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huggingface-accelerate

Simplify distributed training for PyTorch models with DeepSpeed, FSDP, Megatron, and DDP.

1|1|Updated May 9, 2026
Search Tags:#PyTorch#DeepSpeed#distributed training#mixed precision#FSDP#Megatron#DDP
One-click install
npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill huggingface-accelerate-ldzhhxx
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: huggingface-accelerate
Source: https://github.com/ldzhhxx/Hermes_offline_v2/tree/main/hermes-agent/optional-skills/mlops/accelerate
Command: npx skills add https://github.com/ldzhhxx/Hermes_offline_v2 --skill huggingface-accelerate-ldzhhxx

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires accelerate, torch, transformers, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a simple and unified API for distributed training across various platforms and frameworks, solving the complexity and challenges of scaling machine learning models.

Core Features & Use Cases

  • Unified API: Seamlessly integrates with PyTorch, allowing users to leverage distributed training features with minimal code changes.
  • Distributed Support: Supports DeepSpeed, FSDP, Megatron, and DDP frameworks, providing flexibility and compatibility.
  • Mixed Precision: Supports mixed precision training with automatic device placement and optimization.
  • Use Case: If you are developing a large language model and need to distribute training across multiple GPUs, this Skill can simplify the process and improve efficiency.

Quick Start

Install the skill and integrate it into your PyTorch training script using the provided instructions.

Frequently Asked Questions about huggingface-accelerate

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I simplify distributed training across multiple GPUs in PyTorch?▼

Simplify distributed training using a unified API that integrates with PyTorch to support DeepSpeed, FSDP, Megatron, and DDP, enabling efficient scaling across multiple GPUs with minimal code changes.

Does mixed precision training work with DeepSpeed and FSDP frameworks?▼

Mixed precision training works seamlessly with DeepSpeed and FSDP frameworks through automatic device placement and optimization, ensuring efficient memory usage and faster computation when scaling your models.

What do I need to set up before using this distributed training API?▼

You need PyTorch and related libraries like accelerate and transformers installed to function properly. Setting up your Python environment with these dependencies ensures the unified API can manage distributed training and device placement.

What is the best way to scale large language model training without writing complex distributed code?▼

The best way to scale large language model training without complex code is using a unified API that integrates with PyTorch to handle distributed training across DeepSpeed, FSDP, Megatron, and DDP frameworks automatically.

When should I not use automatic device placement for mixed precision training?▼

You should avoid automatic device placement for mixed precision training when your custom PyTorch training script requires manual hardware control or uses unsupported distributed configurations outside of DeepSpeed, FSDP, Megatron, and DDP.

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