# Tag: PyTorch

## Learning paths

- [Prepare models for neural graphics with Arm neural technology](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/preparing-models-for-nt/)
- [Run ExecuTorch Llama 3.2 1B Instruct on an Android phone with Vulkan](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/executorch-vulkan-learning-path/)
- [Deploy optimized ML models with ONNX Runtime on Arm platforms](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/onnx/)
- [Fine-tune neural graphics models using Model Gym](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/model-training-gym/)
- [Train and evaluate Neural Frame Rate Upscaling models using Model Gym](https://learn.arm.com/learning-paths/mobile-graphics-and-gaming/model-training-gym-nfru/)
- [Visualize Ethos-U NPU performance with ExecuTorch on Arm FVPs](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/visualizing-ethos-u-performance/)
- [Edge AI on Arm: PyTorch and ExecuTorch rock-paper-scissors](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/training-inference-pytorch/)
- [Introduction to TinyML on Arm using PyTorch and ExecuTorch](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/introduction-to-tinyml-on-arm/)
- [Deploy ExecuTorch firmware on NXP FRDM i.MX 93 for Ethos-U65 acceleration](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/observing-ethos-u-on-nxp/)
- [Use Keras Core with TensorFlow, PyTorch, and JAX backends](https://learn.arm.com/learning-paths/servers-and-cloud-computing/keras-core/)
- [Train and benchmark AI workloads with DeepSpeed on Google Cloud C4A Axion VMs](https://learn.arm.com/learning-paths/servers-and-cloud-computing/deepspeed-on-axion/)
- [Scale AI workloads with Ray on Google Cloud C4A Axion VM](https://learn.arm.com/learning-paths/servers-and-cloud-computing/ray-on-axion/)
- [Run vLLM inference with INT4 quantization on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/vllm-acceleration/)
- [Run a Natural Language Processing (NLP) model from Hugging Face on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/nlp-hugging-face/)
- [Run a Large Language Model (LLM) chatbot with PyTorch using KleidiAI on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/pytorch-llama/)
- [Measure and accelerate PyTorch Inference on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/torchbench/)
- [Deploy a LLM-based Vision Chatbot with PyTorch and Hugging Face Transformers on Google Axion processors](https://learn.arm.com/learning-paths/servers-and-cloud-computing/llama-vision/)
- [Accelerate Natural Language Processing (NLP) models from Hugging Face on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/benchmark-nlp/)
- [Fine-tune PyTorch models on DGX Spark](https://learn.arm.com/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/)
- [Inspect model artifacts and runtime profiles with Google Model Explorer and Arm extensions](https://learn.arm.com/learning-paths/cross-platform/explore-model-artifacts-with-model-explorer/)
- [Run image classification on an Alif Ensemble E8 DevKit using ExecuTorch and Ethos-U85](https://learn.arm.com/learning-paths/embedded-and-microcontrollers/alif-image-classification/)
- [Run vLLM inference with quantized models and benchmark on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/)
- [Optimize MLOps with Arm-hosted GitHub Runners](https://learn.arm.com/learning-paths/servers-and-cloud-computing/gh-runners/)