source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
|---|---|---|---|---|
/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 | .md | This model is also 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 ([`BigBirdPegasusConfig`]):
Model configuration class wit... | 373_5_1 |
/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 | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 373_5_2 |
/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 | .md | 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... | 373_6_0 |
/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 | .md | This model is also 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 ([`BigBirdPegasusConfig`]):
Model configuration class wit... | 373_6_1 |
/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 | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 373_6_2 |
/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 | .md | 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... | 373_7_0 |
/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 | .md | This model is also 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 ([`BigBirdPegasusConfig`]):
Model configuration class wit... | 373_7_1 |
/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 | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 373_7_2 |
/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 | .md | 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... | 373_8_0 |
/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 | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
This model is also 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... | 373_8_1 |
/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 | .md | Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 373_8_2 |
/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 | .md | No docstring available for BigBirdPegasusForCausalLM
Methods: forward | 373_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/ | .md | <!--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... | 374_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 374_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | 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 ... | 374_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | *Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performa... | 374_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet. | 374_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageN... | 374_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.* | 374_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#overview | .md | This model was contributed by [adirik](https://huggingface.co/adirik).
The original code can be found [here](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet). | 374_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | 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... | 374_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | [google/efficientnet-b7](https://huggingface.co/google/efficientnet-b7) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
num_channels (`int`, *optional*, defaults to 3):
Th... | 374_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | The input image size.
width_coefficient (`float`, *optional*, defaults to 2.0):
Scaling coefficient for network width at each stage.
depth_coefficient (`float`, *optional*, defaults to 3.1):
Scaling coefficient for network depth at each stage.
depth_divisor `int`, *optional*, defaults to 8):
A unit of network width.
ke... | 374_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | kernel_sizes (`List[int]`, *optional*, defaults to `[3, 3, 5, 3, 5, 5, 3]`):
List of kernel sizes to be used in each block.
in_channels (`List[int]`, *optional*, defaults to `[32, 16, 24, 40, 80, 112, 192]`):
List of input channel sizes to be used in each block for convolutional layers.
out_channels (`List[int]`, *opti... | 374_2_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | List of output channel sizes to be used in each block for convolutional layers.
depthwise_padding (`List[int]`, *optional*, defaults to `[]`):
List of block indices with square padding.
strides (`List[int]`, *optional*, defaults to `[1, 2, 2, 2, 1, 2, 1]`):
List of stride sizes to be used in each block for convolutiona... | 374_2_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | List of the number of times each block is to repeated.
expand_ratios (`List[int]`, *optional*, defaults to `[1, 6, 6, 6, 6, 6, 6]`):
List of scaling coefficient of each block.
squeeze_expansion_ratio (`float`, *optional*, defaults to 0.25):
Squeeze expansion ratio.
hidden_act (`str` or `function`, *optional*, defaults ... | 374_2_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | `"selu", `"gelu_new"`, `"silu"` and `"mish"` are supported.
hiddem_dim (`int`, *optional*, defaults to 1280):
The hidden dimension of the layer before the classification head.
pooling_type (`str` or `function`, *optional*, defaults to `"mean"`):
Type of final pooling to be applied before the dense classification head. ... | 374_2_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | `"max"`]
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
batch_norm_eps (`float`, *optional*, defaults to 1e-3):
The epsilon used by the batch normalization layers.
batch_norm_momentum (`float`, *optional*, defau... | 374_2_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | dropout_rate (`float`, *optional*, defaults to 0.5):
The dropout rate to be applied before final classifier layer.
drop_connect_rate (`float`, *optional*, defaults to 0.2):
The drop rate for skip connections.
Example:
```python
>>> from transformers import EfficientNetConfig, EfficientNetModel | 374_2_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetconfig | .md | >>> # Initializing a EfficientNet efficientnet-b7 style configuration
>>> configuration = EfficientNetConfig()
>>> # Initializing a model (with random weights) from the efficientnet-b7 style configuration
>>> model = EfficientNetModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.co... | 374_2_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | 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... | 374_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | resample (`PILImageResampling` filter, *optional*, defaults to 0):
Resampling filter to use if resizing the image. Can be overridden by `resample` in `preprocess`.
do_center_crop (`bool`, *optional*, defaults to `False`):
Whether to center crop the image. If the input size is smaller than `crop_size` along any edge, th... | 374_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | is padded with 0's and then center cropped. Can be overridden by `do_center_crop` in `preprocess`.
crop_size (`Dict[str, int]`, *optional*, defaults to `{"height": 289, "width": 289}`):
Desired output size when applying center-cropping. Can be overridden by `crop_size` in `preprocess`.
rescale_factor (`int` or `float`,... | 374_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`preprocess` method.
rescale_offset (`bool`, *optional*, defaults to `False`):
Whether to rescale the image between [-scale_range, scale_range] instead of [0, scale_range]. Can be
overridden by the `rescale_factor` pa... | 374_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | overridden by the `rescale_factor` parameter in the `preprocess` method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
do_normalize (`bool`, *optional*, defaults to `T... | 374_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | Whether to normalize the image. Can be overridden by the `do_normalize` parameter in the `preprocess`
method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image... | 374_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetimageprocessor | .md | image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_STANDARD_STD`):
Standard deviation to use if normalizing the image. This is a float or list of floats the length of the
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
include_top (`bool`, ... | 374_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetmodel | .md | 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... | 374_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetmodel | .md | behavior.
Parameters:
config ([`EfficientNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Met... | 374_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetforimageclassification | .md | 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 ... | 374_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/efficientnet.md | https://huggingface.co/docs/transformers/en/model_doc/efficientnet/#efficientnetforimageclassification | .md | behavior.
Parameters:
config ([`EfficientNetConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Met... | 374_5_1 |
/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 | <!--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... | 375_0_0 |
/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 | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 375_0_1 |
/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 | .md | 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... | 375_1_0 |
/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 | .md | According to the original blog here are the notable improvements:
- The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
- The Flan-UL2 checkpoint uses a receptive field of 2048 which makes it more usable for few-shot in-context learning. | 375_1_1 |
/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 | .md | - The original UL2 model also had mode switch tokens that was rather mandatory to get good performance. However, they were a little cumbersome as this requires often some changes during inference or finetuning. In this update/change, we continue training UL2 20B for an additional 100k steps (with small batch) to forget... | 375_1_2 |
/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 | .md | Google has released the following variants:
The original checkpoints can be found [here](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints). | 375_1_3 |
/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 | .md | 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... | 375_2_0 |
/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 | .md | >>> inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
>>> outputs = model.generate(**inputs)
>>> print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['In a large skillet, brown the ground beef and onion over medium heat. Add the garlic']
```
<Tip>
Refer to [T5's ... | 375_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/ | .md | <!--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... | 376_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/ | .md | "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
specific language governing permissions an... | 376_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#overview | .md | 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... | 376_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#overview | .md | *Scientific knowledge is predominantly stored in books and scientific journals, often in the form of PDFs. However, the PDF format leads to a loss of semantic information, particularly for mathematical expressions. We propose Nougat (Neural Optical Understanding for Academic Documents), a Visual Transformer model that ... | 376_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#overview | .md | scientific documents into a markup language, and demonstrate the effectiveness of our model on a new dataset of scientific documents. The proposed approach offers a promising solution to enhance the accessibility of scientific knowledge in the digital age, by bridging the gap between human-readable documents and machin... | 376_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/nougat_architecture.jpg"
alt="drawing" width="600"/>
<small> Nougat high-level overview. Taken from the <a href="https://arxiv.org/abs/2308.13418">original paper</a>. </small>
This model was contributed by... | 376_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#usage-tips | .md | - 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... | 376_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | 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... | 376_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | [`NougatProcessor`] wraps [`NougatImageProcessor`] and [`NougatTokenizerFast`] classes
into a single instance to both extract the input features and decode the predicted token ids.
- Step-by-step PDF transcription
```py
>>> from huggingface_hub import hf_hub_download
>>> import re
>>> from PIL import Image | 376_3_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | >>> from transformers import NougatProcessor, VisionEncoderDecoderModel
>>> from datasets import load_dataset
>>> import torch
>>> processor = NougatProcessor.from_pretrained("facebook/nougat-base")
>>> model = VisionEncoderDecoderModel.from_pretrained("facebook/nougat-base")
>>> device = "cuda" if torch.cuda.is_avai... | 376_3_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | >>> device = "cuda" if torch.cuda.is_available() else "cpu"
>>> model.to(device) # doctest: +IGNORE_RESULT
>>> # prepare PDF image for the model
>>> filepath = hf_hub_download(repo_id="hf-internal-testing/fixtures_docvqa", filename="nougat_paper.png", repo_type="dataset")
>>> image = Image.open(filepath)
>>> pixel_va... | 376_3_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | >>> # generate transcription (here we only generate 30 tokens)
>>> outputs = model.generate(
... pixel_values.to(device),
... min_length=1,
... max_new_tokens=30,
... bad_words_ids=[[processor.tokenizer.unk_token_id]],
... ) | 376_3_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | >>> sequence = processor.batch_decode(outputs, skip_special_tokens=True)[0]
>>> sequence = processor.post_process_generation(sequence, fix_markdown=False)
>>> # note: we're using repr here such for the sake of printing the \n characters, feel free to just print the sequence
>>> print(repr(sequence))
'\n\n# Nougat: Neur... | 376_3_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#inference | .md | '\n\n# Nougat: Neural Optical Understanding for Academic Documents\n\n Lukas Blecher\n\nCorrespondence to: lblecher@'
```
See the [model hub](https://huggingface.co/models?filter=nougat) to look for Nougat checkpoints.
<Tip>
The model is identical to [Donut](donut) in terms of architecture.
</Tip> | 376_3_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | 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... | 376_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | Size of the image after resizing. Can be overridden by `size` in the `preprocess` method.
resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
Resampling filter to use if resizing the image. Can be overridden by `resample` in the `preprocess` method.
do_thumbnail (`bool`, *optional*, defaults... | 376_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | Whether to resize the image using thumbnail method.
do_align_long_axis (`bool`, *optional*, defaults to `False`):
Whether to align the long axis of the image with the long axis of `size` by rotating by 90 degrees.
do_pad (`bool`, *optional*, defaults to `True`):
Whether to pad the images to the largest image size in th... | 376_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the
`pr... | 376_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | Whether to normalize the image. Can be overridden by `do_normalize` in the `preprocess` method.
image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`):
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overri... | 376_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatimageprocessor | .md | image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`):
Image standard deviation.
Methods: preprocess | 376_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | 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 ... | 376_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer.
tokenizer_file (`str`, *optional*):
[tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
contains everything ... | 376_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | contains everything needed to load the tokenizer.
clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`):
Wether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra
spaces.
unk_token (`str`, *optional*, defaults to `"<unk>"`):
The unknown token. A token that ... | 376_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | token instead.
bos_token (`str`, *optional*, defaults to `"<s>"`):
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str`, *optional*, defaults to `"</s>"`):
The end of sequence token.
pad_token (`str`, *optional*, defaults to `"<pad>"`):
The to... | 376_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | Class attributes (overridden by derived classes)
- **vocab_files_names** (`Dict[str, str]`) -- A dictionary with, as keys, the `__init__` keyword name of each
vocabulary file required by the model, and as associated values, the filename for saving the associated file
(string).
- **pretrained_vocab_files_map** (`Dict[... | 376_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | high-level keys being the `__init__` keyword name of each vocabulary file required by the model, the
low-level being the `short-cut-names` of the pretrained models with, as associated values, the `url` to the
associated pretrained vocabulary file.
- **model_input_names** (`List[str]`) -- A list of inputs expected in th... | 376_5_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | Should be `'right'` or `'left'`.
- **truncation_side** (`str`) -- The default value for the side on which the model should have truncation
applied. Should be `'right'` or `'left'`.
Args:
model_max_length (`int`, *optional*):
The maximum length (in number of tokens) for the inputs to the transformer model. When the to... | 376_5_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | loaded with [`~tokenization_utils_base.PreTrainedTokenizerBase.from_pretrained`], this will be set to the
value stored for the associated model in `max_model_input_sizes` (see above). If no value is provided, will
default to VERY_LARGE_INTEGER (`int(1e30)`).
padding_side (`str`, *optional*):
The side on which the model... | 376_5_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | Default value is picked from the class attribute of the same name.
truncation_side (`str`, *optional*):
The side on which the model should have truncation applied. Should be selected between ['right', 'left'].
Default value is picked from the class attribute of the same name.
chat_template (`str`, *optional*):
A Jinja ... | 376_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | https://huggingface.co/docs/transformers/chat_templating for a full description.
model_input_names (`List[string]`, *optional*):
The list of inputs accepted by the forward pass of the model (like `"token_type_ids"` or
`"attention_mask"`). Default value is picked from the class attribute of the same name.
bos_token (`st... | 376_5_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | A special token representing the beginning of a sentence. Will be associated to `self.bos_token` and
`self.bos_token_id`.
eos_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token representing the end of a sentence. Will be associated to `self.eos_token` and
`self.eos_token_id`.
unk_token (`str` or `tok... | 376_5_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | A special token representing an out-of-vocabulary token. Will be associated to `self.unk_token` and
`self.unk_token_id`.
sep_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token separating two different sentences in the same input (used by BERT for instance). Will be
associated to `self.sep_token` and ... | 376_5_11 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation. Will be associated to `self.pad_token` and `self.pad_token_id`.
cls_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token representing the class of the inpu... | 376_5_12 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | `self.cls_token` and `self.cls_token_id`.
mask_token (`str` or `tokenizers.AddedToken`, *optional*):
A special token representing a masked token (used by masked-language modeling pretraining objectives, like
BERT). Will be associated to `self.mask_token` and `self.mask_token_id`.
additional_special_tokens (tuple or lis... | 376_5_13 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | A tuple or a list of additional special tokens. Add them here to ensure they are skipped when decoding with
`skip_special_tokens` is set to True. If they are not part of the vocabulary, they will be added at the end
of the vocabulary.
clean_up_tokenization_spaces (`bool`, *optional*, defaults to `True`):
Whether or not... | 376_5_14 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | tokenization process.
split_special_tokens (`bool`, *optional*, defaults to `False`):
Whether or not the special tokens should be split during the tokenization process. Passing will affect the
internal state of the tokenizer. The default behavior is to not split special tokens. This means that if
`<s>` is the `bos_toke... | 376_5_15 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougattokenizerfast | .md | `split_special_tokens=True`, then `tokenizer.tokenize("<s>")` will be give `['<','s', '>']`.
tokenizer_object ([`tokenizers.Tokenizer`]):
A [`tokenizers.Tokenizer`] object from 🤗 tokenizers to instantiate from. See [Using tokenizers from 🤗
tokenizers](../fast_tokenizers) for more information.
tokenizer_file ([`str`... | 376_5_16 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatprocessor | .md | 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... | 376_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/nougat.md | https://huggingface.co/docs/transformers/en/model_doc/nougat/#nougatprocessor | .md | An instance of [`NougatImageProcessor`]. The image processor is a required input.
tokenizer ([`NougatTokenizerFast`]):
An instance of [`NougatTokenizerFast`]. The tokenizer is a required input.
Methods: __call__
- from_pretrained
- save_pretrained
- batch_decode
- decode
- post_process_generation | 376_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/ | .md | <!--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... | 377_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 377_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | .md | 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. | 377_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | .md | The LLaVa model was proposed in [Visual Instruction Tuning](https://arxiv.org/abs/2304.08485) and improved in [Improved Baselines with Visual Instruction Tuning](https://arxiv.org/pdf/2310.03744) by Haotian Liu, Chunyuan Li, Yuheng Li and Yong Jae Lee.
The abstract from the paper is the following: | 377_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | .md | *Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this note, we show that the fully-connected vision-language cross-modal connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA, namely, using CLIP-ViT-L-336px with an ML... | 377_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | .md | response formatting prompts, we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data, and finishes full training in ∼1 day on a single 8-A100 node. We hope this can make state-of-the-art LMM research more accessible. Code and ... | 377_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llava_architecture.jpg"
alt="drawing" width="600"/>
<small> LLaVa architecture. Taken from the <a href="https://arxiv.org/abs/2304.08485">original paper.</a> </small>
This model was contributed by [ArthurZ... | 377_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#usage-tips | .md | - 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... | 377_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#usage-tips | .md | > [!NOTE]
> LLaVA models after release v4.46 will raise warnings about adding `processor.patch_size = {{patch_size}}`, `processor.num_additional_image_tokens = {{num_additional_image_tokens}}` and processor.vision_feature_select_strategy = {{vision_feature_select_strategy}}`. It is strongly recommended to add the attri... | 377_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#usage-tips | .md | Adding these attributes means that LLaVA will try to infer the number of image tokens required per image and expand the text with as many `<image>` placeholders as there will be tokens. Usually it is around 500 tokens per image, so make sure that the text is not truncated as otherwise there will be failure when merging... | 377_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/llava.md | https://huggingface.co/docs/transformers/en/model_doc/llava/#usage-tips | .md | The attributes can be obtained from model config, as `model.config.vision_config.patch_size` or `model.config.vision_feature_select_strategy`. The `num_additional_image_tokens` should be `1` if the vision backbone adds a CLS token or `0` if nothing extra is added to the vision patches. | 377_2_3 |
/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 | .md | 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 ... | 377_3_0 |
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