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docs/source/basic_tutorials/visual_language_models.md
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# Vision Language Models (VLM)
# Vision Language Model Inference in TGI
Visual Language Model (VLM) are models that consume both image and text inputs to generate text.
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### Hugging Face Hub Python Library
To infer with vision language models through Python, you can use the [`huggingface_hub`](https://pypi.org/project/huggingface-hub/) library. The `InferenceClient` class provides a simple way to interact with the [Inference API](https://huggingface.co/docs/api-inference/index)
To infer with vision language models through Python, you can use the [`huggingface_hub`](https://pypi.org/project/huggingface-hub/) library. The `InferenceClient` class provides a simple way to interact with the [Inference API](https://huggingface.co/docs/api-inference/index). Images can be passed as URLs or base64-encoded strings. The `InferenceClient` will automatically detect the image format.
```python
from huggingface_hub import InferenceClient
@ -31,8 +31,6 @@ for token in client.text_generation(prompt, max_new_tokens=16, stream=True):
# This is a picture of an anthropomorphic rabbit in a space suit.
```
Images can be passed as URLs or base64-encoded strings. The `InferenceClient` will automatically detect the image format.
```python
from huggingface_hub import InferenceClient
import base64