This page contains reference documentation for Hugging Face. See the docs for conceptual guides, tutorials, and examples on using Hugging Face modules.
HuggingFace sentence_transformers embedding models.
To use, you should have the sentence_transformers python package installed.
HuggingFaceHub embedding models.
To use, you should have the huggingface_hub python package installed, and the
environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass
it as
Hugging Face Endpoint. This works with any model that supports text generation (i.e. text completion) task.
To use this class, you should have installed the huggingface_hub package, and
the environ
HuggingFace Pipeline API.
To use, you should have the transformers python package installed.
Only supports text-generation, text2text-generation, image-text-to-text,
summarization and `tra
Response from the TextGenInference API.
Message to send to the TextGenInference API.
Hugging Face LLM's as ChatModels.
Works with HuggingFaceTextGenInference, HuggingFaceEndpoint,
HuggingFaceHub, and HuggingFacePipeline LLMs.
Upon instantiating this class, the model_id is re
Hugging Face integration for LangChain.
Model profile data. All edits should be made in profile_augmentations.toml.
Hugging Face Chat Wrapper.
