fix typos in the docs directory (#36639)

* chore: fix typos in the docs directory

* chore: fix typos in the docs directory

* chore: fix typos in the docs directory

docs/source/en/model_doc/depth_pro.md

@@ -90,7 +90,7 @@ The `DepthProEncoder` further uses two encoders:
 - `image_encoder`
    - Input image is also rescaled to `patch_size` and processed by the **`image_encoder`**
 
-Both these encoders can be configured via `patch_model_config` and `image_model_config` respectively, both of which are seperate `Dinov2Model` by default.
+Both these encoders can be configured via `patch_model_config` and `image_model_config` respectively, both of which are separate `Dinov2Model` by default.
 
 Outputs from both encoders (`last_hidden_state`) and selected intermediate states (`hidden_states`) from **`patch_encoder`** are fused by a `DPT`-based `FeatureFusionStage` for depth estimation.
 

docs/source/en/model_doc/llava_next_video.md

@@ -36,7 +36,7 @@ On January 30, 2024, we released LLaVA-NeXT, an open-source Large Multimodal Mod
 
 **In today’s exploration, we delve into the performance of LLaVA-NeXT within the realm of video understanding tasks. We reveal that LLaVA-NeXT surprisingly has strong performance in understanding video content. The current version of LLaVA-NeXT for videos has several improvements:
 
-- Zero-shot video representation capabilities with AnyRes: The AnyRes technique naturally represents a high-resolution image into multiple images that a pre-trained VIT is able to digest, and forms them into a concantenated sequence. This technique is naturally generalizable to represent videos (consisting of multiple frames), allowing the image-only-trained LLaVA-Next model to perform surprisingly well on video tasks. Notably, this is the first time that LMMs show strong zero-shot modality transfer ability.
+- Zero-shot video representation capabilities with AnyRes: The AnyRes technique naturally represents a high-resolution image into multiple images that a pre-trained VIT is able to digest, and forms them into a concatenated sequence. This technique is naturally generalizable to represent videos (consisting of multiple frames), allowing the image-only-trained LLaVA-Next model to perform surprisingly well on video tasks. Notably, this is the first time that LMMs show strong zero-shot modality transfer ability.
 - Inference with length generalization improves on longer videos. The linear scaling technique enables length generalization, allowing LLaVA-NeXT to effectively handle long-video beyond the limitation of the "max_token_length" of the LLM.
 - Strong video understanding ability. (1) LLaVA-Next-Image, which combines the above two techniques, yields superior zero-shot performance than open-source LMMs tuned on videos. (2) LLaVA-Next-Video, further supervised fine-tuning (SFT) LLaVA-Next-Image on video data, achieves better video understanding capabilities compared to LLaVA-Next-Image. (3) LLaVA-Next-Video-DPO, which aligns the model response with AI feedback using direct preference optimization (DPO), showing significant performance boost.
 - Efficient deployment and inference with SGLang. It allows 5x faster inference on video tasks, allowing more scalable serving such as million-level video re-captioning. See instructions in our repo.**