This Hugging Face Blog post appears to be a technical tutorial in a PyTorch profiling series. From the title, it focuses on analyzing performance from basic nn.Linear operations to a fused multilayer perceptron implementation. The likely audience is ML engineers and developers interested in understanding where neural network execution time goes and how kernel fusion can improve model throughput.
Based on the title, this Hugging Face Blog post is an introductory PyTorch profiling guide focused on torch.profiler. It likely targets developers and ML engineers who need to identify training or inference bottlenecks through observable performance data. Since the full article text was not provided, implementation details, examples, and specific optimization advice cannot be confirmed.
In the inference process of large language models (LLMs) and vision-language models (VLMs), autoregressive decoding is a major performance bottleneck. Each…
Hugging Face recently launched an open-source project called nanoVLM, positioned as "the simplest repository for training Vision Language Models (VLMs) in pure…
One of the most common pain points developers face in deep learning and large language model (LLM) training is the "Out of Memory (OOM)" error. To help…
This tutorial comes from Unit 4 of Hugging Face's Deep Reinforcement Learning Course, covering the topic of "Implementing Policy Gradients with PyTorch." In…
This classic blog post from Hugging Face, "The Annotated Diffusion Model," is an essential guide for learning about generative AI image synthesis. Modeled…
Hugging Face has officially announced a deep integration with the well-known high-level deep learning library fastai, formally bringing fastai into the Hugging…