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There are 2 main ways to use SigLIP: either using the pipeline API, which abstracts away all the complexity for you, or by using the `SiglipModel` class yourself. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#usage-example | #usage-example | .md | 292_3 |
The pipeline allows to use the model in a few lines of code:
```python
>>> from transformers import pipeline
>>> from PIL import Image
>>> import requests
>>> # load pipe
>>> image_classifier = pipeline(task="zero-shot-image-classification", model="google/siglip-base-patch16-224")
>>> # load image
>>> url = 'http:/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#pipeline-api | #pipeline-api | .md | 292_4 |
If you want to do the pre- and postprocessing yourself, here's how to do that:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, AutoModel
>>> import torch
>>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224")
>>> processor = AutoProcessor.from_pret... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#using-the-model-yourself | #using-the-model-yourself | .md | 292_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with SigLIP.
- [Zero-shot image classification task guide](../tasks/zero_shot_image_classification)
- Demo notebooks for SigLIP can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/SigLIP)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#resources | #resources | .md | 292_6 |
First, make sure to install the latest version of Flash Attention 2.
```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of flash-attn repository. Make also sure to load your mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#combining-siglip-and-flash-attention-2 | #combining-siglip-and-flash-attention-2 | .md | 292_7 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 292_8 |
Below is an expected speedup diagram that compares inference time between the native implementation in transformers using `google/siglip-so400m-patch14-384` checkpoint in `float16` precision and the Flash Attention 2 / SDPA version of the model using different batch sizes.
<div style="text-align: center">
<img src="h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#expected-speedups | #expected-speedups | .md | 292_9 |
[`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs.
Instantiating a configuration with the defaults will yield a similar configuration to that of the ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipconfig | #siglipconfig | .md | 292_10 |
This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
Siglip text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the text encoder of th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#sigliptextconfig | #sigliptextconfig | .md | 292_11 |
This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
Siglip vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipvisionconfig | #siglipvisionconfig | .md | 292_12 |
Construct a Siglip tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#sigliptokenizer | #sigliptokenizer | .md | 292_13 |
Constructs a SigLIP image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"height": 224, "width": 224}`):... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipimageprocessor | #siglipimageprocessor | .md | 292_14 |
Constructs a Siglip processor which wraps a Siglip image processor and a Siglip tokenizer into a single processor.
[`SiglipProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`SiglipTokenizer`]. See the
[`~SiglipProcessor.__call__`] and [`~SiglipProcessor.decode`] for more information.
Args:... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipprocessor | #siglipprocessor | .md | 292_15 |
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipmodel | #siglipmodel | .md | 292_16 |
The text model from SigLIP without any head or projection on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a Py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#sigliptextmodel | #sigliptextmodel | .md | 292_17 |
The vision model from SigLIP without any head or projection on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipvisionmodel | #siglipvisionmodel | .md | 292_18 |
SigLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
the patch tokens) e.g. for ImageNet.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as dow... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/siglip.md | https://huggingface.co/docs/transformers/en/model_doc/siglip/#siglipforimageclassification | #siglipforimageclassification | .md | 292_19 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/ | .md | 293_0 | |
The PLBART model was proposed in [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
This is a BART-like model which can be used to perform code-summarization, code-generation, and code-translation tasks... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#overview | #overview | .md | 293_1 |
PLBart is a multilingual encoder-decoder (sequence-to-sequence) model primarily intended for code-to-text, text-to-code, code-to-code tasks. As the
model is multilingual it expects the sequences in a different format. A special language id token is added in both the
source and target text. The source text format is `X ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#usage-examples | #usage-examples | .md | 293_2 |
```python
>>> from transformers import PLBartForConditionalGeneration, PLBartTokenizer
>>> tokenizer = PLBartTokenizer.from_pretrained("uclanlp/plbart-base", src_lang="en_XX", tgt_lang="python")
>>> example_python_phrase = "def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])"
>>> expected_translation_english = "Retu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#supervised-training | #supervised-training | .md | 293_3 |
While generating the target text set the `decoder_start_token_id` to the target language id. The following
example shows how to translate Python to English using the `uclanlp/plbart-python-en_XX` model.
```python
>>> from transformers import PLBartForConditionalGeneration, PLBartTokenizer
>>> tokenizer = PLBartToken... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#generation | #generation | .md | 293_4 |
- [Text classification task guide](../tasks/sequence_classification)
- [Causal language modeling task guide](../tasks/language_modeling)
- [Translation task guide](../tasks/translation)
- [Summarization task guide](../tasks/summarization) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#resources | #resources | .md | 293_5 |
This is the configuration class to store the configuration of a [`PLBartModel`]. It is used to instantiate an
PLBART model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of the PLBART
[uclanlp/plbart-base]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartconfig | #plbartconfig | .md | 293_6 |
Construct an PLBART tokenizer.
Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
[SentencePiece](https://github.com/google/sentencepiece).
The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
<tokens> <eos>` for target language documents. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbarttokenizer | #plbarttokenizer | .md | 293_7 |
The bare PLBART Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartmodel | #plbartmodel | .md | 293_8 |
The PLBART Model with a language modeling head. Can be used for code-to-text, text-to-code and code-to-code.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforconditionalgeneration | #plbartforconditionalgeneration | .md | 293_9 |
PLBart model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for code
classification.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforsequenceclassification | #plbartforsequenceclassification | .md | 293_10 |
No docstring available for PLBartForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/plbart.md | https://huggingface.co/docs/transformers/en/model_doc/plbart/#plbartforcausallm | #plbartforcausallm | .md | 293_11 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/ | .md | 294_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=t5">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-t5-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/t5-base">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5 | #t5 | .md | 294_1 |
The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.10683.pdf) by [Colin Raffel](https://huggingface.co/craffel), Noam Shazeer, [Adam Roberts](https://huggingface.co/adarob), Katherine Lee, Sharan Narang,
Michael Matena, Yanqi Zhou... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#overview | #overview | .md | 294_2 |
- T5 is an encoder-decoder model pre-trained on a multi-task mixture of unsupervised and supervised tasks and for which
each task is converted into a text-to-text format. T5 works well on a variety of tasks out-of-the-box by prepending a
different prefix to the input corresponding to each task, e.g., for translation: *... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#usage-tips | #usage-tips | .md | 294_3 |
T5 is an encoder-decoder model and converts all NLP problems into a text-to-text format. It is trained using teacher
forcing. This means that for training, we always need an input sequence and a corresponding target sequence. The input
sequence is fed to the model using `input_ids`. The target sequence is shifted to th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#training | #training | .md | 294_4 |
At inference time, it is recommended to use [`~generation.GenerationMixin.generate`]. This
method takes care of encoding the input and feeding the encoded hidden states via cross-attention layers to the decoder
and auto-regressively generates the decoder output. Check out [this blog post](https://huggingface.co/blog/ho... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#inference | #inference | .md | 294_5 |
If you'd like a faster training and inference performance, install [NVIDIA APEX](https://github.com/NVIDIA/apex#quick-start) for NVIDIA GPUs, or [ROCm APEX](https://github.com/ROCmSoftwarePlatform/apex) for AMD GPUs and then the model will automatically use `apex.normalization.FusedRMSNorm` instead of `T5LayerNorm`. Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#performance | #performance | .md | 294_6 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with T5. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an exist... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#resources | #resources | .md | 294_7 |
This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to
instantiate a T5 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the T5
[google-t5/t5-s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5config | #t5config | .md | 294_8 |
Construct a T5 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
[SentencePiece](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5tokenizer | #t5tokenizer | .md | 294_9 |
Construct a "fast" T5 tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to thi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5tokenizerfast | #t5tokenizerfast | .md | 294_10 |
The bare T5 Model transformer outputting raw hidden-states without any specific head on top.
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Mic... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5model | #t5model | .md | 294_11 |
T5 Model with a `language modeling` head on top.
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Li... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5forconditionalgeneration | #t5forconditionalgeneration | .md | 294_12 |
The bare T5 Model transformer outputting encoder's raw hidden-states without any specific head on top.
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan
N... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5encodermodel | #t5encodermodel | .md | 294_13 |
T5 model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g. for GLUE
tasks.
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel, Noam Shazeer, Adam Roberts, Katherin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5forsequenceclassification | #t5forsequenceclassification | .md | 294_14 |
T5 Encoder Model with a token classification head on top (a linear layer on top of the hidden-states output)
e.g. for Named-Entity-Recognition (NER) tasks.
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5fortokenclassification | #t5fortokenclassification | .md | 294_15 |
T5 Model with a span classification head on top for extractive question-answering tasks like SQuAD (linear layers
on top of the hidden-states output to compute `span start logits` and `span end logits`).
The T5 model was proposed in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text
Transformer](h... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#t5forquestionanswering | #t5forquestionanswering | .md | 294_16 |
No docstring available for TFT5Model
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#tft5model | #tft5model | .md | 294_17 |
No docstring available for TFT5ForConditionalGeneration
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#tft5forconditionalgeneration | #tft5forconditionalgeneration | .md | 294_18 |
No docstring available for TFT5EncoderModel
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#tft5encodermodel | #tft5encodermodel | .md | 294_19 |
No docstring available for FlaxT5Model
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#flaxt5model | #flaxt5model | .md | 294_20 |
No docstring available for FlaxT5ForConditionalGeneration
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#flaxt5forconditionalgeneration | #flaxt5forconditionalgeneration | .md | 294_21 |
No docstring available for FlaxT5EncoderModel
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5.md | https://huggingface.co/docs/transformers/en/model_doc/t5/#flaxt5encodermodel | #flaxt5encodermodel | .md | 294_22 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/ | .md | 295_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=gpt2">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-gpt2-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/gpt2">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Fac... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#openai-gpt2 | #openai-gpt2 | .md | 295_1 |
OpenAI GPT-2 model was proposed in [Language Models are Unsupervised Multitask Learners](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) by Alec
Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever from [OpenAI](https://huggingface.co/opena... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#overview | #overview | .md | 295_2 |
- GPT-2 is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- GPT-2 was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows GPT-2 to generate syn... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#usage-tips | #usage-tips | .md | 295_3 |
The `generate()` method can be used to generate text using GPT2 model.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
>>> prompt = "GPT2 is a model developed by OpenAI."
>>> in... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#usage-example | #usage-example | .md | 295_4 |
Flash Attention 2 is a faster, optimized version of the attention scores computation which relies on `cuda` kernels. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#using-flash-attention-2 | #using-flash-attention-2 | .md | 295_5 |
First, check whether your hardware is compatible with Flash Attention 2. The latest list of compatible hardware can be found in the [official documentation](https://github.com/Dao-AILab/flash-attention#installation-and-features). If your hardware is not compatible with Flash Attention 2, you can still benefit from atte... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#installation | #installation | .md | 295_6 |
To load a model using Flash Attention 2, we can pass the argument `attn_implementation="flash_attention_2"` to [`.from_pretrained`](https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained). We'll also load the model in half-precision (e.g. `torch.float16`), since... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#usage | #usage | .md | 295_7 |
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `gpt2` checkpoint and the Flash Attention 2 version of the model using a sequence length of 512.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/EduardoPacheco/docu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#expected-speedups | #expected-speedups | .md | 295_8 |
PyTorch includes a native scaled dot-product attention (SDPA) operator as part of `torch.nn.functional`. This function
encompasses several implementations that can be applied depending on the inputs and the hardware in use. See the
[official documentation](https://pytorch.org/docs/stable/generated/torch.nn.functional.s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 295_9 |
| Batch size | Seq len | Time per batch (Eager - s) | Time per batch (SDPA - s) | Speedup (%) | Eager peak mem (MB) | SDPA peak mem (MB) | Mem saving (%) |
|-----------:|--------:|----------------------------:|--------------------------:|------------:|--------------------:|-------------------:|------------------:|
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#training | #training | .md | 295_10 |
| Batch size | Seq len | Per token latency Eager (ms) | Per token latency SDPA (ms) | Speedup (%) | Mem Eager (MB) | Mem SDPA (MB) | Mem saved (%) |
|-----------:|--------:|-----------------------------:|----------------------------:|------------:|---------------:|--------------:|--------------:|
| 1 | 128... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#inference | #inference | .md | 295_11 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT2. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an exi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#resources | #resources | .md | 295_12 |
This is the configuration class to store the configuration of a [`GPT2Model`] or a [`TFGPT2Model`]. It is used to
instantiate a GPT-2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the GPT-2
[open... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2config | #gpt2config | .md | 295_13 |
Construct a GPT-2 tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers import... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2tokenizer | #gpt2tokenizer | .md | 295_14 |
Construct a "fast" GPT-2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2tokenizerfast | #gpt2tokenizerfast | .md | 295_15 |
models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.FloatTensor` of shape `(1,)`, *optional*... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2-specific-outputs | #gpt2-specific-outputs | .md | 295_16 |
The bare GPT2 Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2model | #gpt2model | .md | 295_17 |
The GPT2 Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2lmheadmodel | #gpt2lmheadmodel | .md | 295_18 |
The GPT2 Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classificatio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2doubleheadsmodel | #gpt2doubleheadsmodel | .md | 295_19 |
The GPT-2 Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the gener... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2forquestionanswering | #gpt2forquestionanswering | .md | 295_20 |
The GPT2 Model transformer with a sequence classification head on top (linear layer).
[`GPT2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-1) do.
Since it does classification on the last token, it requires to know the position of the last token. I... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2forsequenceclassification | #gpt2forsequenceclassification | .md | 295_21 |
GPT2 Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#gpt2fortokenclassification | #gpt2fortokenclassification | .md | 295_22 |
No docstring available for TFGPT2Model
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfgpt2model | #tfgpt2model | .md | 295_23 |
No docstring available for TFGPT2LMHeadModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfgpt2lmheadmodel | #tfgpt2lmheadmodel | .md | 295_24 |
No docstring available for TFGPT2DoubleHeadsModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfgpt2doubleheadsmodel | #tfgpt2doubleheadsmodel | .md | 295_25 |
No docstring available for TFGPT2ForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfgpt2forsequenceclassification | #tfgpt2forsequenceclassification | .md | 295_26 |
[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast: No module named 'tensorflow' | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfsequenceclassifieroutputwithpast | #tfsequenceclassifieroutputwithpast | .md | 295_27 |
No docstring available for TFGPT2Tokenizer
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#tfgpt2tokenizer | #tfgpt2tokenizer | .md | 295_28 |
No docstring available for FlaxGPT2Model
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#flaxgpt2model | #flaxgpt2model | .md | 295_29 |
No docstring available for FlaxGPT2LMHeadModel
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/gpt2/#flaxgpt2lmheadmodel | #flaxgpt2lmheadmodel | .md | 295_30 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo2.md | https://huggingface.co/docs/transformers/en/model_doc/olmo2/ | .md | 296_0 | |
The OLMo2 model is the successor of the OLMo model, which was proposed in
[OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838).
The architectural changes from the original OLMo model to this model are:
- RMSNorm is used instead of standard layer norm.
- Norm is applied to attention ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo2.md | https://huggingface.co/docs/transformers/en/model_doc/olmo2/#overview | #overview | .md | 296_1 |
This is the configuration class to store the configuration of a [`Olmo2Model`]. It is used to instantiate an OLMo2
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [allenai/Olmo2-7B-1124-hf](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo2.md | https://huggingface.co/docs/transformers/en/model_doc/olmo2/#olmo2config | #olmo2config | .md | 296_2 |
The bare Olmo2 Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo2.md | https://huggingface.co/docs/transformers/en/model_doc/olmo2/#olmo2model | #olmo2model | .md | 296_3 |
No docstring available for Olmo2ForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo2.md | https://huggingface.co/docs/transformers/en/model_doc/olmo2/#olmo2forcausallm | #olmo2forcausallm | .md | 296_4 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/ | .md | 297_0 | |
The LLaVA-OneVision model was proposed in [LLaVA-OneVision: Easy Visual Task Transfer](https://arxiv.org/abs/2408.03326) by <Bo Li, Yuanhan Zhang, Dong Guo, Renrui Zhang, Feng Li, Hao Zhang, Kaichen Zhang, Yanwei Li, Ziwei Liu, Chunyuan Li
LLaVA-OneVision is a Vision-Language Model that can generate text conditioned ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#overview | #overview | .md | 297_1 |
Here's how to load the model and perform inference in half-precision (`torch.float16`):
```python
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
import torch
from PIL import Image
import requests
processor = AutoProcessor.from_pretrained("llava-hf/llava-onevision-qwen2-7b-ov-hf")
mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#single-image-inference | #single-image-inference | .md | 297_2 |
LLaVa-OneVision can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). For that you have to use checkpoints with an "ov" suffix. Here is how you can do it:
```python
import requests
from PIL import Image
import torch
from transfo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#multi-image-inference | #multi-image-inference | .md | 297_3 |
LLaVa-OneVision also can perform inference with videos as input, where video frames are treated as multiple images. Here is how you can do it:
```python
import av
import numpy as np
from huggingface_hub import hf_hub_download
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#video-inference | #video-inference | .md | 297_4 |
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and make sure to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is bein... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#quantization-using-bitsandbytes | #quantization-using-bitsandbytes | .md | 297_5 |
First make sure to install flash-attn. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with:
```python
from transformers import LlavaOnevisionForConditionalGeneration
model = LlavaOnevisionForCond... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#use-flash-attention-2-to-further-speed-up-generation | #use-flash-attention-2-to-further-speed-up-generation | .md | 297_6 |
This is the configuration class to store the configuration of a [`LlavaOnevisionForConditionalGeneration`]. It is used to instantiate an
Llava-NeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
with the defaults will yield a similar configuration to that of t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#llavaonevisionconfig | #llavaonevisionconfig | .md | 297_7 |
Constructs a LLaVa-Onevision processor which wraps a LLaVa-Onevision video processor, LLaVa-NeXT image processor and a LLaMa tokenizer into a single processor.
[`LlavaNextProcessor`] offers all the functionalities of [`LlavaOnevisionVideoProcessor`], [`LlavaOnevisionImageProcessor`] and [`LlamaTokenizerFast`]. See th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#llavaonevisionprocessor | #llavaonevisionprocessor | .md | 297_8 |
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#llavaonevisionimageprocessor | #llavaonevisionimageprocessor | .md | 297_9 |
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#llavaonevisionvideoprocessor | #llavaonevisionvideoprocessor | .md | 297_10 |
The LLaVA-Onevision model which consists of a vision backbone and a language model.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava_onevision.md | https://huggingface.co/docs/transformers/en/model_doc/llava_onevision/#llavaonevisionforconditionalgeneration | #llavaonevisionforconditionalgeneration | .md | 297_11 |
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