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# Megatron Discussions

This directory contains in-depth guides, tutorials, and discussions about optimizing and using Megatron for various use cases.

## Available Guides

### Training Guides

## Previous News

- **[2025/05]** Megatron Core v0.11.0 brings new capabilities for multi-data center LLM training ([blog](https://developer.nvidia.com/blog/turbocharge-llm-training-across-long-haul-data-center-networks-with-nvidia-nemo-framework/)).
- **[2024/07]** Megatron Core v0.7 improves scalability and training resiliency and adds support for multimodal training ([blog](https://developer.nvidia.com/blog/train-generative-ai-models-more-efficiently-with-new-nvidia-Megatron-Core-functionalities/)).
- **[2024/06]** Megatron Core added support for Mamba-based models. Review the paper [An Empirical Study of Mamba-based Language Models](https://arxiv.org/pdf/2406.07887) and [code example](https://github.com/NVIDIA/Megatron-LM/tree/ssm/examples/mamba).
- **[2024/01 Announcement]** NVIDIA has released the core capabilities in **Megatron-LM** into [**Megatron Core**](https://github.com/NVIDIA/Megatron-LM/tree/main/megatron/core) in this repository. Megatron Core expands upon Megatron-LM's GPU-optimized techniques with more cutting-edge innovations on system-level optimizations, featuring composable and modular APIs.

## Contributing

To contribute a guide or tutorial, follow this structure:

1. Create a new directory: `docs/discussions/your-guide-name/`
2. Add your main guide: `docs/discussions/your-guide-name/your-guide-name.md`
3. Create an images directory: `docs/discussions/your-guide-name/images/`
4. Update this README.md with a link to your guide

Each guide should be self-contained with its own images and supporting files.