🤗 Optimum notebooks

You can find here a list of the notebooks associated with each accelerator in 🤗 Optimum. |

Optimum Habana examples

Notebook	Description	Colab	Studio Lab
How to use DeepSpeed to train models with billions of parameters on Habana Gaudi	Show how to use DeepSpeed to pre-train/fine-tune the 1.6B-parameter GPT2-XL for causal language modeling on Habana Gaudi.

Optimum Intel examples

Notebook	Description	Colab	Studio Lab
How to quantize a model with Intel Neural Compressor for text classification	Show how to apply static, dynamic and aware training quantization on a model using Intel Neural Compressor for any GLUE task.
How to quantize a model with OpenVINO NNCF for question answering	Show how to apply post-training quantization on a question answering model using NNCF and to accelerate inference with OpenVINO

Optimum ONNX Runtime examples

Notebook	Description	Colab	Studio Lab
How to quantize a model with ONNX Runtime for text classification	Show how to apply static and dynamic quantization on a model using ONNX Runtime for any GLUE task.
How to fine-tune a model for text classification with ONNX Runtime	Show how to DistilBERT model on GLUE tasks using ONNX Runtime.
How to fine-tune a model for summarization with ONNX Runtime	Show how to fine-tune a T5 model on the BBC news corpus.

Optimum Graphcore examples

Notebook	Description	Colab
Introduction to Optimum Graphcore	Introduce Optimum-Graphcore with a BERT fine-tuning example.
Train an external model	Show how to train an external model that is not supported by Optimum or Transformers.
Train your language model	Show how to train a model for causal or masked language modelling from scratch.
How to fine-tune a model on text classification	Show how to preprocess the data and fine-tune a pretrained model on any GLUE task.
How to fine-tune a model on language modeling	Show how to preprocess the data and fine-tune a pretrained model on a causal or masked LM task.
How to fine-tune a model on token classification	Show how to preprocess the data and fine-tune a pretrained model on a token classification task (NER, PoS).
How to fine-tune a model on question answering	Show how to preprocess the data and fine-tune a pretrained model on SQUAD.
How to fine-tune a model on multiple choice	Show how to preprocess the data and fine-tune a pretrained model on SWAG.
How to fine-tune a model on translation	Show how to preprocess the data and fine-tune a pretrained model on WMT.
How to fine-tune a model on summarization	Show how to preprocess the data and fine-tune a pretrained model on XSUM.
How to fine-tune a model on audio classification	Show how to preprocess the data and fine-tune a pretrained Speech model on Keyword Spotting
How to fine-tune a model on image classfication	Show how to preprocess the data and fine-tune a pretrained model on image classification.
wav2vec 2.0 Inference on IPU	How to run inference on the wav2vec 2.0 model with PyTorch on the Graphcore IPU-POD16 system.