philschmid.de - RSS feed

Learn how to pre-traing BERT from scratch using Hugging Face Transformers and Habana Gaudi.

Learn how to optimize BERT for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed.

Learn how to optimize Sentence Transformers using Hugging Face Optimum.

Learn how to migrate your training jobs to a Habana Gaudi-based DL1 instance on AWS using EC2 Remote Runner.

Learn how to optimize Vision Transformer (ViT) using Hugging Face Optimum.

Learn how to optimize Hugging Face Transformers models for NVIDIA GPUs using Optimum.

Learn how to learn how to fine-tune XLM-RoBERTa for multi-lingual multi-class text-classification using a Habana Gaudi-based DL1 instance.

Learn how to optimize Hugging Face Transformers models using Optimum.

Introduction guide about ONNX and Transformers.

Learn how to setup a Deep Learning Environment for Hugging Face Transformers with Habana Gaudi on AWS using the DL1 instance type.

Learn how to do post-training static quantization on Hugging Face Transformers model with `optimum` to achieve up to 3x latency improvements.

Learn how to do advanced PII detection and anonymization with Hugging Face Transformers and Amazon SageMaker.

Learn about the different existing Amazon SageMaker Inference options and and how to use them.

Learn how to do image segmentation with Hugging Face Transformers, SegFormer and Amazon SageMaker.

Learn how to do automatic speech recognition/speech-to-text with Hugging Face Transformers, Wav2vec2 and Amazon SageMaker.

Learn how to deploy a Transformer model like BERT to Amazon SageMaker Serverless using the Python SageMaker SDK.

Learn how to accelerate Sentence Transformers inference inference using Hugging Face Transformers and AWS Inferentia.

Learn how to leverage AWS Spot Instances when training Hugging Face Transformers with Amazon SageMaker to save up to 90% training cost.

Learn how to accelerate BERT and Transformers inference using Hugging Face Transformers and AWS Inferentia.

Learn how to use a custom Inference script for creating document embeddings with Hugging Face's Transformers, Amazon SageMaker, and Sentence Transformers.

Learn how to apply autoscaling to Hugging Face Transformers and Amazon SageMaker using Terraform.

Learn how to deploy multiple Hugging Face Transformers for inference with Amazon SageMaker and Multi-Container Endpoints.

Learn how to deploy an Asynchronous Inference model with Hugging Face Transformers and Amazon SageMaker, with autoscaling to zero.

Learn how to deploy BERT/DistilBERT with Hugging Face Transformers using Amazon SageMaker and Terraform module.

Learn how to run apply task-specific knowledge distillation for BERT and text-classification using Hugging Face Transformers and Amazon SageMaker including Hyperparameter search.

Learn how to run large-scale distributed training using multilingual BERT on over 1 million data points with Hugging Face Transformers and Amazon SageMaker

Learn how to fine-tune a a Hugging Face Transformer for Financial Text Summarization using vanilla `Keras`, `Tensorflow` , `Transformers`, `Datasets` and Amazon SageMaker.

Learn how to deploy EleutherAIs GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker.

Learn how to fine-tune a Vision Transformer for Image Classification Example using vanilla `Keras`, `Transformers`, `Datasets`.

In October and November, we held a workshop series on “Enterprise-Scale NLP with Hugging Face and Amazon SageMaker”.

Learn how to fine-tune a non-English BERT using Hugging Face Transformers and Keras/TF, Transformers, datasets.

Learn how to deploy Hugging Face Transformers serverless using the new Amazon SageMaker Serverless Inference.

Learn how to Compile and fine-tune a Multi-Class Classification Transformers with `Trainer` and `emotion` dataset using Amazon SageMaker Training Compiler.

Learn how to automatically save your model weights, logs, and artifacts to the Hugging Face Hub using Amazon SageMaker and how to deploy the model afterwards for inference.

If you are like me you are not from the USA and cannot easily travel to Las Vegas.

Learn how to build an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker.

Learn how to add auto-scaling to your Hugging Face Transformers SageMaker Endpoints.

🌸 BigScience released their first modeling paper introducing T0 which outperforms GPT-3 on many zero-shot tasks while being 16x smaller!

Deploy Hugging Face Transformers to Amazon SageMaker and create an API for the Endpoint using AWS Lambda, API Gateway and AWS CDK.

The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning.

Learn how to train distributed models for summarization using Hugging Face Transformers and Amazon SageMaker and upload them afterwards to huggingface.co.

Learn how to build a Multilingual Serverless BERT Question Answering API with a model size of more than 2GB and then testing it in German and France.

Learn how to use the newest cutting edge computing power of AWS with the benefits of serverless architectures to leverage Google's "State-of-the-Art" NLP Model.

Learn how to build and deploy an AWS Lambda function with a custom python docker container as runtime with the use of Amazon ECR.

Build a serverless Question-Answering API using the Serverless Framework, AWS Lambda, AWS EFS, efsync, Terraform, the transformers Library from HuggingFace, and a `mobileBert` model from Google fine-tuned on SQuADv2.

efsync is a CLI/SDK tool, which syncs files from S3 or local filesystem automatically to AWS EFS and enables you to install dependencies with the AWS Lambda runtime directly into your EFS filesystem.

This is the Story of how I became a certified solution architect within 28 hours of preparation.

Create a custom github action in 4 steps.

Fine-tune non-English, German GPT-2 model with Huggingface on German recipes.

Leverage your Serverless architectures with mounting your AWS EFS volume into your AWS Lambda with the Serverless Framework.