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Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:06

From DeepSpeed to FSDP and Back Again with Hugging Face Accelerate

Published:Jun 13, 2024 00:00
1 min read
Hugging Face

Analysis

This article from Hugging Face likely discusses the use of their Accelerate library in managing and optimizing large language model (LLM) training. It probably explores the trade-offs and considerations when choosing between different distributed training strategies, specifically DeepSpeed and Fully Sharded Data Parallel (FSDP). The 'and Back Again' suggests a comparison of the two approaches, potentially highlighting scenarios where one might be preferred over the other, or where a hybrid approach is beneficial. The focus is on practical implementation using Hugging Face's tools.
Reference

“The article likely includes specific examples or code snippets demonstrating how to switch between DeepSpeed and FSDP using Hugging Face Accelerate.”

PermalinkHugging Face
Research#llm📝 BlogAnalyzed: Dec 29, 2025 17:38

Fine-tuning Llama 2 70B using PyTorch FSDP

Published:Sep 13, 2023 00:00
1 min read
Hugging Face

Analysis

This article likely discusses the process of fine-tuning the Llama 2 70B large language model using PyTorch's Fully Sharded Data Parallel (FSDP) technique. Fine-tuning involves adapting a pre-trained model to a specific task or dataset, improving its performance on that task. FSDP is a distributed training strategy that allows for training large models on limited hardware by sharding the model's parameters across multiple devices. The article would probably cover the technical details of the fine-tuning process, including the dataset used, the training hyperparameters, and the performance metrics achieved. It would be of interest to researchers and practitioners working with large language models and distributed training.

Key Takeaways

Reference

“The article likely details the practical implementation of fine-tuning Llama 2 70B.”

PermalinkHugging Face
Research#llm📝 BlogAnalyzed: Dec 29, 2025 09:33

Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel

Published:May 2, 2022 00:00
1 min read
Hugging Face

Analysis

This article from Hugging Face likely discusses the use of PyTorch's Fully Sharded Data Parallel (FSDP) technique to improve the efficiency of training large language models (LLMs). FSDP is a method for distributing the model's parameters, gradients, and optimizer states across multiple devices (e.g., GPUs) to overcome memory limitations and accelerate training. The article probably explains how FSDP works, its benefits (e.g., reduced memory footprint, faster training times), and provides practical examples or tutorials on how to implement it. It would likely target researchers and engineers working on LLMs and deep learning.
Reference

“FSDP enables training of larger models on the same hardware or allows for faster training of existing models.”

PermalinkHugging Face