text stringlengths 5 58.6k | source stringclasses 470
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No docstring available for FlaxBigBirdForSequenceClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdforsequenceclassification | #flaxbigbirdforsequenceclassification | .md | 175_20 |
No docstring available for FlaxBigBirdForMultipleChoice
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdformultiplechoice | #flaxbigbirdformultiplechoice | .md | 175_21 |
No docstring available for FlaxBigBirdForTokenClassification
Methods: __call__ | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdfortokenclassification | #flaxbigbirdfortokenclassification | .md | 175_22 |
No docstring available for FlaxBigBirdForQuestionAnswering
Methods: __call__
</jax>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/big_bird.md | https://huggingface.co/docs/transformers/en/model_doc/big_bird/#flaxbigbirdforquestionanswering | #flaxbigbirdforquestionanswering | .md | 175_23 |
<!--Copyright 2023 Mistral AI and The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applic... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/ | .md | 176_0 | |
Mixtral-8x7B was introduced in the [Mixtral of Experts blogpost](https://mistral.ai/news/mixtral-of-experts/) by Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#overview | #overview | .md | 176_1 |
Mixtral-8x7B is a decoder-only Transformer with the following architectural choices:
- Mixtral is a Mixture of Experts (MoE) model with 8 experts per MLP, with a total of 45 billion parameters. To learn more about mixture-of-experts, refer to the [blog post](https://huggingface.co/blog/moe).
- Despite the model havin... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#architectural-details | #architectural-details | .md | 176_2 |
`Mixtral-8x7B` is released under the Apache 2.0 license. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#license | #license | .md | 176_3 |
The Mistral team has released 2 checkpoints:
- a base model, [Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1), which has been pre-trained to predict the next token on internet-scale data.
- an instruction tuned model, [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Inst... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#usage-tips | #usage-tips | .md | 176_4 |
The code snippets above showcase inference without any optimization tricks. However, one can drastically speed up the model by leveraging [Flash Attention](../perf_train_gpu_one#flash-attention-2), which is a faster implementation of the attention mechanism used inside the model.
First, make sure to install the lates... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#speeding-up-mixtral-by-using-flash-attention | #speeding-up-mixtral-by-using-flash-attention | .md | 176_5 |
Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mixtral-8x7B-v0.1` checkpoint and the Flash Attention 2 version of the model.
<div style="text-align: center">
<img src="https://huggingface.co/datasets/ybelkada/documentation-image... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#expected-speedups | #expected-speedups | .md | 176_6 |
The current implementation supports the sliding window attention mechanism and memory efficient cache management.
To enable sliding window attention, just make sure to have a `flash-attn` version that is compatible with sliding window attention (`>=2.3.0`).
The Flash Attention-2 model uses also a more memory efficien... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#sliding-window-attention | #sliding-window-attention | .md | 176_7 |
As the Mixtral model has 45 billion parameters, that would require about 90GB of GPU RAM in half precision (float16), since each parameter is stored in 2 bytes. However, one can shrink down the size of the model using [quantization](../quantization.md). If the model is quantized to 4 bits (or half a byte per parameter)... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#shrinking-down-mixtral-using-quantization | #shrinking-down-mixtral-using-quantization | .md | 176_8 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mixtral. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#resources | #resources | .md | 176_9 |
This is the configuration class to store the configuration of a [`MixtralModel`]. It is used to instantiate an
Mixtral 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 Mixtral-7B-v0.1 or Mixtral... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#mixtralconfig | #mixtralconfig | .md | 176_10 |
The bare Mixtral 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/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#mixtralmodel | #mixtralmodel | .md | 176_11 |
No docstring available for MixtralForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#mixtralforcausallm | #mixtralforcausallm | .md | 176_12 |
The Mixtral Model transformer with a sequence classification head on top (linear layer).
[`MixtralForSequenceClassification`] 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 to... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#mixtralforsequenceclassification | #mixtralforsequenceclassification | .md | 176_13 |
The Mixtral Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (su... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mixtral.md | https://huggingface.co/docs/transformers/en/model_doc/mixtral/#mixtralfortokenclassification | #mixtralfortokenclassification | .md | 176_14 |
<!--Copyright 2022 The HuggingFace Team and Microsoft. All rights reserved.
Licensed under the MIT License; you may not use this file except in compliance with
the License.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT W... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/ | .md | 177_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don'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: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#graphormer | #graphormer | .md | 177_1 |
The Graphormer model was proposed in [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234) by
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs inst... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#overview | #overview | .md | 177_2 |
This model will not work well on large graphs (more than 100 nodes/edges), as it will make the memory explode.
You can reduce the batch size, increase your RAM, or decrease the `UNREACHABLE_NODE_DISTANCE` parameter in algos_graphormer.pyx, but it will be hard to go above 700 nodes/edges.
This model does not use a tok... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#usage-tips | #usage-tips | .md | 177_3 |
This is the configuration class to store the configuration of a [`~GraphormerModel`]. It is used to instantiate an
Graphormer 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 Graphormer
[graphor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#graphormerconfig | #graphormerconfig | .md | 177_4 |
The Graphormer model is a graph-encoder model.
It goes from a graph to its representation. If you want to use the model for a downstream classification task, use
GraphormerForGraphClassification instead. For any other downstream task, feel free to add a new class, or combine
this model with a downstream model of your... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#graphormermodel | #graphormermodel | .md | 177_5 |
This model can be used for graph-level classification or regression tasks.
It can be trained on
- regression (by setting config.num_classes to 1); there should be one float-type label per graph
- one task classification (by setting config.num_classes to the number of classes); there should be one integer
label per gr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/graphormer.md | https://huggingface.co/docs/transformers/en/model_doc/graphormer/#graphormerforgraphclassification | #graphormerforgraphclassification | .md | 177_6 |
<!--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/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/ | .md | 178_0 | |
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen It is based on Google's
BERT model released in 2018 and Facebook's RoBERTa model released in 2019.
It builds on RoBERTa with disent... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#overview | #overview | .md | 178_1 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with DeBERTa. If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we'll review it! The resource should ideally demonstrate something new instead of duplicating an ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#resources | #resources | .md | 178_2 |
This is the configuration class to store the configuration of a [`DebertaModel`] or a [`TFDebertaModel`]. It is
used to instantiate a DeBERTa 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 DeB... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertaconfig | #debertaconfig | .md | 178_3 |
Construct a DeBERTa tokenizer. Based on byte-level Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without space) or not:
```python
>>> from transformers impo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertatokenizer | #debertatokenizer | .md | 178_4 |
Construct a "fast" DeBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
Byte-Pair-Encoding.
This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
be encoded differently whether it is at the beginning of the sentence (without... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertatokenizerfast | #debertatokenizerfast | .md | 178_5 |
The bare DeBERTa Model transformer outputting raw hidden-states without any specific head on top.
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
on top of BERT/RoBERTa ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertamodel | #debertamodel | .md | 178_6 |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertapretrainedmodel | #debertapretrainedmodel | .md | 178_7 |
DeBERTa Model with a `language modeling` head on top.
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen. It's build
on top of BERT/RoBERTa with two improvements, i.e. disentangled att... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertaformaskedlm | #debertaformaskedlm | .md | 178_8 |
DeBERTa Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Ga... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertaforsequenceclassification | #debertaforsequenceclassification | .md | 178_9 |
DeBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
Named-Entity-Recognition (NER) tasks.
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jian... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertafortokenclassification | #debertafortokenclassification | .md | 178_10 |
DeBERTa 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`).
The DeBERTa model was proposed in [DeBERTa: Decoding-enhanced BERT with Disentangled
Attention](https://arx... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#debertaforquestionanswering | #debertaforquestionanswering | .md | 178_11 |
No docstring available for TFDebertaModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertamodel | #tfdebertamodel | .md | 178_12 |
No docstring available for TFDebertaPreTrainedModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertapretrainedmodel | #tfdebertapretrainedmodel | .md | 178_13 |
No docstring available for TFDebertaForMaskedLM
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertaformaskedlm | #tfdebertaformaskedlm | .md | 178_14 |
No docstring available for TFDebertaForSequenceClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertaforsequenceclassification | #tfdebertaforsequenceclassification | .md | 178_15 |
No docstring available for TFDebertaForTokenClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertafortokenclassification | #tfdebertafortokenclassification | .md | 178_16 |
No docstring available for TFDebertaForQuestionAnswering
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/deberta.md | https://huggingface.co/docs/transformers/en/model_doc/deberta/#tfdebertaforquestionanswering | #tfdebertaforquestionanswering | .md | 178_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/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/ | .md | 179_0 | |
<Tip warning={true}>
This model is in maintenance mode only, we don'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: v4.40.2.
You can do so by running the following command: `pip install -U transformers==4.40.2`.
</Ti... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#hybrid-vision-transformer-vit-hybrid | #hybrid-vision-transformer-vit-hybrid | .md | 179_1 |
The hybrid Vision Transformer (ViT) model was proposed in [An Image is Worth 16x16 Words: Transformers for Image Recognition
at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk
Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Geor... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#overview | #overview | .md | 179_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_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 179_3 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with ViT Hybrid.
<PipelineTag pipeline="image-classification"/>
- [`ViTHybridForImageClassification`] is supported by this [example script](https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-c... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#resources | #resources | .md | 179_4 |
This is the configuration class to store the configuration of a [`ViTHybridModel`]. It is used to instantiate a ViT
Hybrid 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 Hybrid
[google/vit... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#vithybridconfig | #vithybridconfig | .md | 179_5 |
Constructs a ViT Hybrid 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/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#vithybridimageprocessor | #vithybridimageprocessor | .md | 179_6 |
The bare ViT Hybrid 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 us... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#vithybridmodel | #vithybridmodel | .md | 179_7 |
ViT Hybrid Model transformer with an image classification head on top (a linear layer on top of the final hidden
state of the [CLS] token) 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... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/vit_hybrid.md | https://huggingface.co/docs/transformers/en/model_doc/vit_hybrid/#vithybridforimageclassification | #vithybridforimageclassification | .md | 179_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/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/ | .md | 180_0 | |
Zamba is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights.
This model was contributed by [pglo](https://huggingface.co/pglo). | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#zamba | #zamba | .md | 180_1 |
Zamba-7B-v1 is a hybrid between state-space models (Specifically [Mamba](https://github.com/state-spaces/mamba)) and transformer, and was trained using next-token prediction. Zamba uses a shared transformer layer after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#model-details | #model-details | .md | 180_2 |
Zamba requires you use `transformers` version 4.46.0 or higher:
```bash
pip install transformers>=4.45.0
```
In order to run optimized Mamba implementations, you first need to install `mamba-ssm` and `causal-conv1d`:
```bash
pip install mamba-ssm causal-conv1d>=1.2.0
```
You also have to have the model on a CUDA devi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#presequities | #presequities | .md | 180_3 |
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-7B-v1")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-7B-v1", device_map="auto", torch_dtype=torch.bfloat16)
input_text = "A funny prompt would be "
input_ids = toke... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#inference | #inference | .md | 180_4 |
The model cards can be found at:
* [Zamba-7B](MODEL_CARD_ZAMBA-7B-v1.md) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#model-card | #model-card | .md | 180_5 |
For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/zyphra/zamba-7b) | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#issues | #issues | .md | 180_6 |
The model weights are open-sourced via an Apache 2.0 license. | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#license | #license | .md | 180_7 |
This is the configuration class to store the configuration of a [`ZambaModel`]. It is used to instantiate a
Zamba 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 Zamba-v0.1 model.
[Zyphra/Zam... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#zambaconfig | #zambaconfig | .md | 180_8 |
The bare Zamba Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#zambamodel | #zambamodel | .md | 180_9 |
No docstring available for ZambaForCausalLM
Methods: forward | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#zambaforcausallm | #zambaforcausallm | .md | 180_10 |
The Zamba Model with a sequence classification head on top (linear layer).
[`ZambaForSequenceClassification`] 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 a
`pad_t... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/zamba.md | https://huggingface.co/docs/transformers/en/model_doc/zamba/#zambaforsequenceclassification | #zambaforsequenceclassification | .md | 180_11 |
<!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/ | .md | 181_0 | |
The Data2Vec model was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and Michael Auli.
Data2Vec proposes a unified framework for self-supervised learning across ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#overview | #overview | .md | 181_1 |
- Data2VecAudio, Data2VecText, and Data2VecVision have all been trained using the same self-supervised learning method.
- For Data2VecAudio, preprocessing is identical to [`Wav2Vec2Model`], including feature extraction
- For Data2VecText, preprocessing is identical to [`RobertaModel`], including tokenization.
- For Dat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#usage-tips | #usage-tips | .md | 181_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/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#using-scaled-dot-product-attention-sdpa | #using-scaled-dot-product-attention-sdpa | .md | 181_3 |
| num_training_steps | batch_size | image_size | 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/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#training | #training | .md | 181_4 |
| Image batch size | Eager (s/iter) | Eager CI, % | Eager memory (MB) | SDPA (s/iter) | SDPA CI, % | SDPA memory (MB) | SDPA speedup | SDPA memory saved |
|-------------------:|-----------------:|:--------------|--------------------:|----------------:|:-------------|-------------------:|--------------... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#inference | #inference | .md | 181_5 |
A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Data2Vec.
<PipelineTag pipeline="image-classification"/>
- [`Data2VecVisionForImageClassification`] 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/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#resources | #resources | .md | 181_6 |
This is the configuration class to store the configuration of a [`Data2VecTextModel`] and [`Data2VecTextModel`]. It
is used to instantiate a Data2VecText model according to the specified arguments, defining the model architecture.
Instantiating a configuration with the defaults will yield a similar configuration to tha... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextconfig | #data2vectextconfig | .md | 181_7 |
This is the configuration class to store the configuration of a [`Data2VecAudioModel`]. It is used to instantiate
an Data2VecAudio 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 Data2VecAudio
... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudioconfig | #data2vecaudioconfig | .md | 181_8 |
This is the configuration class to store the configuration of a [`Data2VecVisionModel`]. It is used to instantiate
an Data2VecVision 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 Data2VecVisi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecvisionconfig | #data2vecvisionconfig | .md | 181_9 |
The bare Data2VecAudio Model transformer outputting raw hidden-states without any specific head on top.
Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jia... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudiomodel | #data2vecaudiomodel | .md | 181_10 |
Data2VecAudio Model with a frame classification head on top for tasks like Speaker Diarization.
Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudioforaudioframeclassification | #data2vecaudioforaudioframeclassification | .md | 181_11 |
Data2VecAudio Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).
Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu,... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudioforctc | #data2vecaudioforctc | .md | 181_12 |
Data2VecAudio Model with a sequence classification head on top (a linear layer over the pooled output) for tasks
like SUPERB Keyword Spotting.
Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudioforsequenceclassification | #data2vecaudioforsequenceclassification | .md | 181_13 |
Data2VecAudio Model with an XVector feature extraction head on top for tasks like Speaker Verification.
Data2VecAudio was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, J... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecaudioforxvector | #data2vecaudioforxvector | .md | 181_14 |
The bare Data2VecText Model for text transformer outputting raw hidden-states without any specific head on top.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Ba... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextmodel | #data2vectextmodel | .md | 181_15 |
Data2VecText Model with a `language modeling` head on top for CLM fine-tuning.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
Michael Auli. ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextforcausallm | #data2vectextforcausallm | .md | 181_16 |
data2vec Model with a `language modeling` head on top.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu and
Michael Auli.
This model inherits fro... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextformaskedlm | #data2vectextformaskedlm | .md | 181_17 |
Data2VecText Model transformer with a sequence classification/regression head on top (a linear layer on top of the
pooled output) e.g. for GLUE tasks.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Alexei Ba... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextforsequenceclassification | #data2vectextforsequenceclassification | .md | 181_18 |
Data2VecText 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.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextformultiplechoice | #data2vectextformultiplechoice | .md | 181_19 |
Data2VecText 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.
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vision and
Language](https://arxiv.org/pdf/2202.03555) by Al... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextfortokenclassification | #data2vectextfortokenclassification | .md | 181_20 |
Data2VecText 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`).
Data2VecText was proposed in [data2vec: A General Framework for Self-supervised Learning in Speech, Vi... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vectextforquestionanswering | #data2vectextforquestionanswering | .md | 181_21 |
The bare Data2VecVision 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 genera... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecvisionmodel | #data2vecvisionmodel | .md | 181_22 |
Data2VecVision Model transformer with an image classification head on top (a linear layer on top of the average of
the final hidden states of the patch tokens) 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 Mo... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecvisionforimageclassification | #data2vecvisionforimageclassification | .md | 181_23 |
Data2VecVision Model transformer with a semantic segmentation head on top e.g. for ADE20k, CityScapes.
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 genera... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#data2vecvisionforsemanticsegmentation | #data2vecvisionforsemanticsegmentation | .md | 181_24 |
No docstring available for TFData2VecVisionModel
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#tfdata2vecvisionmodel | #tfdata2vecvisionmodel | .md | 181_25 |
No docstring available for TFData2VecVisionForImageClassification
Methods: call | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#tfdata2vecvisionforimageclassification | #tfdata2vecvisionforimageclassification | .md | 181_26 |
No docstring available for TFData2VecVisionForSemanticSegmentation
Methods: call
</tf>
</frameworkcontent> | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/data2vec.md | https://huggingface.co/docs/transformers/en/model_doc/data2vec/#tfdata2vecvisionforsemanticsegmentation | #tfdata2vecvisionforsemanticsegmentation | .md | 181_27 |
<!--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/ul2.md | https://huggingface.co/docs/transformers/en/model_doc/ul2/ | .md | 182_0 | |
The T5 model was presented in [Unifying Language Learning Paradigms](https://arxiv.org/pdf/2205.05131v1.pdf) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler.
The abstract from the paper is the following:
*Existing pre-trained mod... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ul2.md | https://huggingface.co/docs/transformers/en/model_doc/ul2/#overview | #overview | .md | 182_1 |
- UL2 is an encoder-decoder model pre-trained on a mixture of denoising functions as well as fine-tuned on an array of downstream tasks.
- UL2 has the same architecture as [T5v1.1](t5v1.1) but uses the Gated-SiLU activation function instead of Gated-GELU.
- The authors release checkpoints of one architecture which can ... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/ul2.md | https://huggingface.co/docs/transformers/en/model_doc/ul2/#usage-tips | #usage-tips | .md | 182_2 |
<!--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/olmo.md | https://huggingface.co/docs/transformers/en/model_doc/olmo/ | .md | 183_0 | |
The OLMo model was proposed in [OLMo: Accelerating the Science of Language Models](https://arxiv.org/abs/2402.00838) by Dirk Groeneveld, Iz Beltagy, Pete Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Harsh Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, Shane Arora, David Atkinson, Russell Authur, Khyat... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo.md | https://huggingface.co/docs/transformers/en/model_doc/olmo/#overview | #overview | .md | 183_1 |
This is the configuration class to store the configuration of a [`OlmoModel`]. It is used to instantiate an OLMo
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the [allenai/OLMo-7B-hf](https://hug... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo.md | https://huggingface.co/docs/transformers/en/model_doc/olmo/#olmoconfig | #olmoconfig | .md | 183_2 |
The bare Olmo Model outputting raw hidden-states without any specific head on top.
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This m... | /Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/olmo.md | https://huggingface.co/docs/transformers/en/model_doc/olmo/#olmomodel | #olmomodel | .md | 183_3 |
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