 3 model},
author = {Sang Park},
howpublished = {\url{https://github.com/likejazz/llama3.np}},
note = {llama3.np, MIT License}
year = {2024},
}
```
# References
Thank you to the creators of the following libraries and tools and their contributors:
- [llama2.c](https://github.com/karpathy/llama2.c) - @karpathy
- [llama.np](https://github.com/hscspring/llama.np) - @hscspring
- [modeling_llama.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/modeling_llama.py) - Hugging Face's Transformers
I got a lot of information from the articles below:
- [42dot LLM 1.3B](https://42dot.ai/blog/178) - 42dot
- [Exploring and building the LLaMA 3 Architecture : A Deep Dive into Components, Coding, and Inference Techniques](https://medium.com/@vi.ai_/exploring-and-building-the-llama-3-architecture-a-deep-dive-into-components-coding-and-43d4097cfbbb) - @vi.ai_
- [Rotary Embeddings: A Relative Revolution](https://blog.eleuther.ai/rotary-embeddings/) - EleutherAI
- [Mastering LLM Techniques: Inference Optimization](https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/) - NVIDIA
And title image was generated by DALL-E
# License
MIT