source stringclasses 470
values | url stringlengths 49 167 | file_type stringclasses 1
value | chunk stringlengths 1 512 | chunk_id stringlengths 5 9 |
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/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosimageprocessor | .md | provided for preprocessing. If `pad_size` is not provided, images will be padded to the largest
height and width in the batch.
Methods: preprocess
- pad
- post_process_object_detection | 273_5_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosfeatureextractor | .md | No docstring available for YolosFeatureExtractor
Methods: __call__
- pad
- post_process_object_detection | 273_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosmodel | .md | The bare YOLOS 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 a... | 273_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosmodel | .md | behavior.
Parameters:
config ([`YolosConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: f... | 273_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosforobjectdetection | .md | YOLOS Model (consisting of a ViT encoder) with object detection heads on top, for tasks such as COCO detection.
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 ... | 273_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/yolos.md | https://huggingface.co/docs/transformers/en/model_doc/yolos/#yolosforobjectdetection | .md | behavior.
Parameters:
config ([`YolosConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: f... | 273_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/ | .md | <!--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... | 274_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/ | .md | Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that... | 274_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#overview | .md | Mistral was introduced in the [this blogpost](https://mistral.ai/news/announcing-mistral-7b/) 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-Anne Lachaux, P... | 274_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#overview | .md | The introduction of the blog post says:
*Mistral AI team is proud to release Mistral 7B, the most powerful language model for its size to date.*
Mistral-7B is the first large language model (LLM) released by [mistral.ai](https://mistral.ai/). | 274_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#architectural-details | .md | Mistral-7B is a decoder-only Transformer with the following architectural choices:
- Sliding Window Attention - Trained with 8k context length and fixed cache size, with a theoretical attention span of 128K tokens
- GQA (Grouped Query Attention) - allowing faster inference and lower cache size.
- Byte-fallback BPE to... | 274_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#license | .md | `Mistral-7B` is released under the Apache 2.0 license. | 274_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | The Mistral team has released 3 checkpoints:
- a base model, [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1), which has been pre-trained to predict the next token on internet-scale data.
- an instruction tuned model, [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v... | 274_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | - an improved instruction tuned model, [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2), which improves upon v1.
The base model can be used as follows:
```python
>>> from transformers import AutoModelForCausalLM, AutoTokenizer | 274_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model.to(device) | 274_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | >>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"My favourite condiment is to ..."
```
The instruction tuned model can be used as follows:
... | 274_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") | 274_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | >>> messages = [
... {"role": "user", "content": "What is your favourite condiment?"},
... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
... {"role": "user", "cont... | 274_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#usage-tips | .md | >>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"Mayonnaise can be made as follows: (...)"
```
As can be seen, the instruction-tuned model requi... | 274_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#speeding-up-mistral-by-using-flash-attention | .md | 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... | 274_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#speeding-up-mistral-by-using-flash-attention | .md | ```bash
pip install -U flash-attn --no-build-isolation
```
Make also sure that you have a hardware that is compatible with Flash-Attention 2. Read more about it in the official documentation of the [flash attention repository](https://github.com/Dao-AILab/flash-attention). Make also sure to load your model in half-pr... | 274_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#speeding-up-mistral-by-using-flash-attention | .md | To load and run a model using Flash Attention-2, refer to the snippet below:
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer | 274_5_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#speeding-up-mistral-by-using-flash-attention | .md | >>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
>>> prompt = "My favourite condiment is"
>>> model_inputs = tokenizer([prompt]... | 274_5_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#speeding-up-mistral-by-using-flash-attention | .md | >>> model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
>>> model.to(device)
>>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"My favourite condiment is to (...)"
``` | 274_5_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#expected-speedups | .md | Below is a expected speedup diagram that compares pure inference time between the native implementation in transformers using `mistralai/Mistral-7B-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-images/... | 274_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#sliding-window-attention | .md | 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`). | 274_7_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#sliding-window-attention | .md | The Flash Attention-2 model uses also a more memory efficient cache slicing mechanism - as recommended per the official implementation of Mistral model that use rolling cache mechanism we keep the cache size fixed (`self.config.sliding_window`), support batched generation only for `padding_side="left"` and use the abso... | 274_7_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | .md | As the Mistral model has 7 billion parameters, that would require about 14GB 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),... | 274_8_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | .md | Quantizing a model is as simple as passing a `quantization_config` to the model. Below, we'll leverage the BitsAndyBytes quantization (but refer to [this page](../quantization.md) for other quantization methods):
```python
>>> import torch
>>> from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytes... | 274_8_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | .md | >>> # specify how to quantize the model
>>> quantization_config = BitsAndBytesConfig(
... load_in_4bit=True,
... bnb_4bit_quant_type="nf4",
... bnb_4bit_compute_dtype="torch.float16",
... )
>>> model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2", quantization_confi... | 274_8_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | .md | >>> prompt = "My favourite condiment is"
>>> messages = [
... {"role": "user", "content": "What is your favourite condiment?"},
... {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the ... | 274_8_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#shrinking-down-mistral-using-quantization | .md | >>> model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
>>> generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)
>>> tokenizer.batch_decode(generated_ids)[0]
"The expected output"
```
This model was contributed by [Younes Belkada](https://huggingface.co/... | 274_8_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#resources | .md | A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with Mistral. 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 ... | 274_9_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#resources | .md | <PipelineTag pipeline="text-generation"/>
- A demo notebook to perform supervised fine-tuning (SFT) of Mistral-7B can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/blob/master/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb). 🌎
- A [blog post](https://www.philsc... | 274_9_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#resources | .md | - The [Alignment Handbook](https://github.com/huggingface/alignment-handbook) by Hugging Face includes scripts and recipes to perform supervised fine-tuning (SFT) and direct preference optimization with Mistral-7B. This includes scripts for full fine-tuning, QLoRa on a single GPU as well as multi-GPU fine-tuning.
- [Ca... | 274_9_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
Mistral 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 Mistral-7B-v0.1 or Mistral... | 274_10_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedCo... | 274_10_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 32000):
Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`MistralModel`]
hidden_size (`int`, *optional*, defaults ... | 274_10_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | intermediate_size (`int`, *optional*, defaults to 14336):
Dimension of the MLP representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 32):
Number of attention heads for each attention layer in the Tr... | 274_10_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | This is the number of key_value heads that should be used to implement Grouped Query Attention. If
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
`num_key_value_heads=1` the model will use Multi Query Attention (MQA) otherwise GQA is used. When
converting a multi-head check... | 274_10_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | by meanpooling all the original heads within that group. For more details checkout [this
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
head_dim (`int`, *optional*, defaults to `hidden_size // num_attention_heads`):
The attention head dimension.
hidden_act (`str` or `function... | 274_10_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
allows sequence of up to 4096*32 tokens.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_i... | 274_10_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | rms_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the rms normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
pad_token_id (`int`, *op... | 274_10_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 2):
The id of the "end-of-sequence" token.
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
Whether the model's input and output word embeddings should be tied.
rope_theta (`float`, *optional*, defaults to 10000.0):
... | 274_10_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | Sliding window attention window size. If not specified, will default to `4096`.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
```python
>>> from transformers import MistralModel, MistralConfig | 274_10_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralconfig | .md | >>> # Initializing a Mistral 7B style configuration
>>> configuration = MistralConfig()
>>> # Initializing a model from the Mistral 7B style configuration
>>> model = MistralModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
``` | 274_10_10 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralmodel | .md | The bare Mistral 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... | 274_11_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralmodel | .md | etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class wi... | 274_11_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralmodel | .md | load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MistralDecoderLayer`]
Args:
config: MistralConfig
Methods: forward | 274_11_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforcausallm | .md | No docstring available for MistralForCausalLM
Methods: forward | 274_12_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | .md | The Mistral Model transformer with a sequence classification head on top (linear layer).
[`MistralForSequenceClassification`] 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... | 274_13_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | .md | `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (ta... | 274_13_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | .md | 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 [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#to... | 274_13_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforsequenceclassification | .md | and behavior.
Parameters:
config ([`MistralConfig`]):
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Meth... | 274_13_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralfortokenclassification | .md | The Mistral 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... | 274_14_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralfortokenclassification | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all ma... | 274_14_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralfortokenclassification | .md | Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 274_14_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforquestionanswering | .md | The Mistral Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute `span start logits` and `span end logits`).
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the gen... | 274_15_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforquestionanswering | .md | library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all ma... | 274_15_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#mistralforquestionanswering | .md | Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods: forward | 274_15_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralmodel | .md | No docstring available for FlaxMistralModel
Methods: __call__ | 274_16_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#flaxmistralforcausallm | .md | No docstring available for FlaxMistralForCausalLM
Methods: __call__ | 274_17_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralmodel | .md | No docstring available for TFMistralModel
Methods: call | 274_18_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforcausallm | .md | No docstring available for TFMistralForCausalLM
Methods: call | 274_19_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/mistral.md | https://huggingface.co/docs/transformers/en/model_doc/mistral/#tfmistralforsequenceclassification | .md | No docstring available for TFMistralForSequenceClassification
Methods: call | 274_20_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/ | .md | <!--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... | 275_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 275_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | The Swin2SR model was proposed in [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
Swin2SR improves the [SwinIR](https://github.com/JingyunLiang/SwinIR/) model by incorporating [Swin Transf... | 275_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | and fine-tuning, and hunger on data.
The abstract from the paper is the following: | 275_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | *Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual ... | 275_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. | 275_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tun... | 275_1_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on Super-Res... | 275_1_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#overview | .md | <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/swin2sr_architecture.png"
alt="drawing" width="600"/>
<small> Swin2SR architecture. Taken from the <a href="https://arxiv.org/abs/2209.11345">original paper.</a> </small>
This model was contributed by [nie... | 275_1_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#resources | .md | Demo notebooks for Swin2SR can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/Swin2SR).
A demo Space for image super-resolution with SwinSR can be found [here](https://huggingface.co/spaces/jjourney1125/swin2sr). | 275_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srimageprocessor | .md | Constructs a Swin2SR image processor.
Args:
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the `do_rescale`
parameter in the `preprocess` method.
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale f... | 275_3_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | This is the configuration class to store the configuration of a [`Swin2SRModel`]. It is used to instantiate a Swin
Transformer v2 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 Swin Transforme... | 275_4_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | [caidas/swin2sr-classicalsr-x2-64](https://huggingface.co/caidas/swin2sr-classicalsr-x2-64) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
image_size (`int`, *optional*, ... | 275_4_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
num_channels_out (`int`, *optional*, defaults to `num_channels`):
The number of output channels. If not set, it will be set to `num_channels`.
embed_dim (`int`, *optional*, defaults to 180):
Dimensionalit... | 275_4_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | depths (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
Depth of each layer in the Transformer encoder.
num_heads (`list(int)`, *optional*, defaults to `[6, 6, 6, 6, 6, 6]`):
Number of attention heads in each layer of the Transformer encoder.
window_size (`int`, *optional*, defaults to 8):
Size of windows.
... | 275_4_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | Ratio of MLP hidden dimensionality to embedding dimensionality.
qkv_bias (`bool`, *optional*, defaults to `True`):
Whether or not a learnable bias should be added to the queries, keys and values.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the em... | 275_4_4 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
drop_path_rate (`float`, *optional*, defaults to 0.1):
Stochastic depth rate.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string... | 275_4_5 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | `"selu"` and `"gelu_new"` are supported.
use_absolute_embeddings (`bool`, *optional*, defaults to `False`):
Whether or not to add absolute position embeddings to the patch embeddings.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing a... | 275_4_6 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | The epsilon used by the layer normalization layers.
upscale (`int`, *optional*, defaults to 2):
The upscale factor for the image. 2/3/4/8 for image super resolution, 1 for denoising and compress artifact
reduction
img_range (`float`, *optional*, defaults to 1.0):
The range of the values of the input image.
resi_connect... | 275_4_7 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | upsampler (`str`, *optional*, defaults to `"pixelshuffle"`):
The reconstruction reconstruction module. Can be 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None.
Example:
```python
>>> from transformers import Swin2SRConfig, Swin2SRModel | 275_4_8 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srconfig | .md | >>> # Initializing a Swin2SR caidas/swin2sr-classicalsr-x2-64 style configuration
>>> configuration = Swin2SRConfig()
>>> # Initializing a model (with random weights) from the caidas/swin2sr-classicalsr-x2-64 style configuration
>>> model = Swin2SRModel(configuration)
>>> # Accessing the model configuration
>>> confi... | 275_4_9 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srmodel | .md | The bare Swin2SR 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 usag... | 275_5_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srmodel | .md | behavior.
Parameters:
config ([`Swin2SRConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods:... | 275_5_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srforimagesuperresolution | .md | Swin2SR Model transformer with an upsampler head on top for image super resolution and restoration.
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 ... | 275_6_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/swin2sr.md | https://huggingface.co/docs/transformers/en/model_doc/swin2sr/#swin2srforimagesuperresolution | .md | behavior.
Parameters:
config ([`Swin2SRConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
Methods:... | 275_6_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/ | .md | <!--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... | 276_0_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered ... | 276_0_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | .md | The FLAVA model was proposed in [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela and is accepted at CVPR 2022.
The paper aims at creating a single unifie... | 276_1_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | .md | as well as vision-and-language multimodal tasks.
The abstract from the paper is the following:
*State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety
of downstream tasks. Generally, such models are often either cross-modal... | 276_1_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | .md | (with earlier fusion) but not both; and they often only target specific modalities or tasks. A promising
direction would be to use a single holistic universal model, as a "foundation", that targets all modalities
at once -- a true vision and language foundation model should be good at vision tasks, language tasks, and
... | 276_1_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#overview | .md | impressive performance on a wide range of 35 tasks spanning these target modalities.*
This model was contributed by [aps](https://huggingface.co/aps). The original code can be found [here](https://github.com/facebookresearch/multimodal/tree/main/examples/flava). | 276_1_3 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | .md | [`FlavaConfig`] is the configuration class to store the configuration of a [`FlavaModel`]. It is used to
instantiate FLAVA model according to the specified arguments, defining the text model, image model, image codebook
and multimodal model configs. Instantiating a configuration with the defaults will yield a similar c... | 276_2_0 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | .md | that of the FLAVA [facebook/flava-full](https://huggingface.co/facebook/flava-full) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dicti... | 276_2_1 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | .md | image_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaImageConfig`].
multimodal_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`FlavaMultimodalConfig`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers... | 276_2_2 |
/Users/nielsrogge/Documents/python_projecten/transformers/docs/source/en/model_doc/flava.md | https://huggingface.co/docs/transformers/en/model_doc/flava/#flavaconfig | .md | The epsilon used by the layer normalization layers.
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and image projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter. Default is used as per the original FLAVA/CLIP
... | 276_2_3 |
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