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https://huggingface.co/nikitawriter	1 1 Nikita Osadchiy nikitawriter https://writer.com osadchiyn Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/starlingfeather	2 Ambar starlingfeather Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/TorpedoFrank	1 Alexander TorpedoFrank Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/ZaneRavenholt	Zane Ravenholt ZaneRavenholt Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/kirkg	2 1 kirk goddard kirkg Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/heathexer	1 Heath Robinson heathexer heathexer Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/heathexer-writer	1 Heath Robinson heathexer-writer heathexer Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/mark-saad	1 Mark Saad mark-saad Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/andrewracine	1 1 Andrew Racine andrewracine AndrewRacine Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/dsweenor	1 1 David Sweenor dsweenor Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/hussamo	1 otri hussamo Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/kirill-writer	1 Kyrylo Buha kirill-writer Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/chrisbryant	2 Chris Bryant chrisbryant Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/Juliene	1 Santos Juliene Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/kevin-w	Kevin Wei kevin-w Research interests None yet Organizations models None public yet datasets None public yet
https://huggingface.co/docs/transformers/quicktour	Quick tour Get up and running with 🤗 Transformers! Whether you’re a developer or an everyday user, this quick tour will help you get started and show you how to use the pipeline() for inference, load a pretrained model and preprocessor with an AutoClass, and quickly train a model with PyTorch or TensorFlow. If you’re ...
https://huggingface.co/docs/transformers/training	Train a TensorFlow model with Keras You can also train 🤗 Transformers models in TensorFlow with the Keras API! Loading data for Keras When you want to train a 🤗 Transformers model with the Keras API, you need to convert your dataset to a format that Keras understands. If your dataset is small, you can just convert th...
https://huggingface.co/docs/transformers/preprocessing	Preprocess Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data for the ...
https://huggingface.co/docs/transformers/autoclass_tutorial	Load pretrained instances with an AutoClass With so many different Transformer architectures, it can be challenging to create one for your checkpoint. As a part of 🤗 Transformers core philosophy to make the library easy, simple and flexible to use, an AutoClass automatically infers and loads the correct architecture f...
https://huggingface.co/docs/transformers/installation	Installation Install 🤗 Transformers for whichever deep learning library you’re working with, setup your cache, and optionally configure 🤗 Transformers to run offline. 🤗 Transformers is tested on Python 3.6+, PyTorch 1.1.0+, TensorFlow 2.0+, and Flax. Follow the installation instructions below for the deep learning l...
https://huggingface.co/docs/transformers/pipeline_tutorial	Pipelines for inference The pipeline() makes it simple to use any model from the Hub for inference on any language, computer vision, speech, and multimodal tasks. Even if you don’t have experience with a specific modality or aren’t familiar with the underlying code behind the models, you can still use them for inferenc...
https://huggingface.co/docs/transformers/run_scripts	Train with a script Along with the 🤗 Transformers notebooks, there are also example scripts demonstrating how to train a model for a task with PyTorch, TensorFlow, or JAX/Flax. You will also find scripts we’ve used in our research projects and legacy examples which are mostly community contributed. These scripts are n...
https://huggingface.co/docs/transformers/peft	Load adapters with 🤗 PEFT Parameter-Efficient Fine Tuning (PEFT) methods freeze the pretrained model parameters during fine-tuning and add a small number of trainable parameters (the adapters) on top of it. The adapters are trained to learn task-specific information. This approach has been shown to be very memory-effi...
https://huggingface.co/Writer/palmyra-med-20b	Palmyra-med-20b Model description Palmyra-Med-20b is a 20 billion parameter Large Language Model that has been uptrained on Palmyra-Large with a specialized custom-curated medical dataset. The main objective of this model is to enhance performance in tasks related to medical dialogue and question-answering. Developed...
https://huggingface.co/docs/transformers/multilingual	Multilingual models for inference 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, can be used just like a monolingual model. This guide will show y...
https://huggingface.co/docs/transformers/model_sharing	Share a model The last two tutorials showed how you can fine-tune a model with PyTorch, Keras, and 🤗 Accelerate for distributed setups. The next step is to share your model with the community! At Hugging Face, we believe in openly sharing knowledge and resources to democratize artificial intelligence for everyone. We ...
https://huggingface.co/docs/transformers/transformers_agents	Transformers Agents Transformers Agents is an experimental API which is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models are prone to change. Transformers version v4.29.0, building on the concept of tools and agents. You can play with in this colab. In short, it pr...
https://huggingface.co/docs/transformers/llm_tutorial	Generation with LLMs LLMs, or Large Language Models, are the key component behind text generation. In a nutshell, they consist of large pretrained transformer models trained to predict the next word (or, more precisely, token) given some input text. Since they predict one token at a time, you need to do something more ...
https://huggingface.co/docs/transformers/custom_models	Sharing custom models The 🤗 Transformers library is designed to be easily extensible. Every model is fully coded in a given subfolder of the repository with no abstraction, so you can easily copy a modeling file and tweak it to your needs. If you are writing a brand new model, it might be easier to start from scratch....
https://huggingface.co/docs/transformers/accelerate	Distributed training with 🤗 Accelerate As models get bigger, parallelism has emerged as a strategy for training larger models on limited hardware and accelerating training speed by several orders of magnitude. At Hugging Face, we created the 🤗 Accelerate library to help users easily train a 🤗 Transformers model on a...
https://huggingface.co/docs/transformers/sagemaker	Transformers documentation Run training on Amazon SageMaker Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes
https://huggingface.co/docs/transformers/fast_tokenizers	Use tokenizers from 🤗 Tokenizers The PreTrainedTokenizerFast depends on the 🤗 Tokenizers library. The tokenizers obtained from the 🤗 Tokenizers library can be loaded very simply into 🤗 Transformers. Before getting in the specifics, let’s first start by creating a dummy tokenizer in a few lines: >>> from tokenizers ...
https://huggingface.co/docs/transformers/serialization	Export to ONNX Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into a serialized format that can be loaded and executed on specialized runtimes and hardware. 🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or Tens...
https://huggingface.co/docs/transformers/benchmarks	Benchmarks Hugging Face’s Benchmarking tools are deprecated and it is advised to use external Benchmarking libraries to measure the speed and memory complexity of Transformer models. Let’s take a look at how 🤗 Transformers models can be benchmarked, best practices, and already available benchmarks. A notebook explaini...
https://huggingface.co/docs/transformers/chat_templating	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 includes a role as w...
https://huggingface.co/docs/transformers/create_a_model	Create a custom architecture An AutoClass automatically infers the model architecture and downloads pretrained configuration and weights. Generally, we recommend using an AutoClass to produce checkpoint-agnostic code. But users who want more control over specific model parameters can create a custom 🤗 Transformers mod...
https://huggingface.co/docs/transformers/tflite	Export to TFLite TensorFlow Lite is a lightweight framework for deploying machine learning models on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. TFLite is designed to optimize and run models efficiently on these devices with limited computational power, m...
https://huggingface.co/docs/transformers/notebooks	🤗 Transformers Notebooks You can find here a list of the official notebooks provided by Hugging Face. Also, we would like to list here interesting content created by the community. If you wrote some notebook(s) leveraging 🤗 Transformers and would like to be listed here, please open a Pull Request so it can be include...
https://huggingface.co/docs/transformers/torchscript	Export to TorchScript This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases, with more code examples, a more flexible implementation, and benchmarks ...
https://huggingface.co/docs/transformers/community	Fine-tune a pre-trained Transformer to generate lyrics How to generate lyrics in the style of your favorite artist by fine-tuning a GPT-2 model Aleksey Korshuk Train T5 in Tensorflow 2 How to train T5 for any task using Tensorflow 2. This notebook demonstrates a Question & Answer task implemented in Tensorflow 2 using ...
https://huggingface.co/docs/transformers/main_classes/processors	Processors Processors can mean two different things in the Transformers library: the objects that pre-process inputs for multi-modal models such as Wav2Vec2 (speech and text) or CLIP (text and vision) deprecated objects that were used in older versions of the library to preprocess data for GLUE or SQUAD. Multi-modal pr...
https://huggingface.co/docs/transformers/main_classes/quantization	Quantize 🤗 Transformers models AutoGPTQ Integration 🤗 Transformers has integrated optimum API to perform GPTQ quantization on language models. You can load and quantize your model in 8, 4, 3 or even 2 bits without a big drop of performance and faster inference speed! This is supported by most GPU hardwares. To learn ...
https://huggingface.co/docs/transformers/internal/pipelines_utils	Utilities for pipelines This page lists all the utility functions the library provides for pipelines. Most of those are only useful if you are studying the code of the models in the library. Argument handling class transformers.pipelines.ArgumentHandler < source > ( ) Base interface for handling arguments for each Pip...
https://huggingface.co/docs/transformers/internal/modeling_utils	Custom Layers and Utilities This page lists all the custom layers used by the library, as well as the utility functions it provides for modeling. Most of those are only useful if you are studying the code of the models in the library. Pytorch custom modules class transformers.Conv1D < source > ( nf nx ) Parameters nf...
https://huggingface.co/docs/transformers/main_classes/feature_extractor	Feature Extractor A feature extractor is in charge of preparing input features for audio or vision models. This includes feature extraction from sequences, e.g., pre-processing audio files to Log-Mel Spectrogram features, feature extraction from images e.g. cropping image image files, but also padding, normalization, a...
https://huggingface.co/docs/transformers/main_classes/image_processor	Transformers documentation Image Processor Image Processor An image processor is in charge of preparing input features for vision models and post processing their outputs. This includes transformations such as resizing, normalization, and conversion to PyTorch, TensorFlow, Flax and Numpy tensors. It may also include m...
https://huggingface.co/docs/transformers/main_classes/tokenizer	Tokenizer A tokenizer is in charge of preparing the inputs for a model. The library contains tokenizers for all the models. Most of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the Rust library 🤗 Tokenizers. The “Fast” implementations allows: a signific...
https://huggingface.co/docs/transformers/main_classes/deepspeed	DeepSpeed Integration DeepSpeed implements everything described in the ZeRO paper. Currently it provides full support for: Optimizer state partitioning (ZeRO stage 1) Gradient partitioning (ZeRO stage 2) Parameter partitioning (ZeRO stage 3) Custom mixed precision training handling A range of fast CUDA-extension-based ...
https://huggingface.co/thegovind	12 Govind K thegovind thegovind Research interests None yet Organizations spaces 4 pinned 3 ⛓️ LangFlow Build error 🐠 Segment Anything Stopped 📊 Reddog Pill Training Build error 💻 Reddog Sandbox models 2 thegovind/reddogpillmodel512 Text-to-Image • Updated Dec 12, 2022 • 533 • 1 thegovind/pills1testmodel Text-...
https://huggingface.co/docs/transformers/internal/tokenization_utils	Most of those are only useful if you are studying the code of the tokenizers in the library. class transformers.PreTrainedTokenizerBase < source > ( **kwargs ) Parameters model_max_length (int, optional) — The maximum length (in number of tokens) for the inputs to the transformer model. When the tokenizer is loaded w...
https://huggingface.co/docs/transformers/main_classes/trainer	Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. It’s used in most of the example scripts. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. The API supports distributed training on mult...
https://huggingface.co/docs/transformers/main_classes/pipelines	Pipelines The pipelines are a great and easy way to use models for inference. These pipelines are objects that abstract most of the complex code from the library, offering a simple API dedicated to several tasks, including Named Entity Recognition, Masked Language Modeling, Sentiment Analysis, Feature Extraction and Qu...
https://huggingface.co/docs/transformers/internal/time_series_utils	Time Series Utilities This page lists all the utility functions and classes that can be used for Time Series based models. Most of those are only useful if you are studying the code of the time series models or you wish to add to the collection of distributional output classes. Distributional Output class transformers....
https://huggingface.co/docs/transformers/model_doc/bark	Bark Overview Bark is a transformer-based text-to-speech model proposed by Suno AI in suno-ai/bark. Bark is made of 4 main models: BarkSemanticModel (also referred to as the ‘text’ model): a causal auto-regressive transformer model that takes as input tokenized text, and predicts semantic text tokens that capture the m...
https://huggingface.co/docs/transformers/model_doc/autoformer	Autoformer Overview The Autoformer model was proposed in Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long. This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the tre...
https://huggingface.co/docs/transformers/internal/image_processing_utils	Utilities for Image Processors This page lists all the utility functions used by the image processors, mainly the functional transformations used to process the images. Most of those are only useful if you are studying the code of the image processors in the library. Image Transformations transformers.image_transforms....
https://huggingface.co/docs/transformers/internal/audio_utils	Utilities for FeatureExtractors This page lists all the utility functions that can be used by the audio FeatureExtractor in order to compute special features from a raw audio using common algorithms such as Short Time Fourier Transform or log mel spectrogram. Most of those are only useful if you are studying the code o...
https://huggingface.co/docs/transformers/internal/file_utils	Transformers documentation General Utilities General Utilities This page lists all of Transformers general utility functions that are found in the file utils.py. Most of those are only useful if you are studying the general code in the library. Enums and namedtuples class transformers.utils.ExplicitEnum < source > ( v...
https://huggingface.co/docs/transformers/model_doc/align	ALIGN Overview The ALIGN model was proposed in Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. ALIGN is a multi-modal vision and language model. It can be u...
https://huggingface.co/docs/transformers/model_doc/altclip	AltCLIP Overview The AltCLIP model was proposed in AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities by Zhongzhi Chen, Guang Liu, Bo-Wen Zhang, Fulong Ye, Qinghong Yang, Ledell Wu. AltCLIP (Altering the Language Encoder in CLIP) is a neural network trained on a variety of image-text and ...
https://huggingface.co/docs/transformers/model_doc/bart	BART DISCLAIMER: If you see something strange, file a Github Issue and assign @patrickvonplaten Overview The Bart model was proposed in BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrah...
https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer	Audio Spectrogram Transformer Overview The Audio Spectrogram Transformer model was proposed in AST: Audio Spectrogram Transformer by Yuan Gong, Yu-An Chung, James Glass. The Audio Spectrogram Transformer applies a Vision Transformer to audio, by turning audio into an image (spectrogram). The model obtains state-of-the-...
https://huggingface.co/docs/transformers/model_doc/albert	ALBERT Overview The ALBERT model was proposed in ALBERT: A Lite BERT for Self-supervised Learning of Language Representations by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut. It presents two parameter-reduction techniques to lower memory consumption and increase the training ...
https://huggingface.co/docs/transformers/internal/trainer_utils	Utilities for Trainer This page lists all the utility functions used by Trainer. Most of those are only useful if you are studying the code of the Trainer in the library. Utilities class transformers.EvalPrediction < source > ( predictions: typing.Union[numpy.ndarray, typing.Tuple[numpy.ndarray]] label_ids: typing.Unio...
https://huggingface.co/docs/transformers/model_doc/barthez	BARThez Overview The BARThez model was proposed in BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis on 23 Oct, 2020. The abstract of the paper: Inductive transfer learning, enabled by self-supervised learning, have taken the entire Natur...
https://huggingface.co/docs/transformers/internal/generation_utils	Utilities for Generation This page lists all the utility functions used by generate(), greedy_search(), contrastive_search(), sample(), beam_search(), beam_sample(), group_beam_search(), and constrained_beam_search(). Most of those are only useful if you are studying the code of the generate methods in the library. Gen...
https://huggingface.co/docs/transformers/perf_train_tpu	Transformers documentation Training on TPUs Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes Training on TPUs Note: Most of the strategies introduced in ...
https://huggingface.co/docs/transformers/perf_train_cpu_many	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 (collective communications library) is a library for efficient distributed deep...
https://huggingface.co/docs/transformers/perf_infer_gpu_one	Efficient Inference on a Single GPU In addition to this guide, relevant information can be found as well in the guide for training on a single GPU and the guide for inference on CPUs. Flash Attention 2 Note that this feature is experimental and might considerably change in future versions. For instance, the Flash Atten...
https://huggingface.co/docs/transformers/perf_train_tpu_tf	Training on TPU with TensorFlow If you don’t need long explanations and just want TPU code samples to get started with, check out our TPU example notebook! What is a TPU? A TPU is a Tensor Processing Unit. They are hardware designed by Google, which are used to greatly speed up the tensor computations within neural net...
https://huggingface.co/docs/transformers/hpo_train	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. Hyperparamet...
https://huggingface.co/docs/transformers/perf_train_gpu_one	Methods and tools for efficient training on a single GPU This guide demonstrates practical techniques that you can use to increase the efficiency of your model’s training by optimizing memory utilization, speeding up the training, or both. If you’d like to understand how GPU is utilized during training, please refer to...
https://huggingface.co/docs/transformers/performance	Performance and Scalability Training large transformer models and deploying them to production present various challenges. During training, the model may require more GPU memory than available or exhibit slow training speed. In the deployment phase, the model can struggle to handle the required throughput in a producti...
https://huggingface.co/docs/transformers/perf_hardware	Custom hardware for training The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer’s excellent blog post. Let’s have a look at some practical advice for GPU setups. GPU When you train bigger models you have essentially...
https://huggingface.co/docs/transformers/perf_train_special	Transformers documentation Training on Specialized Hardware Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes Training on Specialized Hardware Note: Most ...
https://huggingface.co/docs/transformers/perf_train_cpu	Efficient Training on CPU This guide focuses on training large models efficiently on CPU. Mixed precision with IPEX IPEX is optimized for CPUs with AVX-512 or above, and functionally works for CPUs with only AVX2. So, it is expected to bring performance benefit for Intel CPU generations with AVX-512 or above while CPUs...
https://huggingface.co/docs/transformers/custom_tools	Custom Tools and Prompts If you are not aware of what tools and agents are in the context of transformers, we recommend you read the Transformers Agents page first. Transformers Agents is an experimental API that is subject to change at any time. Results returned by the agents can vary as the APIs or underlying models ...
https://huggingface.co/docs/transformers/perf_infer_cpu	Efficient Inference on CPU This guide focuses on inferencing large models efficiently on CPU. BetterTransformer for faster inference We have recently integrated BetterTransformer for faster inference on CPU for text, image and audio models. Check the documentation about this integration here for more details. PyTorch J...
https://huggingface.co/docs/transformers/perf_train_gpu_many	Efficient Training on Multiple GPUs When training on a single GPU is too slow or the model weights don’t fit in a single GPUs memory we use a multi-GPU setup. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. There are several techniques to achieve parallism ...
https://huggingface.co/docs/transformers/troubleshooting	Troubleshoot Sometimes errors occur, but we are here to help! This guide covers some of the most common issues we’ve seen and how you can resolve them. However, this guide isn’t meant to be a comprehensive collection of every 🤗 Transformers issue. For more help with troubleshooting your issue, try: Asking for help on ...
https://huggingface.co/docs/transformers/perf_infer_special	Transformers documentation Inference on Specialized Hardware Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes Inference on Specialized Hardware This docu...
https://huggingface.co/docs/transformers/perf_infer_gpu_many	Efficient Inference on a Multiple GPUs This document contains information on how to efficiently infer on a multiple GPUs. Note: A multi GPU setup can use the majority of the strategies described in the single GPU section. You must be aware of simple techniques, though, that can be used for a better usage. Flash Attenti...
https://huggingface.co/docs/transformers/model_doc/bartpho	BARTpho Overview The BARTpho model was proposed in BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen. The abstract from the paper is the following: We present BARTpho with two versions — BARTpho_word and BARTpho_syllable — the first public large-scal...
https://huggingface.co/docs/transformers/tf_xla	XLA Integration for TensorFlow Models Accelerated Linear Algebra, dubbed XLA, is a compiler for accelerating the runtime of TensorFlow Models. From the official documentation: XLA (Accelerated Linear Algebra) is a domain-specific compiler for linear algebra that can accelerate TensorFlow models with potentially no sour...
https://huggingface.co/docs/transformers/add_tensorflow_model	How to convert a 🤗 Transformers model to TensorFlow? Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that adding TensorFlow compatibilit...
https://huggingface.co/docs/transformers/debugging	Debugging Multi-GPU Network Issues Debug When training or inferencing with DistributedDataParallel and multiple GPU, if you run into issue of inter-communication between processes and/or nodes, you can use the following script to diagnose network issues. wget https://raw.githubusercontent.com/huggingface/transformers/m...
https://huggingface.co/docs/transformers/model_doc/bert-generation	BertGeneration Overview The BertGeneration model is a BERT model that can be leveraged for sequence-to-sequence tasks using EncoderDecoderModel as proposed in Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn. The abstract from the paper is the following:...
https://huggingface.co/docs/transformers/perf_torch_compile	Optimize inference using torch.compile() This guide aims to provide a benchmark on the inference speed-ups introduced with torch.compile() for computer vision models in 🤗 Transformers. Benefits of torch.compile Depending on the model and the GPU, `torch.compile()` yields up to 30% speed-up during inference. To use `to...
https://huggingface.co/docs/transformers/add_new_model	How to add a model to 🤗 Transformers? The 🤗 Transformers library is often able to offer new models thanks to community contributors. But this can be a challenging project and requires an in-depth knowledge of the 🤗 Transformers library and the model to implement. At Hugging Face, we’re trying to empower more of the ...
https://huggingface.co/docs/transformers/contributing	Contribute to 🤗 Transformers Everyone is welcome to contribute, and we value everybody’s contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable. It also helps us if you spread the word! Reference the ...
https://huggingface.co/docs/transformers/testing	Testing Let’s take a look at how 🤗 Transformers models are tested and how you can write new tests and improve the existing ones. There are 2 test suites in the repository: tests — tests for the general API examples — tests primarily for various applications that aren’t part of the API How transformers are tested Once ...
https://huggingface.co/docs/transformers/add_new_pipeline	How to create a custom pipeline? In this guide, we will see how to create a custom pipeline and share it on the Hub or add it to the 🤗 Transformers library. First and foremost, you need to decide the raw entries the pipeline will be able to take. It can be strings, raw bytes, dictionaries or whatever seems to be the m...
https://huggingface.co/docs/transformers/model_doc/beit	BEiT Overview The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predic...
https://huggingface.co/docs/transformers/big_models	Instantiating a big model When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow from PyTorch is: Create your model with random weights. Load your pretrained weights. Put those pretrained weights in your random model. Step 1 and 2 both require a full versio...
https://huggingface.co/docs/transformers/model_doc/bert	BERT Overview The BERT model was proposed in BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova. It’s a bidirectional transformer pretrained using a combination of masked language modeling objective and next sentence predic...
https://huggingface.co/docs/transformers/model_doc/bertweet	BERTweet Overview The BERTweet model was proposed in BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu, Anh Tuan Nguyen. The abstract from the paper is the following: We present BERTweet, the first public large-scale pre-trained language model for English Tweets. Our BERTweet, havin...
https://huggingface.co/docs/transformers/model_doc/bort	This model is in maintenance mode only, so we won’t accept any new PRs changing its code. If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0. You can do so by running the following command: pip install -U transformers==4.30.0. The BORT model was proposed ...
https://huggingface.co/docs/transformers/model_doc/byt5	ByT5 Overview The ByT5 model was presented in ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel. The abstract from the paper is the following: Most widely-used pre-trained language model...
https://huggingface.co/docs/transformers/model_doc/bros	BROS Overview The BROS model was proposed in BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents by Teakgyu Hong, Donghyun Kim, Mingi Ji, Wonseok Hwang, Daehyun Nam, Sungrae Park. BROS stands for BERT Relying On Spatiality. It is an encoder-only Transforme...
https://huggingface.co/docs/transformers/model_doc/biogpt	BioGPT Overview The BioGPT model was proposed in BioGPT: generative pre-trained transformer for biomedical text generation and mining by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu. BioGPT is a domain-specific generative pre-trained Transformer language model for biomedical tex...

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