Pytorch Fsdp (Nous Research)

---
name: pytorch-fsdp
description: Fully sharded data-parallel training for large models.
version: 1.0.0
author: Orchestra Research
license: MIT
dependencies: [torch>=2.0, transformers]
platforms: [linux, macos]
metadata:
  origin: Nous Research
  hermes:
    tags: [Distributed Training, PyTorch, FSDP, Data Parallel, Sharding, Mixed Precision, CPU Offloading, FSDP2, Large-Scale Training]
---

# Pytorch-Fsdp Skill

Assistance with pytorch-fsdp development, generated from official documentation.

## When to Use This Skill

This skill should be triggered when:
- Working with pytorch-fsdp
- Asking about pytorch-fsdp features or APIs
- Implementing pytorch-fsdp solutions
- Debugging pytorch-fsdp code
- Learning pytorch-fsdp best practices

## Quick Reference

The full common-patterns catalog (~157k chars of runnable FSDP snippets) lives in
`references/common-patterns.md` — load it with `read_file` when you need wrapping,
sharding-strategy, checkpoint, or mixed-precision examples. Start there rather than
reconstructing FSDP incantations from memory.

## Reference Files

This skill includes comprehensive documentation in `references/`:

- **other.md** - Other documentation

Use `view` to read specific reference files when detailed information is needed.

## Working with This Skill

### For Beginners
Start with the getting_started or tutorials reference files for foundational concepts.

### For Specific Features
Use the appropriate category reference file (api, guides, etc.) for detailed information.

### For Code Examples
The quick reference section above contains common patterns extracted from the official docs.

## Resources

### references/
Organized documentation extracted from official sources. These files contain:
- Detailed explanations
- Code examples with language annotations
- Links to original documentation
- Table of contents for quick navigation

### scripts/
Add helper scripts here for common automation tasks.

### assets/
Add templates, boilerplate, or example projects here.

## Notes

- This skill was automatically generated from official documentation
- Reference files preserve the structure and examples from source docs
- Code examples include language detection for better syntax highlighting
- Quick reference patterns are extracted from common usage examples in the docs

## Updating

To refresh this skill with updated documentation:
1. Re-run the scraper with the same configuration
2. The skill will be rebuilt with the latest information