Faster inference for PyTorch models with OpenVINO Integration . . . You can now use OpenVINO™ Integration with Torch-ORT on Mac OS and Windows OS through Docker Pre-built Docker images are readily available on Docker Hub for your convenience With a simple docker pull, you will now be able to unleash the key to accelerating performance of PyTorch models
Model Optimization Pipeline for Inference Speedup with . . . We showed that applying the proposed pipeline it is possible to substantially improve the inference performance and reduce the size of the model If you have any ideas in ways we can improve the product, we welcome contributions to the open-sourced OpenVINO™ toolkit
Developer Guide: Model Optimization with the OpenVINO™ Toolkit Convert your model to OpenVINO (IR) format, then use POT to further help increase its inference speed by performing post-training quantization with a representative dataset It’s important to
A guide to optimizing Transformer-based models for faster . . . We take a fine-tuned version of the well-known BERT model architecture as a case study You will see how to perform model optimization by loading the transformer model, pruning the model with sparsity and block movement, converting the model to ONNX, and applying graph optimization and downcasting
Accelerated Inference with Optimum and Transformers Pipelines Convert a Hugging Face Transformers model to ONNX for inference; Use the ORTOptimizer to optimize the model; Use the ORTQuantizer to apply dynamic quantization; Run accelerated inference using Transformers pipelines; Evaluate the performance and speed; Let’s get started 🚀 This tutorial was created and run on an m5 xlarge AWS EC2 Instance
How to reduce the inference time of Helsinki-NLP opus-mt-es . . . Currently Helsinki-NLP opus-mt-es-en model takes around 1 5sec for inference from transformer How can that be reduced? Also when trying to convert it to onxx runtime getting this error: ValueError: Unrecognized configuration class <class 'transformers models marian configuration_marian MarianConfig'> for this kind of AutoModel: AutoModel
7 Ways To Speed Up Inference of Your Hosted LLMs Use tensor parallelism for faster inference on multiple GPUs to run large models If possible, use libraries for LLM inference and serving, such as Text Generation Inference, DeepSpeed, or