Lucain Pouget PRO

Thanks for the ping @clem !

This documentation is more recent regarding HfApi (the Python client). You have methods like model_info and list_models to get details about models (and similarly with datasets and Spaces). In addition to the package reference, we also have a small guide on how to use it.

Otherwise, if you are interested in the HTTP endpoint to build your requests yourself, here is the API reference.

Depends what you want to do. We have full documentation here: https://huggingface.co/docs/huggingface_hub/index. You can find many guides showing you how to use the library.

Are you referring to Agents in transformers? If yes, here is the docs about it: https://huggingface.co/docs/transformers/agents. Regarding tools, TGI supports them and the InferenceClient from huggingface_hub as well, meaning you can pass tools to chat_completion (see "Example using tools:" section in https://huggingface.co/docs/huggingface_hub/v0.24.0/en/package_reference/inference_client#huggingface_hub.InferenceClient.chat_completion). These tools parameters were already available on huggingface_hub 0.23.x.

Hope this answers your question :)

Mostly that it's better integrated with HF services. If you pass a model_id you can use the serverless Inference API without setting an base_url. No need either to pass an api_key if you are already logged in (with $HF_TOKEN environment variable or huggingface-cli login). If you are an Inference Endpoint user (i.e. deploying a model using https://ui.endpoints.huggingface.co/), you get a seamless integration to make requests to it with URL already configured. Finally, you are assured that the client will stay up to date with latest updates in TGI/Inference API/Inference Endpoints.