Repository: huggingface/transformers
Pull request: Fix typos in documentation

# What does this PR do?
As the title says.
 

## Before submitting
- [X] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [ ] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#create-a-pull-request),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker @Cyrilvallez
- vision models: @yonigozlan @molbap
- audio models: @eustlb @ebezzam @vasqu
- multimodal models: @zucchini-nlp
- graph models: @clefourrier

Library:

- generate: @zucchini-nlp (visual-language models) or @gante (all others)
- continuous batching: @remi-or @ArthurZucker @McPatate
- pipelines: @Rocketknight1
- tokenizers: @ArthurZucker and @itazap
- trainer: @SunMarc
- attention: @vasqu @ArthurZucker @CyrilVallez
- model loading (from pretrained, etc): @CyrilVallez
- distributed: @3outeille @ArthurZucker
- CIs: @ydshieh

Integrations:

- ray/raytune: @richardliaw, @amogkam
- Big Model Inference: @SunMarc
- quantization: @SunMarc @MekkCyber
- kernels: @MekkCyber @drbh
- peft: @BenjaminBossan @githubnemo

Devices/Backends:

- AMD ROCm: @ivarflakstad
- Intel XPU: @IlyasMoutawwakil
- Ascend NPU: @ivarflakstad 

Documentation: @stevhliu

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Changed files:
filename: docs/source/en/model_doc/evolla.md
patch: @@ -61,7 +61,7 @@ message_list = [
     ]
 ]
 input_dict = processor(
-    protein_informations, messages_list, return_tensors="pt", text_max_length=512, protein_max_length=1024
+    protein_inputs, messages_list, return_tensors="pt", text_max_length=512, protein_max_length=1024
 )
 with torch.no_grad():
     generated_ids = hf_model.generate(**input_dict)

filename: docs/source/en/model_doc/nllb.md
patch: @@ -55,7 +55,7 @@ pipeline("UN Chief says there is no military solution in Syria")
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
 
 tokenizer = AutoTokenizer.from_pretrained("facebook/nllb-200-distilled-600M")
-model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", dtype="auto", attn_implementaiton="sdpa")
+model = AutoModelForSeq2SeqLM.from_pretrained("facebook/nllb-200-distilled-600M", dtype="auto", attn_implementation="sdpa")
 
 article = "UN Chief says there is no military solution in Syria"
 inputs = tokenizer(article, return_tensors="pt")

filename: docs/source/en/model_doc/voxtral.md
patch: @@ -19,7 +19,7 @@ rendered properly in your Markdown viewer.
 
 Voxtral is an upgrade of [Ministral 3B and Mistral Small 3B](https://mistral.ai/news/ministraux), extending its language capabilities with audio input support. It is designed to handle tasks such as speech transcription, translation, and audio understanding.
 
-You can read more in Mistral's [realease blog post](https://mistral.ai/news/voxtral).
+You can read more in Mistral's [release blog post](https://mistral.ai/news/voxtral).
 
 The model is available in two checkpoints:
 

filename: docs/source/en/trainer.md
patch: @@ -33,7 +33,7 @@ This guide will show you how [`Trainer`] works and how to customize it for your
 3. update the weights based on the gradients
 4. repeat until the predetermined number of epochs is reached
 
-Manually coding this training loop everytime can be inconvenient or a barrier if you're just getting started with machine learning. [`Trainer`] abstracts this process, allowing you to focus on the model, dataset, and training design choices.
+Manually coding this training loop every time can be inconvenient or a barrier if you're just getting started with machine learning. [`Trainer`] abstracts this process, allowing you to focus on the model, dataset, and training design choices.
 
 Configure your training with hyperparameters and options from [`TrainingArguments`] which supports many features such as distributed training, torch.compile, mixed precision training, and saving the model to the Hub.
 

filename: notebooks/README.md
patch: @@ -100,7 +100,7 @@ You can open any page of the documentation as a notebook in Colab (there is a bu
 
 ### Optimum notebooks
 
-🤗  [Optimum](https://github.com/huggingface/optimum) is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardwares.
+🤗  [Optimum](https://github.com/huggingface/optimum) is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardware.
 
 | Notebook     |      Description      |   |   |
 |:----------|:-------------|:-------------|------:|