text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started. 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 existing reso... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#resources | #resources | .md | 369_2 |
AutoformerConfig
This is the configuration class to store the configuration of an [`AutoformerModel`]. It is used to instantiate an
Autoformer 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerconfig | #autoformerconfig | .md | 369_3 |
AutoformerModel
The bare Autoformer 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, prunin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformermodel | #autoformermodel | .md | 369_4 |
AutoformerForPrediction
The Autoformer Model with a distribution head on top for time-series forecasting.
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, pruni... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/autoformer.md | https://huggingface.co/docs/transformers/en/model_doc/autoformer/#autoformerforprediction | #autoformerforprediction | .md | 369_5 |
<!--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/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/ | .md | 370_0 | |
The CLIPSeg model was proposed in [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke
and Alexander Ecker. CLIPSeg adds a minimal decoder on top of a frozen [CLIP](clip) model for zero-shot and one-shot image segmentation.
The abstract from the paper is the following: ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#overview | #overview | .md | 370_1 |
- [`CLIPSegForImageSegmentation`] adds a decoder on top of [`CLIPSegModel`]. The latter is identical to [`CLIPModel`].
- [`CLIPSegForImageSegmentation`] can generate image segmentations based on arbitrary prompts at test time. A prompt can be either a text
(provided to the model as `input_ids`) or an image (provided to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#usage-tips | #usage-tips | .md | 370_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with CLIPSeg. 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#resources | #resources | .md | 370_3 |
[`CLIPSegConfig`] is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to
instantiate a CLIPSeg 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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegconfig | #clipsegconfig | .md | 370_4 |
This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
CLIPSeg 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 CLIPSeg
[CIDAS/clipseg-rd6... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextconfig | #clipsegtextconfig | .md | 370_5 |
This is the configuration class to store the configuration of a [`CLIPSegModel`]. It is used to instantiate an
CLIPSeg 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 CLIPSeg
[CIDAS/clipseg-rd6... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionconfig | #clipsegvisionconfig | .md | 370_6 |
Constructs a CLIPSeg processor which wraps a CLIPSeg image processor and a CLIP tokenizer into a single processor.
[`CLIPSegProcessor`] offers all the functionalities of [`ViTImageProcessor`] and [`CLIPTokenizerFast`]. See the
[`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information.
Arg... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegprocessor | #clipsegprocessor | .md | 370_7 |
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`CLIPSegConfig`]): Model configuration class with all the pa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegmodel | #clipsegmodel | .md | 370_8 |
No docstring available for CLIPSegTextModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegtextmodel | #clipsegtextmodel | .md | 370_9 |
No docstring available for CLIPSegVisionModel
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegvisionmodel | #clipsegvisionmodel | .md | 370_10 |
CLIPSeg model with a Transformer-based decoder on top for zero-shot and one-shot image segmentation.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/clipseg.md | https://huggingface.co/docs/transformers/en/model_doc/clipseg/#clipsegforimagesegmentation | #clipsegforimagesegmentation | .md | 370_11 |
<!--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/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/ | .md | 371_0 | |
The Conditional DETR model was proposed in [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Cond... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#overview | #overview | .md | 371_1 |
- Scripts for finetuning [`ConditionalDetrForObjectDetection`] with [`Trainer`] or [Accelerate](https://huggingface.co/docs/accelerate/index) can be found [here](https://github.com/huggingface/transformers/tree/main/examples/pytorch/object-detection).
- See also: [Object detection task guide](../tasks/object_detection)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#resources | #resources | .md | 371_2 |
This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate
a Conditional DETR 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 Conditiona... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrconfig | #conditionaldetrconfig | .md | 371_3 |
Constructs a Conditional Detr image processor.
Args:
format (`str`, *optional*, defaults to `"coco_detection"`):
Data format of the annotations. One of "coco_detection" or "coco_panoptic".
do_resize (`bool`, *optional*, defaults to `True`):
Controls whether to resize the image's (height, width) dimensions to the spec... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrimageprocessor | #conditionaldetrimageprocessor | .md | 371_4 |
No docstring available for ConditionalDetrFeatureExtractor
Methods: __call__
- post_process_object_detection
- post_process_instance_segmentation
- post_process_semantic_segmentation
- post_process_panoptic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrfeatureextractor | #conditionaldetrfeatureextractor | .md | 371_5 |
The bare Conditional DETR Model (consisting of a backbone and encoder-decoder 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 downloadi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrmodel | #conditionaldetrmodel | .md | 371_6 |
CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on
top, for tasks such as COCO detection.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrforobjectdetection | #conditionaldetrforobjectdetection | .md | 371_7 |
CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top,
for tasks such as COCO panoptic.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/conditional_detr.md | https://huggingface.co/docs/transformers/en/model_doc/conditional_detr/#conditionaldetrforsegmentation | #conditionaldetrforsegmentation | .md | 371_8 |
<!--Copyright 2021 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/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/ | .md | 372_0 | |
The VisualBERT model was proposed in [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
VisualBERT is a neural network trained on a variety of (image, text) pairs.
The abstract from the pape... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#overview | #overview | .md | 372_1 |
1. Most of the checkpoints provided work with the [`VisualBertForPreTraining`] configuration. Other
checkpoints provided are the fine-tuned checkpoints for down-stream tasks - VQA ('visualbert-vqa'), VCR
('visualbert-vcr'), NLVR2 ('visualbert-nlvr2'). Hence, if you are not working on these downstream tasks, it is
recom... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#usage-tips | #usage-tips | .md | 372_2 |
This is the configuration class to store the configuration of a [`VisualBertModel`]. It is used to instantiate an
VisualBERT 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 VisualBERT
[uclanlp/... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertconfig | #visualbertconfig | .md | 372_3 |
The bare VisualBert 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 he... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertmodel | #visualbertmodel | .md | 372_4 |
VisualBert Model with two heads on top as done during the pretraining: a `masked language modeling` head and a
`sentence-image prediction (classification)` head.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as d... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertforpretraining | #visualbertforpretraining | .md | 372_5 |
VisualBert Model with a classification/regression head on top (a dropout and a linear layer on top of the pooled
output) for VQA.
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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertforquestionanswering | #visualbertforquestionanswering | .md | 372_6 |
VisualBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for VCR tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or sa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertformultiplechoice | #visualbertformultiplechoice | .md | 372_7 |
VisualBert Model with a sequence classification head on top (a dropout and a linear layer on top of the pooled
output) for Visual Reasoning e.g. for NLVR task.
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/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertforvisualreasoning | #visualbertforvisualreasoning | .md | 372_8 |
VisualBert Model with a Masked Language Modeling head and an attention layer on top for Region-to-Phrase Alignment
e.g. for Flickr30 Entities task.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/visual_bert.md | https://huggingface.co/docs/transformers/en/model_doc/visual_bert/#visualbertforregiontophrasealignment | #visualbertforregiontophrasealignment | .md | 372_9 |
<!--Copyright 2021 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/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/ | .md | 373_0 | |
The BigBird model was proposed in [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by
Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon,
Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others. Big... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#overview | #overview | .md | 373_1 |
- For an in-detail explanation on how BigBird's attention works, see [this blog post](https://huggingface.co/blog/big-bird).
- BigBird comes with 2 implementations: **original_full** & **block_sparse**. For the sequence length < 1024, using
**original_full** is advised as there is no benefit in using **block_sparse** a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#usage-tips | #usage-tips | .md | 373_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Question answering task guide](../tasks/question_answering)
- [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/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#resources | #resources | .md | 373_3 |
This is the configuration class to store the configuration of a [`BigBirdPegasusModel`]. It is used to instantiate
an BigBirdPegasus 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 BigBirdPegas... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusconfig | #bigbirdpegasusconfig | .md | 373_4 |
The bare BigBirdPegasus 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 etc.)
This model ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusmodel | #bigbirdpegasusmodel | .md | 373_5 |
The BigBirdPegasus Model with a language modeling head. Can be used for summarization.
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 etc.)
This model is als... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusforconditionalgeneration | #bigbirdpegasusforconditionalgeneration | .md | 373_6 |
BigBirdPegasus model with a sequence classification/head on top (a linear layer on top of the pooled output) e.g.
for GLUE tasks.
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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusforsequenceclassification | #bigbirdpegasusforsequenceclassification | .md | 373_7 |
BigBirdPegasus Model 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 generic meth... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusforquestionanswering | #bigbirdpegasusforquestionanswering | .md | 373_8 |
No docstring available for BigBirdPegasusForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/bigbird_pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/bigbird_pegasus/#bigbirdpegasusforcausallm | #bigbirdpegasusforcausallm | .md | 373_9 |
<!--Copyright 2023 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/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/ | .md | 374_0 | |
The EfficientNet model was proposed in [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
by Mingxing Tan and Quoc V. Le. EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | #overview | .md | 374_1 |
This is the configuration class to store the configuration of a [`EfficientNetModel`]. It is used to instantiate an
EfficientNet 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 EfficientNet
[go... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | #efficientnetconfig | .md | 374_2 |
Constructs a EfficientNet 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 `preprocess`.
size (`Dict[str, int]` *optional*, defaults to `{"height": 346, "width": 346}`):
Size... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | #efficientnetimageprocessor | .md | 374_3 |
The bare EfficientNet model outputting raw features without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetmodel | #efficientnetmodel | .md | 374_4 |
EfficientNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g.
for ImageNet.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetforimageclassification | #efficientnetforimageclassification | .md | 374_5 |
<!--Copyright 2023 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/flan-ul2.md | https://huggingface.co/docs/transformers/en/model_doc/flan-ul2/ | .md | 375_0 | |
Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](ul2) model released earlier last year.
It was fine tuned using the "Flan" prompt tuning and dataset collection. Similar to `Flan-T5`, one can directly use FLAN-UL2 weights without finetuning the model:
Acco... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flan-ul2.md | https://huggingface.co/docs/transformers/en/model_doc/flan-ul2/#overview | #overview | .md | 375_1 |
The model is pretty heavy (~40GB in half precision) so if you just want to run the model, make sure you load your model in 8bit, and use `device_map="auto"` to make sure you don't have any OOM issue!
```python
>>> from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
>>> model = AutoModelForSeq2SeqLM.from_p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flan-ul2.md | https://huggingface.co/docs/transformers/en/model_doc/flan-ul2/#running-on-low-resource-devices | #running-on-low-resource-devices | .md | 375_2 |
<!--Copyright 2023 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/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/ | .md | 376_0 | |
The Nougat model was proposed in [Nougat: Neural Optical Understanding for Academic Documents](https://arxiv.org/abs/2308.13418) by
Lukas Blecher, Guillem Cucurull, Thomas Scialom, Robert Stojnic. Nougat uses the same architecture as [Donut](donut), meaning an image Transformer
encoder and an autoregressive text Transf... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#overview | #overview | .md | 376_1 |
- The quickest way to get started with Nougat is by checking the [tutorial
notebooks](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Nougat), which show how to use the model
at inference time as well as fine-tuning on custom data.
- Nougat is always used within the [VisionEncoderDecoder](vision-encode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#usage-tips | #usage-tips | .md | 376_2 |
Nougat's [`VisionEncoderDecoder`] model accepts images as input and makes use of
[`~generation.GenerationMixin.generate`] to autoregressively generate text given the input image.
The [`NougatImageProcessor`] class is responsible for preprocessing the input image and
[`NougatTokenizerFast`] decodes the generated targe... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | #inference | .md | 376_3 |
Constructs a Nougat image processor.
Args:
do_crop_margin (`bool`, *optional*, defaults to `True`):
Whether to crop the image margins.
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 `preproce... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | #nougatimageprocessor | .md | 376_4 |
Fast tokenizer for Nougat (backed by HuggingFace tokenizers library).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. This class mainly adds Nougat-specific
methods for postprocessing ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | #nougattokenizerfast | .md | 376_5 |
Constructs a Nougat processor which wraps a Nougat image processor and a Nougat tokenizer into a single processor.
[`NougatProcessor`] offers all the functionalities of [`NougatImageProcessor`] and [`NougatTokenizerFast`]. See the
[`~NougatProcessor.__call__`] and [`~NougatProcessor.decode`] for more information.
A... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatprocessor | #nougatprocessor | .md | 376_6 |
<!--Copyright 2023 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.md | https://huggingface.co/docs/transformers/en/model_doc/llava/ | .md | 377_0 | |
LLaVa is an open-source chatbot trained by fine-tuning LlamA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. In other words, it is an multi-modal version of LLMs fine-tuned for chat / instructions.
The LLaVa model was propo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | #overview | .md | 377_1 |
- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to call `processor.tokenizer.padding_side = "left"` before generating.
- Note the model has not been explicitly trained to process multiple images in the same prompt, although this i... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#usage-tips | #usage-tips | .md | 377_2 |
For best results, we recommend users to use the processor's `apply_chat_template()` method to format your prompt correctly. For that you need to construct a conversation history, passing in a plain string will not format your prompt. Each message in the conversation history for chat templates is a dictionary with keys ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#single-image-inference | #single-image-inference | .md | 377_3 |
LLaVa also supports batched inference. Here is how you can do it:
```python
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration
# Load the model in half-precision
model = LlavaForConditionalGeneration.from_pretrained("llava-hf/llava-1.5-7b-hf", tor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#batched-inference | #batched-inference | .md | 377_4 |
Flash Attention 2 is an even faster, optimized version of the previous optimization, please refer to the [Flash Attention 2 section of performance docs](https://huggingface.co/docs/transformers/perf_infer_gpu_one). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#using-flash-attention-2 | #using-flash-attention-2 | .md | 377_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with BEiT.
<PipelineTag pipeline="image-to-text"/>
- A [Google Colab demo](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) on how to run Llava on a free-tier Google colab instance... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#resources | #resources | .md | 377_6 |
This is the configuration class to store the configuration of a [`LlavaForConditionalGeneration`]. It is used to instantiate an
Llava 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 Llava-9B. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#llavaconfig | #llavaconfig | .md | 377_7 |
Constructs a Llava processor which wraps a Llava image processor and a Llava tokenizer into a single processor.
[`LlavaProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the
[`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information.
Args:
imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#llavaprocessor | #llavaprocessor | .md | 377_8 |
The LLAVA 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 model is a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#llavaforconditionalgeneration | #llavaforconditionalgeneration | .md | 377_9 |
<!--Copyright 2021 NVIDIA Corporation and 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 b... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron_gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/megatron_gpt2/ | .md | 378_0 | |
The MegatronGPT2 model was proposed in [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model
Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley,
Jared Casper and Bryan Catanzaro.
The abstract from the paper is the following:
*Recent... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron_gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/megatron_gpt2/#overview | #overview | .md | 378_1 |
We have provided pretrained [GPT2-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints
for use to evaluate or finetuning downstream tasks.
To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and setup the NVIDIA GPU Cloud (NGC)
Registry CLI. Further documentatio... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/megatron_gpt2.md | https://huggingface.co/docs/transformers/en/model_doc/megatron_gpt2/#usage-tips | #usage-tips | .md | 378_2 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Copyright (c) 2024, NVIDIA CORPORATION. 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/l... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/ | .md | 379_0 | |
The use of this model is governed by the [NVIDIA AI Foundation Models Community License Agreement](https://developer.nvidia.com/downloads/nv-ai-foundation-models-license). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#license | #license | .md | 379_1 |
Nemotron-4 is a family of enterprise ready generative text models compatible with [NVIDIA NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/).
NVIDIA NeMo is an end-to-end, cloud-native platform to build, customize, and deploy generative AI models anywhere. It includes training... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#description | #description | .md | 379_2 |
[Announcement Blog](https://developer.nvidia.com/blog/nvidia-ai-foundation-models-build-custom-enterprise-chatbots-and-co-pilots-with-production-ready-llms/) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#references | #references | .md | 379_3 |
**Architecture Type:** Transformer
**Network Architecture:** Transformer Decoder (auto-regressive language model). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#model-architecture | #model-architecture | .md | 379_4 |
Minitron is a family of small language models (SLMs) obtained by pruning NVIDIA's [Nemotron-4 15B](https://arxiv.org/abs/2402.16819) model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models.
De... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#minitron-4b-base | #minitron-4b-base | .md | 379_5 |
The following code provides an example of how to load the Minitron-4B model and use it to perform text generation.
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Load the tokenizer and model
model_path = 'nvidia/Minitron-4B-Base'
tokenizer = AutoTokenizer.from_pretrained(model... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#huggingface-quickstart | #huggingface-quickstart | .md | 379_6 |
Minitron is released under the [NVIDIA Open Model License Agreement](https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#license | #license | .md | 379_7 |
*5-shot performance.* Language Understanding evaluated using [Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300):
| Average |
| :---- |
| 58.6 |
*Zero-shot performance.* Evaluated using select datasets from the [LM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) wit... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#evaluation-results | #evaluation-results | .md | 379_8 |
If you find our work helpful, please consider citing our paper:
```
@article{minitron2024,
title={Compact Language Models via Pruning and Knowledge Distillation},
author={Saurav Muralidharan and Sharath Turuvekere Sreenivas and Raviraj Joshi and Marcin Chochowski and Mostofa Patwary and Mohammad Shoeybi and Bryan Catan... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#citation | #citation | .md | 379_9 |
This is the configuration class to store the configuration of a [`NemotronModel`]. It is used to instantiate an Nemotron
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 Nemotron-8B.
e.g. [nvidi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronconfig | #nemotronconfig | .md | 379_10 |
The bare Nemotron 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.)
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronmodel | #nemotronmodel | .md | 379_11 |
No docstring available for NemotronForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronforcausallm | #nemotronforcausallm | .md | 379_12 |
The Nemotron Model transformer with a sequence classification head on top (linear layer).
[`NemotronForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronforsequenceclassification | #nemotronforsequenceclassification | .md | 379_13 |
The Nemotron 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 ge... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronforquestionanswering | #nemotronforquestionanswering | .md | 379_14 |
The Nemotron Model transformer 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 (s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nemotron.md | https://huggingface.co/docs/transformers/en/model_doc/nemotron/#nemotronfortokenclassification | #nemotronfortokenclassification | .md | 379_15 |
<!--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/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/ | .md | 380_0 | |
The OPT model was proposed in [Open Pre-trained Transformer Language Models](https://arxiv.org/pdf/2205.01068) by Meta AI.
OPT is a series of open-sourced large causal language models which perform similar in performance to GPT3.
The abstract from the paper is the following:
*Large language models, which are often ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#overview | #overview | .md | 380_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OPT. If you're
interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an ex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#resources | #resources | .md | 380_2 |
First, make sure to install the latest version of Flash Attention 2 to include the sliding window attention feature.
```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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#combining-opt-and-flash-attention-2 | #combining-opt-and-flash-attention-2 | .md | 380_3 |
Below is an expected speedup diagram that compares pure inference time between the native implementation in transformers using `facebook/opt-2.7b` checkpoint and the Flash Attention 2 version of the model using two different sequence lengths.
<div style="text-align: center">
<img src="https://user-images.githubuserco... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#expected-speedups | #expected-speedups | .md | 380_4 |
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/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 380_5 |
| 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/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#training | #training | .md | 380_6 |
| batch_size | seq_len | Per token latency eager (ms) | Per token latency SDPA (ms) | Speedup (%) | Mem eager (MB) | Mem BT (MB) | Mem saved (%) |
|--------------:|-----------:|--------------------------------:|-------------------------------:|---------------:|------------------:|---------------... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#inference | #inference | .md | 380_7 |
This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT 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 OPT
[facebook/opt-350m](https://hug... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#optconfig | #optconfig | .md | 380_8 |
The bare OPT 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 mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#optmodel | #optmodel | .md | 380_9 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.