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perf_train_cpu_many.md |
# Efficient Training on Multiple CPUs
When training on a single CPU is too slow, we can use multiple CPUs. This guide focuses on PyTorch-based DDP enabling distributed CPU training efficiently.
## Intel® oneCCL Bindings for PyTorch
[Intel® oneCCL](https://github.com/oneapi-src/oneCCL) (collective communications li... |
bertology.md |
# BERTology
There is a growing field of study concerned with investigating the inner working of large-scale transformers like BERT
(that some call "BERTology"). Some good examples of this field are:
- BERT Rediscovers the Classical NLP Pipeline by Ian Tenney, Dipanjan Das, Ellie Pavlick:
https://arxiv.org/abs/1... |
training.md |
# Fine-tune a pretrained model
[[open-in-colab]]
There are significant benefits to using a pretrained model. It reduces computation costs, your carbon footprint, and allows you to use state-of-the-art models without having to train one from scratch. 🤗 Transformers provides access to thousands of pretrained models ... |
tf_xla.md |
# XLA Integration for TensorFlow Models
[[open-in-colab]]
Accelerated Linear Algebra, dubbed XLA, is a compiler for accelerating the runtime of TensorFlow Models. From the [official documentation](https://www.tensorflow.org/xla):
XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra tha... |
run_scripts.md |
# Train with a script
Along with the 🤗 Transformers [notebooks](./noteboks/README), there are also example scripts demonstrating how to train a model for a task with [PyTorch](https://github.com/huggingface/transformers/tree/main/examples/pytorch), [TensorFlow](https://github.com/huggingface/transformers/tree/main/... |
generation_strategies.md |
# Text generation strategies
Text generation is essential to many NLP tasks, such as open-ended text generation, summarization, translation, and
more. It also plays a role in a variety of mixed-modality applications that have text as an output like speech-to-text
and vision-to-text. Some of the models that can gen... |
multilingual.md |
# Multilingual models for inference
[[open-in-colab]]
There are several multilingual models in 🤗 Transformers, and their inference usage differs from monolingual models. Not *all* multilingual model usage is different though. Some models, like [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multi... |
community.md |
# Community
This page regroups resources around 🤗 Transformers developed by the community.
## Community resources:
| Resource | Description | Author |
|:----------|:-------------|------:|
| [Hugging Face Transformers Glossary Flashcards](https://www.darigovresearch.com/huggingface-transf... |
task_summary.md |
# What 🤗 Transformers can do
🤗 Transformers is a library of pretrained state-of-the-art models for natural language processing (NLP), computer vision, and audio and speech processing tasks. Not only does the library contain Transformer models, but it also has non-Transformer models like modern convolutional networ... |
chat_templating.md |
# Templates for Chat Models
## Introduction
An increasingly common use case for LLMs is **chat**. In a chat context, rather than continuing a single string
of text (as is the case with a standard language model), the model instead continues a conversation that consists
of one or more **messages**, each of which i... |
perf_torch_compile.md |
# Optimize inference using torch.compile()
This guide aims to provide a benchmark on the inference speed-ups introduced with [`torch.compile()`](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) for [computer vision models in 🤗 Transformers](https://huggingface.co/models?pipeline_tag=image-cla... |
hpo_train.md |
# Hyperparameter Search using Trainer API
🤗 Transformers provides a [`Trainer`] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [`Trainer`] provides API for hyperparameter search. This doc shows how to enable it in example.... |
glossary.md |
# Glossary
This glossary defines general machine learning and 🤗 Transformers terms to help you better understand the
documentation.
## A
### attention mask
The attention mask is an optional argument used when batching sequences together.
This argument indicates to the model which tokens should be attended to, ... |
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