Remove benchmarks from hook API (#743)

## Description

As in the title & moving section `Benchmark` on the sidebar up, so the
benchmarks will be visible immediately.

### Introduces a breaking change?

- [ ] Yes
- [x] No

### Type of change

- [ ] Bug fix (change which fixes an issue)
- [ ] New feature (change which adds functionality)
- [x] Documentation update (improves or adds clarity to existing
documentation)
- [ ] Other (chores, tests, code style improvements etc.)

### Tested on

- [ ] iOS
- [ ] Android

### Testing instructions

<!-- Provide step-by-step instructions on how to test your changes.
Include setup details if necessary. -->

### Screenshots

<!-- Add screenshots here, if applicable -->

### Related issues

<!-- Link related issues here using #issue-number -->

### Checklist

- [ ] I have performed a self-review of my code
- [ ] I have commented my code, particularly in hard-to-understand areas
- [x] I have updated the documentation accordingly
- [ ] My changes generate no new warnings

### Additional notes

<!-- Include any additional information, assumptions, or context that
reviewers might need to understand this PR. -->

Author: msluszniak

docs/docs/01-fundamentals/03-frequently-asked-questions.md

@@ -10,11 +10,11 @@ Each hook documentation subpage (useClassification, useLLM, etc.) contains a sup
 
 ### How can I run my own AI model?
 
-To run your own model, you need to directly access the underlying [ExecuTorch Module API](https://pytorch.org/executorch/stable/extension-module.html). We provide an experimental [React hook](../02-hooks/03-executorch-bindings/useExecutorchModule.md) along with a [TypeScript alternative](../03-typescript-api/03-executorch-bindings/ExecutorchModule.md), which serve as a way to use the aforementioned API without the need of diving into native code. In order to get a model in a format runnable by the runtime, you'll need to get your hands dirty with some ExecuTorch knowledge. For more guides on exporting models, please refer to the [ExecuTorch tutorials](https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html). Once you obtain your model in a `.pte` format, you can run it with `useExecuTorchModule` and `ExecuTorchModule`.
+To run your own model, you need to directly access the underlying [ExecuTorch Module API](https://pytorch.org/executorch/stable/extension-module.html). We provide an experimental [React hook](../03-hooks/03-executorch-bindings/useExecutorchModule.md) along with a [TypeScript alternative](../04-typescript-api/03-executorch-bindings/ExecutorchModule.md), which serve as a way to use the aforementioned API without the need of diving into native code. In order to get a model in a format runnable by the runtime, you'll need to get your hands dirty with some ExecuTorch knowledge. For more guides on exporting models, please refer to the [ExecuTorch tutorials](https://pytorch.org/executorch/stable/tutorials/export-to-executorch-tutorial.html). Once you obtain your model in a `.pte` format, you can run it with `useExecuTorchModule` and `ExecuTorchModule`.
 
 ### Can you do function calling with useLLM?
 
-If your model supports tool calling (i.e. its chat template can process tools) you can use the method explained on the [useLLM page](../02-hooks/01-natural-language-processing/useLLM.md).
+If your model supports tool calling (i.e. its chat template can process tools) you can use the method explained on the [useLLM page](../03-hooks/01-natural-language-processing/useLLM.md).
 
 If your model doesn't support it, you can still work around it using context. For details, refer to [this comment](https://github.com/software-mansion/react-native-executorch/issues/173#issuecomment-2775082278).
 

docs/docs/04-benchmarks/_category_.json docs/docs/02-benchmarks/_category_.jsondocs/docs/04-benchmarks/_category_.json renamed to docs/docs/02-benchmarks/_category_.json

@@ -498,40 +498,3 @@ Depending on selected model and the user's device generation speed can be above
 | [Phi 4 Mini](https://huggingface.co/software-mansion/react-native-executorch-phi-4-mini) |        4B        |    ✅     |
 | [SmolLM 2](https://huggingface.co/software-mansion/react-native-executorch-smolLm-2)     | 135M, 360M, 1.7B |    ✅     |
 | [LLaMA 3.2](https://huggingface.co/software-mansion/react-native-executorch-llama-3.2)   |      1B, 3B      |    ✅     |
-
-## Benchmarks
-
-### Model size
-
-| Model                 | XNNPACK [GB] |
-| --------------------- | :----------: |
-| LLAMA3_2_1B           |     2.47     |
-| LLAMA3_2_1B_SPINQUANT |     1.14     |
-| LLAMA3_2_1B_QLORA     |     1.18     |
-| LLAMA3_2_3B           |     6.43     |
-| LLAMA3_2_3B_SPINQUANT |     2.55     |
-| LLAMA3_2_3B_QLORA     |     2.65     |
-
-### Memory usage
-
-| Model                 | Android (XNNPACK) [GB] | iOS (XNNPACK) [GB] |
-| --------------------- | :--------------------: | :----------------: |
-| LLAMA3_2_1B           |          3.2           |        3.1         |
-| LLAMA3_2_1B_SPINQUANT |          1.9           |         2          |
-| LLAMA3_2_1B_QLORA     |          2.2           |        2.5         |
-| LLAMA3_2_3B           |          7.1           |        7.3         |
-| LLAMA3_2_3B_SPINQUANT |          3.7           |        3.8         |
-| LLAMA3_2_3B_QLORA     |           4            |        4.1         |
-
-### Inference time
-
-| Model                 | iPhone 16 Pro (XNNPACK) [tokens/s] | iPhone 13 Pro (XNNPACK) [tokens/s] | iPhone SE 3 (XNNPACK) [tokens/s] | Samsung Galaxy S24 (XNNPACK) [tokens/s] | OnePlus 12 (XNNPACK) [tokens/s] |
-| --------------------- | :--------------------------------: | :--------------------------------: | :------------------------------: | :-------------------------------------: | :-----------------------------: |
-| LLAMA3_2_1B           |                16.1                |                11.4                |                ❌                |                  15.6                   |              19.3               |
-| LLAMA3_2_1B_SPINQUANT |                40.6                |                16.7                |               16.5               |                  40.3                   |              48.2               |
-| LLAMA3_2_1B_QLORA     |                31.8                |                11.4                |               11.2               |                  37.3                   |              44.4               |
-| LLAMA3_2_3B           |                 ❌                 |                 ❌                 |                ❌                |                   ❌                    |               7.1               |
-| LLAMA3_2_3B_SPINQUANT |                17.2                |                8.2                 |                ❌                |                  16.2                   |              19.4               |
-| LLAMA3_2_3B_QLORA     |                14.5                |                 ❌                 |                ❌                |                  14.8                   |              18.1               |
-
-❌ - Insufficient RAM.

docs/docs/04-benchmarks/inference-time.md docs/docs/02-benchmarks/inference-time.mddocs/docs/04-benchmarks/inference-time.md renamed to docs/docs/02-benchmarks/inference-time.md

@@ -322,22 +322,3 @@ function App() {
 | [whisper-base](https://huggingface.co/openai/whisper-base)         | Multilingual |
 | [whisper-small.en](https://huggingface.co/openai/whisper-small.en) |   English    |
 | [whisper-small](https://huggingface.co/openai/whisper-small)       | Multilingual |
-
-## Benchmarks
-
-### Model size
-
-| Model            | XNNPACK [MB] |
-| ---------------- | :----------: |
-| WHISPER_TINY_EN  |     151      |
-| WHISPER_TINY     |     151      |
-| WHISPER_BASE_EN  |    290.6     |
-| WHISPER_BASE     |    290.6     |
-| WHISPER_SMALL_EN |     968      |
-| WHISPER_SMALL    |     968      |
-
-### Memory usage
-
-| Model        | Android (XNNPACK) [MB] | iOS (XNNPACK) [MB] |
-| ------------ | :--------------------: | :----------------: |
-| WHISPER_TINY |          410           |        375         |

docs/docs/04-benchmarks/memory-usage.md docs/docs/02-benchmarks/memory-usage.mddocs/docs/04-benchmarks/memory-usage.md renamed to docs/docs/02-benchmarks/memory-usage.md

@@ -116,43 +116,3 @@ function App() {
 :::info
 For the supported models, the returned embedding vector is normalized, meaning that its length is equal to 1. This allows for easier comparison of vectors using cosine similarity, just calculate the dot product of two vectors to get the cosine similarity score.
 :::
-
-## Benchmarks
-
-### Model size
-
-| Model                      | XNNPACK [MB] |
-| -------------------------- | :----------: |
-| ALL_MINILM_L6_V2           |      91      |
-| ALL_MPNET_BASE_V2          |     438      |
-| MULTI_QA_MINILM_L6_COS_V1  |      91      |
-| MULTI_QA_MPNET_BASE_DOT_V1 |     438      |
-| CLIP_VIT_BASE_PATCH32_TEXT |     254      |
-
-### Memory usage
-
-| Model                      | Android (XNNPACK) [MB] | iOS (XNNPACK) [MB] |
-| -------------------------- | :--------------------: | :----------------: |
-| ALL_MINILM_L6_V2           |           95           |        110         |
-| ALL_MPNET_BASE_V2          |          405           |        455         |
-| MULTI_QA_MINILM_L6_COS_V1  |          120           |        140         |
-| MULTI_QA_MPNET_BASE_DOT_V1 |          435           |        455         |
-| CLIP_VIT_BASE_PATCH32_TEXT |          200           |        280         |
-
-### Inference time
-
-:::warning
-Times presented in the tables are measured as consecutive runs of the model. Initial run times may be up to 2x longer due to model loading and initialization.
-:::
-
-| Model                      | iPhone 17 Pro (XNNPACK) [ms] | OnePlus 12 (XNNPACK) [ms] |
-| -------------------------- | :--------------------------: | :-----------------------: |
-| ALL_MINILM_L6_V2           |              7               |            21             |
-| ALL_MPNET_BASE_V2          |              24              |            90             |
-| MULTI_QA_MINILM_L6_COS_V1  |              7               |            19             |
-| MULTI_QA_MPNET_BASE_DOT_V1 |              24              |            88             |
-| CLIP_VIT_BASE_PATCH32_TEXT |              14              |            39             |
-
-:::info
-Benchmark times for text embeddings are highly dependent on the sentence length. The numbers above are based on a sentence of around 80 tokens. For shorter or longer sentences, inference time may vary accordingly.
-:::