Large Scale Training of Hugging Face Transformers on TPUs . . . These new features make it easy to train a wide range of Hugging Face models at large scales In this guide, we demonstrate training GPT-2 models with up to 128B parameters on Google Cloud TPUs PyTorch XLA FSDP training on TPUs is highly efficient, achieving up to 45 1% model FLOPS utilization (MFU) for GPT-2: Figure 1: Model FLOPS utilization
Fine-Tuning Transformers with PyTorch and Hugging Face A project demonstrating fine-tuning techniques for large language models (LLMs) using PyTorch and Hugging Face’s SFTTrainer module Covers data preparation, training loop implementation, task-specific fine-tuning, and performance evaluation with PyTorch and Hugging Face
Fine-Tuning Large Language Models - The Basics with . . . - Corpnce In this article, we’ll delve into the intricacies of fine-tuning large language models using Hugging Face and PyTorch, exploring the process step by step and highlighting best practices and advanced fine-tuning techniques
Accelerating Hugging Face and TIMM models with PyTorch 2. 0 Our goal with PyTorch was to build a breadth-first compiler that would speed up the vast majority of actual models people run in open source The Hugging Face Hub ended up being an extremely valuable benchmarking tool for us, ensuring that any optimization we work on actually helps accelerate models people want to run
Baffling performance issue on most . . . - Hugging Face Forums I am hoping that someone can help me get to the bottom of a perplexing performance problem that I’ve discovered while benchmarking language model inference using transformers + pytorch 2 0 0 I was testing float16 inference on pytorch bin format models, as well as 4bit quantisation with GPTQ
Integrating PyTorch with Hugging Face Transformers for NLP . . . Integrating PyTorch with Hugging Face Transformers allows developers to leverage state-of-the-art models for various NLP tasks efficiently By following the steps outlined above, you can quickly set up a workflow to tokenize input data, feed it through a model, and use the derived encodings for applications such as text classification, name