text stringlengths 5 58.6k | source stringclasses 470
values | url stringlengths 49 167 | source_section stringlengths 0 90 | file_type stringclasses 1
value | id stringlengths 3 6 |
|---|---|---|---|---|---|
No docstring available for OPTForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#optforcausallm | #optforcausallm | .md | 380_10 |
The OPT Model transformer with a sequence classification head on top (linear layer).
[`OPTForSequenceClassification`] uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.
Since it does classification on the last token, it requires to know the position of the last token. If ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#optforsequenceclassification | #optforsequenceclassification | .md | 380_11 |
The OPT 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 inherits from [`PreTrainedModel`]. Check the superclass documentation for the generi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#optforquestionanswering | #optforquestionanswering | .md | 380_12 |
No docstring available for TFOPTModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#tfoptmodel | #tfoptmodel | .md | 380_13 |
No docstring available for TFOPTForCausalLM
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#tfoptforcausallm | #tfoptforcausallm | .md | 380_14 |
No docstring available for FlaxOPTModel
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#flaxoptmodel | #flaxoptmodel | .md | 380_15 |
No docstring available for FlaxOPTForCausalLM
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/opt.md | https://huggingface.co/docs/transformers/en/model_doc/opt/#flaxoptforcausallm | #flaxoptforcausallm | .md | 380_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/t5v1.1.md | https://huggingface.co/docs/transformers/en/model_doc/t5v1.1/ | .md | 381_0 | |
T5v1.1 was released in the [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511)
repository by Colin Raffel et al. It's an improved version of the original T5 model.
This model was contributed by [patrickvonplaten... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5v1.1.md | https://huggingface.co/docs/transformers/en/model_doc/t5v1.1/#overview | #overview | .md | 381_1 |
One can directly plug in the weights of T5v1.1 into a T5 model, like so:
```python
>>> from transformers import T5ForConditionalGeneration
>>> model = T5ForConditionalGeneration.from_pretrained("google/t5-v1_1-base")
```
T5 Version 1.1 includes the following improvements compared to the original T5 model:
- GEGL... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/t5v1.1.md | https://huggingface.co/docs/transformers/en/model_doc/t5v1.1/#usage-tips | #usage-tips | .md | 381_2 |
<!--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/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/ | .md | 382_0 | |
The ViTMAE model was proposed in [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377v2) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li,
Piotr Dollár, Ross Girshick. The paper shows that, by pre-training a Vision Transformer (ViT) to reconstruct pixel values for masked patches, one c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#overview | #overview | .md | 382_1 |
- MAE (masked auto encoding) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training objective is relatively simple:
by masking a large portion (75%) of the image patches, the model must reconstruct raw pixel values. One can use [`ViTMAEForPreTraining`] for this purpose.
- After pre... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#usage-tips | #usage-tips | .md | 382_2 |
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/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 382_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViTMAE.
- [`ViTMAEForPreTraining`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining), allowing you to pre-train the model from scratch/fu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#resources | #resources | .md | 382_4 |
This is the configuration class to store the configuration of a [`ViTMAEModel`]. It is used to instantiate an ViT
MAE 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 ViT
[facebook/vit-mae-base]... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#vitmaeconfig | #vitmaeconfig | .md | 382_5 |
The bare ViTMAE 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) subclass. 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/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#vitmaemodel | #vitmaemodel | .md | 382_6 |
The ViTMAE Model transformer with the decoder on top for self-supervised pre-training.
<Tip>
Note that we provide a script to pre-train this model on custom data in our [examples
directory](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining).
</Tip>
This model is a PyTorch ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#vitmaeforpretraining | #vitmaeforpretraining | .md | 382_7 |
No docstring available for TFViTMAEModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#tfvitmaemodel | #tfvitmaemodel | .md | 382_8 |
No docstring available for TFViTMAEForPreTraining
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_mae.md | https://huggingface.co/docs/transformers/en/model_doc/vit_mae/#tfvitmaeforpretraining | #tfvitmaeforpretraining | .md | 382_9 |
<!--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/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/ | .md | 383_0 | |
The I-BERT model was proposed in [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by
Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney and Kurt Keutzer. It's a quantized version of RoBERTa running
inference up to four times faster.
The abstract from the paper is the following:
*Tran... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#overview | #overview | .md | 383_1 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/masked... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#resources | #resources | .md | 383_2 |
This is the configuration class to store the configuration of a [`IBertModel`]. It is used to instantiate a I-BERT
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 IBERT
[kssteven/ibert-roberta-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertconfig | #ibertconfig | .md | 383_3 |
The bare I-BERT Model transformer outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning head... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertmodel | #ibertmodel | .md | 383_4 |
I-BERT Model with a `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 PyTorch [torc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertformaskedlm | #ibertformaskedlm | .md | 383_5 |
I-BERT 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 sav... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertforsequenceclassification | #ibertforsequenceclassification | .md | 383_6 |
I-BERT 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 downloadi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertformultiplechoice | #ibertformultiplechoice | .md | 383_7 |
I-BERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertfortokenclassification | #ibertfortokenclassification | .md | 383_8 |
I-BERT 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 the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ibert.md | https://huggingface.co/docs/transformers/en/model_doc/ibert/#ibertforquestionanswering | #ibertforquestionanswering | .md | 383_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/decision_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/decision_transformer/ | .md | 384_0 | |
The Decision Transformer model was proposed in [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345)
by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
The abstract from the paper is the... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/decision_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/decision_transformer/#overview | #overview | .md | 384_1 |
This is the configuration class to store the configuration of a [`DecisionTransformerModel`]. It is used to
instantiate a Decision Transformer 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 st... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/decision_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/decision_transformer/#decisiontransformerconfig | #decisiontransformerconfig | .md | 384_2 |
No docstring available for DecisionTransformerGPT2Model
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/decision_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/decision_transformer/#decisiontransformergpt2model | #decisiontransformergpt2model | .md | 384_3 |
The Decision Transformer Model
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:
config ([`~DecisionTransformerConfig`... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/decision_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/decision_transformer/#decisiontransformermodel | #decisiontransformermodel | .md | 384_4 |
<!--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/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/ | .md | 385_0 | |
<div class="flex flex-wrap space-x-1">
<a href="https://huggingface.co/models?filter=pegasus">
<img alt="Models" src="https://img.shields.io/badge/All_model_pages-pegasus-blueviolet">
</a>
<a href="https://huggingface.co/spaces/docs-demos/pegasus_paraphrase">
<img alt="Spaces" src="https://img.shields.io/badge/%F0%9F%A... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasus | #pegasus | .md | 385_1 |
The Pegasus model was proposed in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu on Dec 18, 2019.
According to the abstract,
- Pegasus' pretraining task is intentionally similar to ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#overview | #overview | .md | 385_2 |
- Sequence-to-sequence model with the same encoder-decoder model architecture as BART. Pegasus is pre-trained jointly on two self-supervised objective functions: Masked Language Modeling (MLM) and a novel summarization specific pretraining objective, called Gap Sentence Generation (GSG).
* MLM: encoder input tokens a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#usage-tips | #usage-tips | .md | 385_3 |
All the [checkpoints](https://huggingface.co/models?search=pegasus) are fine-tuned for summarization, besides
*pegasus-large*, whence the other checkpoints are fine-tuned:
- Each checkpoint is 2.2 GB on disk and 568M parameters.
- FP16 is not supported (help/ideas on this appreciated!).
- Summarizing xsum in fp32 tak... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#checkpoints | #checkpoints | .md | 385_4 |
- All models are transformer encoder-decoders with 16 layers in each component.
- The implementation is completely inherited from [`BartForConditionalGeneration`]
- Some key configuration differences:
- static, sinusoidal position embeddings
- the model starts generating with pad_token_id (which has 0 token_embedding) ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#implementation-notes | #implementation-notes | .md | 385_5 |
```python
>>> from transformers import PegasusForConditionalGeneration, PegasusTokenizer
>>> import torch
>>> src_text = [
... """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were sched... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#usage-example | #usage-example | .md | 385_6 |
- [Script](https://github.com/huggingface/transformers/tree/main/examples/research_projects/seq2seq-distillation/finetune_pegasus_xsum.sh) to fine-tune pegasus
on the XSUM dataset. Data download instructions at [examples/pytorch/summarization/](https://github.com/huggingface/transformers/tree/main/examples/pytorch/summ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#resources | #resources | .md | 385_7 |
This is the configuration class to store the configuration of a [`PegasusModel`]. It is used to instantiate an
PEGASUS 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 PEGASUS
[google/pegasus-la... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasusconfig | #pegasusconfig | .md | 385_8 |
warning: `add_tokens` does not work at the moment.
Construct a PEGASUS tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
this superclass for more information regarding thos... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasustokenizer | #pegasustokenizer | .md | 385_9 |
Construct a "fast" PEGASUS tokenizer (backed by HuggingFace's *tokenizers* library). Based on
[Unigram](https://huggingface.co/docs/tokenizers/python/latest/components.html?highlight=unigram#models).
This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
refer t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasustokenizerfast | #pegasustokenizerfast | .md | 385_10 |
The bare PEGASUS Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
Thi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasusmodel | #pegasusmodel | .md | 385_11 |
The PEGASUS 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, pruning heads
etc.)
This mode... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasusforconditionalgeneration | #pegasusforconditionalgeneration | .md | 385_12 |
No docstring available for PegasusForCausalLM
Methods: forward
</pt>
<tf> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#pegasusforcausallm | #pegasusforcausallm | .md | 385_13 |
No docstring available for TFPegasusModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#tfpegasusmodel | #tfpegasusmodel | .md | 385_14 |
No docstring available for TFPegasusForConditionalGeneration
Methods: call
</tf>
<jax> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#tfpegasusforconditionalgeneration | #tfpegasusforconditionalgeneration | .md | 385_15 |
No docstring available for FlaxPegasusModel
Methods: __call__
- encode
- decode | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#flaxpegasusmodel | #flaxpegasusmodel | .md | 385_16 |
No docstring available for FlaxPegasusForConditionalGeneration
Methods: __call__
- encode
- decode
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/pegasus.md | https://huggingface.co/docs/transformers/en/model_doc/pegasus/#flaxpegasusforconditionalgeneration | #flaxpegasusforconditionalgeneration | .md | 385_17 |
<!--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/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/ | .md | 386_0 | |
The PoolFormer model was proposed in [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Sea AI Labs. Instead of designing complicated token mixer to achieve SOTA performance, the target of this work is to demonstrate the competence of transformer models largely stem from the general... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#overview | #overview | .md | 386_1 |
- PoolFormer has a hierarchical architecture, where instead of Attention, a simple Average Pooling layer is present. All checkpoints of the model can be found on the [hub](https://huggingface.co/models?other=poolformer).
- One can use [`PoolFormerImageProcessor`] to prepare images for the model.
- As most models, PoolF... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#usage-tips | #usage-tips | .md | 386_2 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with PoolFormer.
<PipelineTag pipeline="image-classification"/>
- [`PoolFormerForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#resources | #resources | .md | 386_3 |
This is the configuration class to store the configuration of [`PoolFormerModel`]. It is used to instantiate a
PoolFormer 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 PoolFormer
[sail/poolfo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#poolformerconfig | #poolformerconfig | .md | 386_4 |
No docstring available for PoolFormerFeatureExtractor
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#poolformerfeatureextractor | #poolformerfeatureextractor | .md | 386_5 |
Constructs a PoolFormer 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": 224}`):
Si... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#poolformerimageprocessor | #poolformerimageprocessor | .md | 386_6 |
The bare PoolFormer 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 u... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#poolformermodel | #poolformermodel | .md | 386_7 |
PoolFormer Model transformer with an image classification 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.
Paramete... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/poolformer.md | https://huggingface.co/docs/transformers/en/model_doc/poolformer/#poolformerforimageclassification | #poolformerforimageclassification | .md | 386_8 |
<!--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/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/ | .md | 387_0 | |
The Chameleon model was proposed in [Chameleon: Mixed-Modal Early-Fusion Foundation Models
](https://arxiv.org/abs/2405.09818v1) by META AI Chameleon Team. Chameleon is a Vision-Language Model that use vector quantization to tokenize images which enables the model to generate multimodal output. The model takes images a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#overview | #overview | .md | 387_1 |
- We advise users to use `padding_side="left"` when computing batched generation as it leads to more accurate results. Simply make sure to set `processor.tokenizer.padding_side = "left"` before generating.
- Note that Chameleon was tuned for safety alignment. If the model is refusing to answer, consider asking a more... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#usage-tips | #usage-tips | .md | 387_2 |
Chameleon is a gated model so make sure to have access and login to Hugging Face Hub using a token.
Here's how to load the model and perform inference in half-precision (`torch.bfloat16`):
```python
from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
import torch
from PIL import Image
impor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#single-image-inference | #single-image-inference | .md | 387_3 |
Chameleon can perform inference with multiple images as input, where images either belong to the same prompt or different prompts (in batched inference). Here is how you can do it:
```python
from transformers import ChameleonProcessor, ChameleonForConditionalGeneration
import torch
from PIL import Image
import reques... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#multi-image-inference | #multi-image-inference | .md | 387_4 |
The model can be loaded in 8 or 4 bits, greatly reducing the memory requirements while maintaining the performance of the original model. First make sure to install bitsandbytes, `pip install bitsandbytes` and to have access to a GPU/accelerator that is supported by the library.
<Tip>
bitsandbytes is being refactor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#quantization-using-bitsandbytes | #quantization-using-bitsandbytes | .md | 387_5 |
The models supports both, Flash-Attention 2 and PyTorch's [`torch.nn.functional.scaled_dot_product_attention`](https://pytorch.org/docs/master/generated/torch.nn.functional.scaled_dot_product_attention.html) which can be enables for optimization. SDPA is the default options when you load the model, If you want to switc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#use-flash-attention-2-and-sdpa-to-further-speed-up-generation | #use-flash-attention-2-and-sdpa-to-further-speed-up-generation | .md | 387_6 |
This is the configuration class to store the configuration of a [`ChameleonModel`]. It is used to instantiate a
chameleon 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
[meta/chameleon-7B](htt... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonconfig | #chameleonconfig | .md | 387_7 |
This is the configuration class to store the configuration of a [`ChameleonVQModel`]. It is used to instantiate a
`ChameleonVQModel` according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
docum... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonvqvaeconfig | #chameleonvqvaeconfig | .md | 387_8 |
Constructs a Chameleon processor which wraps a Chameleon image processor and a Chameleon tokenizer into a single
processor.
[`ChameleonProcessor`] offers all the functionalities of [`ChameleonImageProcessor`] and [`LlamaTokenizerFast`].
See the [`~ChameleonProcessor.__call__`] and [`~ChameleonProcessor.decode`] for m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonprocessor | #chameleonprocessor | .md | 387_9 |
Constructs a Chameleon 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": 512}`):
Siz... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonimageprocessor | #chameleonimageprocessor | .md | 387_10 |
The VQ-VAE model used in Chameleon for encoding/decoding images into discrete tokens.
This model follows the "Make-a-scene: Scene-based text-to-image generation with human priors" paper from
[ Oran Gafni, Adam Polyak, Oron Ashual, Shelly Sheynin, Devi Parikh, and Yaniv Taigman](https://arxiv.org/abs/2203.13131).
This... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonvqvae | #chameleonvqvae | .md | 387_11 |
The bare chameleon Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
T... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonmodel | #chameleonmodel | .md | 387_12 |
Chameleon Model with a head on top used for outputting logits for next token prediction.
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.)
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/chameleon.md | https://huggingface.co/docs/transformers/en/model_doc/chameleon/#chameleonforconditionalgeneration | #chameleonforconditionalgeneration | .md | 387_13 |
<!--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/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/ | .md | 388_0 | |
The YOSO model was proposed in [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714)
by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh. YOSO approximates standard softmax self-attention
via a Bernoulli sampling schem... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#overview | #overview | .md | 388_1 |
- The YOSO attention algorithm is implemented through custom CUDA kernels, functions written in CUDA C++ that can be executed multiple times
in parallel on a GPU.
- The kernels provide a `fast_hash` function, which approximates the random projections of the queries and keys using the Fast Hadamard Transform. Using thes... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#usage-tips | #usage-tips | .md | 388_2 |
- [Text classification task guide](../tasks/sequence_classification)
- [Token classification task guide](../tasks/token_classification)
- [Question answering task guide](../tasks/question_answering)
- [Masked language modeling task guide](../tasks/masked_language_modeling)
- [Multiple choice task guide](../tasks/multip... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#resources | #resources | .md | 388_3 |
This is the configuration class to store the configuration of a [`YosoModel`]. It is used to instantiate an YOSO
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 YOSO
[uw-madison/yoso-4096](http... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosoconfig | #yosoconfig | .md | 388_4 |
The bare YOSO 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 a... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosomodel | #yosomodel | .md | 388_5 |
YOSO Model 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:
config ([`YosoCon... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosoformaskedlm | #yosoformaskedlm | .md | 388_6 |
YOSO 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 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 documen... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosoforsequenceclassification | #yosoforsequenceclassification | .md | 388_7 |
YOSO 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 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 PyTorc... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosoformultiplechoice | #yosoformultiplechoice | .md | 388_8 |
YOSO 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 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 ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosofortokenclassification | #yosofortokenclassification | .md | 388_9 |
YOSO 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 is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Us... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yoso.md | https://huggingface.co/docs/transformers/en/model_doc/yoso/#yosoforquestionanswering | #yosoforquestionanswering | .md | 388_10 |
<!--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/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/ | .md | 389_0 | |
<Tip warning={true}>
This model is in maintenance mode only, so we won't accept any new PRs changing its code.
If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
You can do so by running the following command: `pip install -U transformers==4.30.0`. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/#trajectory-transformer | #trajectory-transformer | .md | 389_1 |
The Trajectory Transformer model was proposed in [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine.
The abstract from the paper is the following:
*Reinforcement learning (RL) is typically concerned with estimating sta... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/#overview | #overview | .md | 389_2 |
This Transformer is used for deep reinforcement learning. To use it, you need to create sequences from
actions, states and rewards from all previous timesteps. This model will treat all these elements together
as one big sequence (a trajectory). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/#usage-tips | #usage-tips | .md | 389_3 |
This is the configuration class to store the configuration of a [`TrajectoryTransformerModel`]. It is used to
instantiate an TrajectoryTransformer model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to that of th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/#trajectorytransformerconfig | #trajectorytransformerconfig | .md | 389_4 |
The bare TrajectoryTransformer 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 t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/trajectory_transformer.md | https://huggingface.co/docs/transformers/en/model_doc/trajectory_transformer/#trajectorytransformermodel | #trajectorytransformermodel | .md | 389_5 |
<!--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/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/ | .md | 390_0 | |
`StableLM 3B 4E1T` was proposed in [`StableLM 3B 4E1T`: Technical Report](https://stability.wandb.io/stability-llm/stable-lm/reports/StableLM-3B-4E1T--VmlldzoyMjU4?accessToken=u3zujipenkx5g7rtcj9qojjgxpconyjktjkli2po09nffrffdhhchq045vp0wyfo) by Stability AI and is the first model in a series of multi-epoch pre-trained ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#overview | #overview | .md | 390_1 |
`StableLM 3B 4E1T` is a decoder-only base language model pre-trained on 1 trillion tokens of diverse English and code datasets for four epochs.
The model architecture is transformer-based with partial Rotary Position Embeddings, SwiGLU activation, LayerNorm, etc.
We also provide `StableLM Zephyr 3B`, an instruction f... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#model-details | #model-details | .md | 390_2 |
- The architecture is similar to LLaMA but with RoPE applied to 25% of head embedding dimensions, LayerNorm instead of RMSNorm, and optional QKV bias terms.
- `StableLM 3B 4E1T`-based models uses the same tokenizer as [`GPTNeoXTokenizerFast`].
`StableLM 3B 4E1T` and `StableLM Zephyr 3B` can be found on the [Huggingfa... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#usage-tips | #usage-tips | .md | 390_3 |
First, make sure to install the latest version of Flash Attention v2.
```bash
pip install -U flash-attn --no-build-isolation
```
Also make sure that your hardware is compatible with Flash-Attention 2. Read more about it in the official documentation of the [`flash-attn`](https://github.com/Dao-AILab/flash-attention... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#combining-stablelm-and-flash-attention-2 | #combining-stablelm-and-flash-attention-2 | .md | 390_4 |
This is the configuration class to store the configuration of a [`~StableLmModel`].
It is used to instantiate an StableLM 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 StableLM [stabilityai/s... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#stablelmconfig | #stablelmconfig | .md | 390_5 |
The bare StableLm Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
Th... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/stablelm.md | https://huggingface.co/docs/transformers/en/model_doc/stablelm/#stablelmmodel | #stablelmmodel | .md | 390_6 |
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