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docs: Fix typos (#1943)

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Fix typos

Signed-off-by: stephencox-ict <scox@ict.co>
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docs/usage/vision_models.md vendored
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@ -1,5 +1,5 @@
The `VlmPipeline` in Docling allows to convert documents end-to-end using a vision-language model.
The `VlmPipeline` in Docling allows you to convert documents end-to-end using a vision-language model.
Docling supports vision-language models which output:
@ -39,7 +39,7 @@ For running Docling using local models with the `VlmPipeline`:
## Available local models
By default, the vision-language models are running locally.
Docling allows to choose between the Hugging Face [Transformers](https://github.com/huggingface/transformers) framweork and the [MLX](https://github.com/Blaizzy/mlx-vlm) (for Apple devices with MPS acceleration) one.
Docling allows to choose between the Hugging Face [Transformers](https://github.com/huggingface/transformers) framework and the [MLX](https://github.com/Blaizzy/mlx-vlm) (for Apple devices with MPS acceleration) one.
The following table reports the models currently available out-of-the-box.
@ -54,7 +54,7 @@ The following table reports the models currently available out-of-the-box.
| `vlm_model_specs.PHI4_TRANSFORMERS` | [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) | `Transformers/AutoModelForCasualLM` | CPU | 1 | 1175.67 |
| `vlm_model_specs.PIXTRAL_12B_TRANSFORMERS` | [mistral-community/pixtral-12b](https://huggingface.co/mistral-community/pixtral-12b) | `Transformers/AutoModelForVision2Seq` | CPU | 1 | 1828.21 |
_Inference time is computed on a Macbook M3 Max using the example page `tests/data/pdf/2305.03393v1-pg9.pdf`. The comparision is done with the example [compare_vlm_models.py](./../examples/compare_vlm_models.py)._
_Inference time is computed on a Macbook M3 Max using the example page `tests/data/pdf/2305.03393v1-pg9.pdf`. The comparison is done with the example [compare_vlm_models.py](./../examples/compare_vlm_models.py)._
For choosing the model, the code snippet above can be extended as follow