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<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/ | .md | 409_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=distilbert">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-distilbert-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/distilbert-base-uncased">
<img alt="Spaces" src="https://img.shields.io/bad... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbert | #distilbert | .md | 409_1 |
The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#overview | #overview | .md | 409_2 |
- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
necessary ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#usage-tips | #usage-tips | .md | 409_3 |
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/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 409_4 |
| num_training_steps | batch_size | seq_len | is cuda | 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/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#training | #training | .md | 409_5 |
| num_batches | batch_size | seq_len | is cuda | is half | use mask | 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/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#inference | #inference | .md | 409_6 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DistilBERT. 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#resources | #resources | .md | 409_7 |
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/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#combining-distilbert-and-flash-attention-2 | #combining-distilbert-and-flash-attention-2 | .md | 409_8 |
This is the configuration class to store the configuration of a [`DistilBertModel`] or a [`TFDistilBertModel`]. It
is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertconfig | #distilbertconfig | .md | 409_9 |
Construct a DistilBERT tokenizer. Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
File containing the vocabulary.
do_lower_case (`bool`, *op... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilberttokenizer | #distilberttokenizer | .md | 409_10 |
Construct a "fast" DistilBERT tokenizer (backed by HuggingFace's *tokenizers* library). Based on WordPiece.
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.
Args:
vocab_file (`str`):
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilberttokenizerfast | #distilberttokenizerfast | .md | 409_11 |
The bare DistilBERT encoder/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, prunin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertmodel | #distilbertmodel | .md | 409_12 |
DistilBert Model with a `masked language modeling` 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 model is also a Py... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertformaskedlm | #distilbertformaskedlm | .md | 409_13 |
DistilBert Model transformer with a sequence classification/regression 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertforsequenceclassification | #distilbertforsequenceclassification | .md | 409_14 |
DistilBert Model with a multiple choice classification head on top (a linear layer on top of the pooled output and
a softmax) e.g. for RocStories/SWAG tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downl... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertformultiplechoice | #distilbertformultiplechoice | .md | 409_15 |
DistilBert Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.
for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertfortokenclassification | #distilbertfortokenclassification | .md | 409_16 |
DistilBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a
linear layers 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 methods... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#distilbertforquestionanswering | #distilbertforquestionanswering | .md | 409_17 |
No docstring available for TFDistilBertModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertmodel | #tfdistilbertmodel | .md | 409_18 |
No docstring available for TFDistilBertForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertformaskedlm | #tfdistilbertformaskedlm | .md | 409_19 |
No docstring available for TFDistilBertForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertforsequenceclassification | #tfdistilbertforsequenceclassification | .md | 409_20 |
No docstring available for TFDistilBertForMultipleChoice
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertformultiplechoice | #tfdistilbertformultiplechoice | .md | 409_21 |
No docstring available for TFDistilBertForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertfortokenclassification | #tfdistilbertfortokenclassification | .md | 409_22 |
No docstring available for TFDistilBertForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#tfdistilbertforquestionanswering | #tfdistilbertforquestionanswering | .md | 409_23 |
No docstring available for FlaxDistilBertModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertmodel | #flaxdistilbertmodel | .md | 409_24 |
No docstring available for FlaxDistilBertForMaskedLM
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertformaskedlm | #flaxdistilbertformaskedlm | .md | 409_25 |
No docstring available for FlaxDistilBertForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertforsequenceclassification | #flaxdistilbertforsequenceclassification | .md | 409_26 |
No docstring available for FlaxDistilBertForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertformultiplechoice | #flaxdistilbertformultiplechoice | .md | 409_27 |
No docstring available for FlaxDistilBertForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertfortokenclassification | #flaxdistilbertfortokenclassification | .md | 409_28 |
No docstring available for FlaxDistilBertForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/distilbert.md | https://huggingface.co/docs/transformers/en/model_doc/distilbert/#flaxdistilbertforquestionanswering | #flaxdistilbertforquestionanswering | .md | 409_29 |
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/ | .md | 410_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=openai-gpt">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-openai--gpt-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/openai-gpt">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A4... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openai-gpt | #openai-gpt | .md | 410_1 |
OpenAI GPT model was proposed in [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf)
by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever. It's a causal (unidirectional) tra... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#overview | #overview | .md | 410_2 |
- GPT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right rather than
the left.
- GPT was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
token in a sequence. Leveraging this feature allows GPT-2 to generate syntact... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#usage-tips | #usage-tips | .md | 410_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with OpenAI GPT. 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#resources | #resources | .md | 410_4 |
This is the configuration class to store the configuration of a [`OpenAIGPTModel`] or a [`TFOpenAIGPTModel`]. It is
used to instantiate a GPT model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of the GPT... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigptconfig | #openaigptconfig | .md | 410_5 |
Construct a GPT Tokenizer. Based on Byte-Pair-Encoding with the following peculiarities:
- lowercases all inputs,
- uses `SpaCy` tokenizer and `ftfy` for pre-BPE tokenization if they are installed, fallback to BERT's
`BasicTokenizer` if not.
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most o... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigpttokenizer | #openaigpttokenizer | .md | 410_6 |
Construct a "fast" GPT Tokenizer (backed by HuggingFace's *tokenizers* library). Based on Byte-Pair-Encoding with
the following peculiarities:
- lower case all inputs
- uses BERT's BasicTokenizer for pre-BPE tokenization
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main method... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigpttokenizerfast | #openaigpttokenizerfast | .md | 410_7 |
models.openai.modeling_openai.OpenAIGPTDoubleHeadsModelOutput
Base class for outputs of models predicting if two sentences are consecutive or not.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss.
mc_loss (`torch.FloatTensor` of shape `(1,)`, *... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openai-specific-outputs | #openai-specific-outputs | .md | 410_8 |
The bare OpenAI GPT transformer 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigptmodel | #openaigptmodel | .md | 410_9 |
OpenAI GPT Model transformer with a language modeling head on top (linear layer with weights tied to the input
embeddings).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the inp... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigptlmheadmodel | #openaigptlmheadmodel | .md | 410_10 |
OpenAI GPT Model transformer with a language modeling and a multiple-choice classification head on top e.g. for
RocStories/SWAG tasks. The two heads are two linear layers. The language modeling head has its weights tied to the
input embeddings, the classification head takes as input the input of a specified classificat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigptdoubleheadsmodel | #openaigptdoubleheadsmodel | .md | 410_11 |
The Original OpenAI GPT Model transformer with a sequence classification head on top (linear layer).
[`OpenAIGPTForSequenceClassification`] 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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#openaigptforsequenceclassification | #openaigptforsequenceclassification | .md | 410_12 |
No docstring available for TFOpenAIGPTModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#tfopenaigptmodel | #tfopenaigptmodel | .md | 410_13 |
No docstring available for TFOpenAIGPTLMHeadModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#tfopenaigptlmheadmodel | #tfopenaigptlmheadmodel | .md | 410_14 |
No docstring available for TFOpenAIGPTDoubleHeadsModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#tfopenaigptdoubleheadsmodel | #tfopenaigptdoubleheadsmodel | .md | 410_15 |
No docstring available for TFOpenAIGPTForSequenceClassification
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/openai-gpt.md | https://huggingface.co/docs/transformers/en/model_doc/openai-gpt/#tfopenaigptforsequenceclassification | #tfopenaigptforsequenceclassification | .md | 410_16 |
<!--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/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/ | .md | 411_0 | |
The LeViT model was proposed in [LeViT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze. LeViT improves the [Vision Transformer (ViT)](vit) in performance and efficiency by a few ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#overview | #overview | .md | 411_1 |
- Compared to ViT, LeViT models use an additional distillation head to effectively learn from a teacher (which, in the LeViT paper, is a ResNet like-model). The distillation head is learned through backpropagation under supervision of a ResNet like-model. They also draw inspiration from convolution neural networks to u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#usage-tips | #usage-tips | .md | 411_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LeViT.
<PipelineTag pipeline="image-classification"/>
- [`LevitForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classifica... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#resources | #resources | .md | 411_3 |
This is the configuration class to store the configuration of a [`LevitModel`]. It is used to instantiate a LeViT
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 LeViT
[facebook/levit-128S](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitconfig | #levitconfig | .md | 411_4 |
No docstring available for LevitFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitfeatureextractor | #levitfeatureextractor | .md | 411_5 |
Constructs a LeViT image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Wwhether to resize the shortest edge of the input to int(256/224 *`size`). Can be overridden by the
`do_resize` parameter in the `preprocess` method.
size (`Dict[str, int]`, *optional*, defaults to `{"shortest_edge": 224}`):... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitimageprocessor | #levitimageprocessor | .md | 411_6 |
The bare Levit 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
behavior.
Pa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitmodel | #levitmodel | .md | 411_7 |
Levit 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 matter ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitforimageclassification | #levitforimageclassification | .md | 411_8 |
LeViT Model transformer with image classification heads on top (a linear layer on top of the final hidden state and
a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet. .. warning::
This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not ye... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/levit.md | https://huggingface.co/docs/transformers/en/model_doc/levit/#levitforimageclassificationwithteacher | #levitforimageclassificationwithteacher | .md | 411_9 |
<!--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/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/ | .md | 412_0 | |
The MobileNet model was proposed in [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
The abstract from the paper is the following:
*In this paper we describe a new mobile architecture, MobileN... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#overview | #overview | .md | 412_1 |
- The checkpoints are named **mobilenet\_v2\_*depth*\_*size***, for example **mobilenet\_v2\_1.0\_224**, where **1.0** is the depth multiplier (sometimes also referred to as "alpha" or the width multiplier) and **224** is the resolution of the input images the model was trained on.
- Even though the checkpoint is tra... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#usage-tips | #usage-tips | .md | 412_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with MobileNetV2.
<PipelineTag pipeline="image-classification"/>
- [`MobileNetV2ForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/imag... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#resources | #resources | .md | 412_3 |
This is the configuration class to store the configuration of a [`MobileNetV2Model`]. It is used to instantiate a
MobileNetV2 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 MobileNetV2
[google... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2config | #mobilenetv2config | .md | 412_4 |
No docstring available for MobileNetV2FeatureExtractor
Methods: preprocess
- post_process_semantic_segmentation | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2featureextractor | #mobilenetv2featureextractor | .md | 412_5 |
Constructs a MobileNetV2 image processor.
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 256}`):
S... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2imageprocessor | #mobilenetv2imageprocessor | .md | 412_6 |
The bare MobileNetV2 model outputting raw hidden-states 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
beh... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2model | #mobilenetv2model | .md | 412_7 |
MobileNetV2 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 m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2forimageclassification | #mobilenetv2forimageclassification | .md | 412_8 |
MobileNetV2 model with a semantic segmentation head on top, e.g. for Pascal VOC.
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. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mobilenet_v2.md | https://huggingface.co/docs/transformers/en/model_doc/mobilenet_v2/#mobilenetv2forsemanticsegmentation | #mobilenetv2forsemanticsegmentation | .md | 412_9 |
<!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/ | .md | 413_0 | |
The MusicGen Melody model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi and Alexandre Défossez.
MusicGen Melody is a single stage auto-regressive Transformer model capable of generat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#overview | #overview | .md | 413_1 |
There are two key differences with MusicGen:
1. The audio prompt is used here as a conditional signal for the generated audio sample, whereas it's used for audio continuation in [MusicGen](https://huggingface.co/docs/transformers/main/en/model_doc/musicgen).
2. Conditional text and audio signals are concatenated to the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#difference-with-musicgenhttpshuggingfacecodocstransformersmainenmodeldocmusicgen | #difference-with-musicgenhttpshuggingfacecodocstransformersmainenmodeldocmusicgen | .md | 413_2 |
MusicGen Melody is compatible with two generation modes: greedy and sampling. In practice, sampling leads to significantly better results than greedy, thus we encourage sampling mode to be used where possible. Sampling is enabled by default, and can be explicitly specified by setting `do_sample=True` in the call to [`M... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#generation | #generation | .md | 413_3 |
The model can generate an audio sample conditioned on a text and an audio prompt through use of the [`MusicgenMelodyProcessor`] to pre-process the inputs.
In the following examples, we load an audio file using the 🤗 Datasets library, which can be pip installed through the command below:
```
pip install --upgrade p... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#audio-conditional-generation | #audio-conditional-generation | .md | 413_4 |
The same [`MusicgenMelodyProcessor`] can be used to pre-process a text-only prompt.
```python
>>> from transformers import AutoProcessor, MusicgenMelodyForConditionalGeneration
>>> processor = AutoProcessor.from_pretrained("facebook/musicgen-melody")
>>> model = MusicgenMelodyForConditionalGeneration.from_pretrained... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#text-only-conditional-generation | #text-only-conditional-generation | .md | 413_5 |
The inputs for unconditional (or 'null') generation can be obtained through the method [`MusicgenMelodyProcessor.get_unconditional_inputs`]:
```python
>>> from transformers import MusicgenMelodyForConditionalGeneration, MusicgenMelodyProcessor
>>> model = MusicgenMelodyForConditionalGeneration.from_pretrained("faceb... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#unconditional-generation | #unconditional-generation | .md | 413_6 |
The default parameters that control the generation process, such as sampling, guidance scale and number of generated tokens, can be found in the model's generation config, and updated as desired:
```python
>>> from transformers import MusicgenMelodyForConditionalGeneration
>>> model = MusicgenMelodyForConditionalGen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#generation-configuration | #generation-configuration | .md | 413_7 |
The MusicGen model can be de-composed into three distinct stages:
1. Text encoder: maps the text inputs to a sequence of hidden-state representations. The pre-trained MusicGen models use a frozen text encoder from either T5 or Flan-T5.
2. MusicGen Melody decoder: a language model (LM) that auto-regressively generates a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#model-structure | #model-structure | .md | 413_8 |
- After downloading the original checkpoints from [here](https://github.com/facebookresearch/audiocraft/blob/main/docs/MUSICGEN.md#importing--exporting-models), you can convert them using the **conversion script** available at `src/transformers/models/musicgen_melody/convert_musicgen_melody_transformers.py` with the fo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#checkpoint-conversion | #checkpoint-conversion | .md | 413_9 |
This is the configuration class to store the configuration of an [`MusicgenMelodyDecoder`]. It is used to instantiate a
Musicgen Melody decoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Musicge... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodydecoderconfig | #musicgenmelodydecoderconfig | .md | 413_10 |
Constructs a MusicGen Melody processor which wraps a Wav2Vec2 feature extractor - for raw audio waveform processing - and a T5 tokenizer into a single processor
class.
[`MusicgenProcessor`] offers all the functionalities of [`MusicgenMelodyFeatureExtractor`] and [`T5Tokenizer`]. See
[`~MusicgenProcessor.__call__`] an... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodyprocessor | #musicgenmelodyprocessor | .md | 413_11 |
Constructs a MusicgenMelody feature extractor.
This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
most of the main methods. Users should refer to this superclass for more information regarding those methods.
This class extracts chroma features from au... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodyfeatureextractor | #musicgenmelodyfeatureextractor | .md | 413_12 |
This is the configuration class to store the configuration of a [`MusicgenMelodyModel`]. It is used to instantiate a
Musicgen Melody model according to the specified arguments, defining the text encoder, audio encoder and Musicgen Melody decoder
configs. Instantiating a configuration with the defaults will yield a simi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodyconfig | #musicgenmelodyconfig | .md | 413_13 |
The bare MusicgenMelody decoder model outputting raw hidden-states without any specific head on top.
The Musicgen Melody model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodymodel | #musicgenmelodymodel | .md | 413_14 |
The Musicgen Melody decoder model with a language modelling head on top.
The Musicgen Melody model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez. It is a
decoder-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodyforcausallm | #musicgenmelodyforcausallm | .md | 413_15 |
The composite Musicgen Melody model with a text and audio conditional models, a MusicgenMelody decoder and an audio encoder, for music generation tasks with one or both of text and audio prompts.
The Musicgen Melody model was proposed in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/musicgen_melody.md | https://huggingface.co/docs/transformers/en/model_doc/musicgen_melody/#musicgenmelodyforconditionalgeneration | #musicgenmelodyforconditionalgeneration | .md | 413_16 |
<!--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/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/ | .md | 414_0 | |
The GPT-J model was released in the [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) repository by Ben Wang and Aran Komatsuzaki. It is a GPT-2-like
causal language model trained on [the Pile](https://pile.eleuther.ai/) dataset.
This model was contributed by [Stella Biderman](http... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#overview | #overview | .md | 414_1 |
- To load [GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B) in float32 one would need at least 2x model size
RAM: 1x for initial weights and another 1x to load the checkpoint. So for GPT-J it would take at least 48GB
RAM to just load the model. To reduce the RAM usage there are a few options. The `torch_dtype` argume... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#usage-tips | #usage-tips | .md | 414_2 |
The [`~generation.GenerationMixin.generate`] method can be used to generate text using GPT-J
model.
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#usage-examples | #usage-examples | .md | 414_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GPT-J. 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 ex... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#resources | #resources | .md | 414_4 |
This is the configuration class to store the configuration of a [`GPTJModel`]. It is used to instantiate a GPT-J
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the GPT-J
[EleutherAI/gpt-j-6B](http... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#gptjconfig | #gptjconfig | .md | 414_5 |
The bare GPT-J Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#gptjmodel | #gptjmodel | .md | 414_6 |
The GPT-J Model transformer with a language modeling head on top.
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#gptjforcausallm | #gptjforcausallm | .md | 414_7 |
The GPT-J Model transformer with a sequence classification head on top (linear layer).
[`GPTJForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT, GPT-2, GPT-Neo) do.
Since it does classification on the last token, it requires to know the position of th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#gptjforsequenceclassification | #gptjforsequenceclassification | .md | 414_8 |
The GPT-J Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#gptjforquestionanswering | #gptjforquestionanswering | .md | 414_9 |
No docstring available for TFGPTJModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#tfgptjmodel | #tfgptjmodel | .md | 414_10 |
No docstring available for TFGPTJForCausalLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#tfgptjforcausallm | #tfgptjforcausallm | .md | 414_11 |
No docstring available for TFGPTJForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#tfgptjforsequenceclassification | #tfgptjforsequenceclassification | .md | 414_12 |
No docstring available for TFGPTJForQuestionAnswering
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#tfgptjforquestionanswering | #tfgptjforquestionanswering | .md | 414_13 |
No docstring available for FlaxGPTJModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#flaxgptjmodel | #flaxgptjmodel | .md | 414_14 |
No docstring available for FlaxGPTJForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/gptj.md | https://huggingface.co/docs/transformers/en/model_doc/gptj/#flaxgptjforcausallm | #flaxgptjforcausallm | .md | 414_15 |
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