Chore: Typo fixed in multiple files of docs/source/en/model_doc (#26833)

* Chore: Typo fixed in multiple files of docs/source/en/model_doc

* Update docs/source/en/model_doc/nllb-moe.md
Co-authored-by: Aryan V S <avs050602@gmail.com>

---------
Co-authored-by: Aryan V S <avs050602@gmail.com>

docs/source/en/model_doc/bark.md

@@ -64,7 +64,7 @@ model.enable_cpu_offload()
 
 Note that 🤗 Accelerate must be installed before using this feature. [Here's how to install it.](https://huggingface.co/docs/accelerate/basic_tutorials/install)
 
-#### Combining optimizaton techniques
+#### Combining optimization techniques
 
 You can combine optimization techniques, and use CPU offload, half-precision and 🤗 Better Transformer all at once.
 

docs/source/en/model_doc/flan-ul2.md

@@ -19,10 +19,10 @@ rendered properly in your Markdown viewer.
 ## Overview
 
 Flan-UL2 is an encoder decoder model based on the T5 architecture. It uses the same configuration as the [UL2](ul2) model released earlier last year. 
-It was fine tuned using the "Flan" prompt tuning and dataset collection. Similiar to `Flan-T5`,  one can directly use FLAN-UL2 weights without finetuning the model:
+It was fine tuned using the "Flan" prompt tuning and dataset collection. Similar to `Flan-T5`,  one can directly use FLAN-UL2 weights without finetuning the model:
 
 
-According ot the original blog here are the notable improvements:
+According to the original blog here are the notable improvements:
 
 - The original UL2 model was only trained with receptive field of 512, which made it non-ideal for N-shot prompting where N is large.
 - The Flan-UL2 checkpoint uses a receptive field of 2048 which makes it more usable for few-shot in-context learning.
@@ -53,4 +53,4 @@ The model is pretty heavy (~40GB in half precision) so if you just want to run t
 
 ## Inference
 
-The inference protocol is exaclty the same as any `T5` model, please have a look at the [T5's documentation page](t5) for more details.
+The inference protocol is exactly the same as any `T5` model, please have a look at the [T5's documentation page](t5) for more details.

docs/source/en/model_doc/jukebox.md

@@ -28,7 +28,7 @@ The abstract from the paper is the following:
 
 As shown on the following figure, Jukebox is made of 3 `priors` which are decoder only models. They follow the architecture described in [Generating Long Sequences with Sparse Transformers](https://arxiv.org/abs/1904.10509), modified to support longer context length.
 First, a autoencoder is used to encode the text lyrics. Next, the first (also called `top_prior`) prior attends to the last hidden states extracted from the lyrics encoder. The priors are linked to the previous priors respectively via an `AudioConditionner` module. The`AudioConditioner` upsamples the outputs of the previous prior to raw tokens at a certain audio frame per second resolution. 
-The metadata such as *artist, genre and timing* are passed to each prior, in the form of a start token and positionnal embedding for the timing data.  The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.
+The metadata such as *artist, genre and timing* are passed to each prior, in the form of a start token and positional embedding for the timing data.  The hidden states are mapped to the closest codebook vector from the VQVAE in order to convert them to raw audio.
 
 ![JukeboxModel](https://gist.githubusercontent.com/ArthurZucker/92c1acaae62ebf1b6a951710bdd8b6af/raw/c9c517bf4eff61393f6c7dec9366ef02bdd059a3/jukebox.svg)
 
@@ -36,7 +36,7 @@ Tips:
 - This model only supports inference. This is for a few reasons, mostly because it requires a crazy amount of memory to train. Feel free to open a PR and add what's missing to have a full integration with the hugging face traineer!
 - This model is very slow, and takes 8h to generate a minute long audio using the 5b top prior on a V100 GPU. In order automaticallay handle the device on which the model should execute, use `accelerate`.
 - Contrary to the paper, the order of the priors goes from `0` to `1` as it felt more intuitive : we sample starting from `0`.
-- Primed sampling (conditionning the sampling on raw audio) requires more memory than ancestral sampling and should be used with `fp16` set to `True`.
+- Primed sampling (conditioning the sampling on raw audio) requires more memory than ancestral sampling and should be used with `fp16` set to `True`.
 
 This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ).
 The original code can be found [here](https://github.com/openai/jukebox).

docs/source/en/model_doc/mistral.md

@@ -55,7 +55,7 @@ These ready-to-use checkpoints can be downloaded and used via the HuggingFace Hu
 
 >>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
 >>> tokenizer.batch_decode(generated_ids)[0]
-"The expected outupt"
+"The expected output"
 ```
 
 Raw weights for `Mistral-7B-v0.1` and `Mistral-7B-Instruct-v0.1` can be downloaded from:
@@ -109,7 +109,7 @@ To load and run a model using Flash Attention 2, refer to the snippet below:
 
 >>> generated_ids = model.generate(**model_inputs, max_new_tokens=100, do_sample=True)
 >>> tokenizer.batch_decode(generated_ids)[0]
-"The expected outupt"
+"The expected output"
 ```
 
 ### Expected speedups

docs/source/en/model_doc/mra.md

@@ -22,7 +22,7 @@ The MRA model was proposed in [Multi Resolution Analysis (MRA) for Approximate S
 
 The abstract from the paper is the following:
 
-*Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention.*
+*Transformers have emerged as a preferred model for many tasks in natural language processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, low-rank basis expansions and combinations thereof. In this paper, we revisit classical Multiresolution Analysis (MRA) concepts such as Wavelets, whose potential value in this setting remains underexplored thus far. We show that simple approximations based on empirical feedback and design choices informed by modern hardware and implementation challenges, eventually yield a MRA-based approach for self-attention with an excellent performance profile across most criteria of interest. We undertake an extensive set of experiments and demonstrate that this multi-resolution scheme outperforms most efficient self-attention proposals and is favorable for both short and long sequences. Code is available at https://github.com/mlpen/mra-attention.*
 
 This model was contributed by [novice03](https://huggingface.co/novice03).
 The original code can be found [here](https://github.com/mlpen/mra-attention).

docs/source/en/model_doc/nllb-moe.md

@@ -53,7 +53,7 @@ which means that tokens have less probability of being forwarded. Moreover, if a
 states (kind of like a residual connection) while they are masked in `NLLB`'s top-2 routing mechanism. 
 
 ## Generating with NLLB-MoE
-The avalable checkpoints requires around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
+The available checkpoints require around 350GB of storage. Make sure to use `accelerate` if you do not have enough RAM on your machine.
 
 While generating the target text set the `forced_bos_token_id` to the target language id. The following
 example shows how to translate English to French using the *facebook/nllb-200-distilled-600M* model.