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.”