Welcome to Megatron-LLM's documentation!
========================================
.. image:: imgs/llama-falcon.png
The `Megatron-LLM `_ library enables pre-training and fine-tuning of large language models (LLMs) at scale.
Our repository is a modification of the `original Megatron-LM codebase `_ by Nvidia.
Added key features include:
- `LLaMa `_, `LLaMa 2 `_, `Falcon `_, and `Code Llama `_ support.
- support training of large models (70B Llama 2, 65B Llama 1, 34B Code Llama, and 40B Falcon) on commodity hardware on multiple nodes
- 3-way parallelism: tensor parallel, pipeline parallel and data parallel training (inherited from Megatron)
- full pretraining, finetuning and instruct tuning support
- Support for special tokens & tokenizers
- grouped-query attention (GQA) and multi-query attention (MQA)
- Rotary Position Embeddings (RoPE), RMS layer norm, Lima dropout
- `ROPE scaling `_ for longer attention context support
- FlashAttention 2
- BF16 / FP16 training
- WandB integration
- Metrics support: Ease to add custom metrics to evaluate on the validation set while training
- Conversion to and from Hugging Face hub
Example models trained with `Megatron-LLM `_: See `README `_.
User guide
----------
For information on installation and usage, take a look at our user guide.
.. toctree::
:maxdepth: 2
guide/index
API
---
Detailed information about Megatron-LLM components:
.. toctree::
:maxdepth: 2
api/index
Citation
--------
If you use this software please cite it:
.. code-block:: bib
@software{epfmgtrn,
author = {Alejandro Hernández Cano and
Matteo Pagliardini and
Andreas Köpf and
Kyle Matoba and
Amirkeivan Mohtashami and
Olivia Simin Fan and
Axel Marmet and
Deniz Bayazit and
Igor Krawczuk and
Zeming Chen and
Francesco Salvi and
Antoine Bosselut and
Martin Jaggi},
title = {epfLLM Megatron-LM},
year = 2023,
url = {https://github.com/epfLLM/Megatron-LLM}
}