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fix typos (#38336)

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* Update video_processor.md

* Update deepseek_v3.md
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Ragnar
2025-05-26 16:42:37 +02:00
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parent 8b03c8eaf2
commit 63964b7c67
2 changed files with 3 additions and 3 deletions
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docs/source/en/main_classes/video_processor.md
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@@ -21,7 +21,7 @@ A **Video Processor** is a utility responsible for preparing input features for
The video processor extends the functionality of image processors by allowing Vision Large Language Models (VLMs) to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM. The video processor extends the functionality of image processors by allowing Vision Large Language Models (VLMs) to handle videos with a distinct set of arguments compared to images. It serves as the bridge between raw video data and the model, ensuring that input features are optimized for the VLM.
When adding a new VLM or updating an existing one to enable distinct video preprocessing, saving and reloading the processor configuration will store the video related arguments in a dedicated file named `video_preprocessing_config.json`. Don't worry if you haven't upadted your VLM, the processor will try to load video related configurations from a file named `preprocessing_config.json`. When adding a new VLM or updating an existing one to enable distinct video preprocessing, saving and reloading the processor configuration will store the video related arguments in a dedicated file named `video_preprocessing_config.json`. Don't worry if you haven't updated your VLM, the processor will try to load video related configurations from a file named `preprocessing_config.json`.
### Usage Example ### Usage Example

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docs/source/en/model_doc/deepseek_v3.md
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@@ -28,8 +28,8 @@ We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 67
We are super happy to make this code community-powered, and would love to see how you can best optimize the following: We are super happy to make this code community-powered, and would love to see how you can best optimize the following:
- current implementation uses the "naive" attention compution (so not really MLA) - current implementation uses the "naive" attention compution (so not really MLA)
- current implementation loops through the experts. This should be replaced. Pointers to use `get_packed_weights` from `intetrations/tensor_parallel`. - current implementation loops through the experts. This should be replaced. Pointers to use `get_packed_weights` from `integrations/tensor_parallel`.
- current implementation uses the eleuther formula for ROPE, using the orginal one would be more efficient! (should still follow our API) - current implementation uses the eleuther formula for ROPE, using the original one would be more efficient! (should still follow our API)
- static cache is not supported (this should be just a generation config issue / config shape issues) - static cache is not supported (this should be just a generation config issue / config shape issues)
### Usage tips ### Usage tips