text: It is broken into two parts: installation and setup, and then references to specific Hugging Face wrappers.Installation and Setupâ€‹If you want to work with the Hugging Face Hub:Install the Hub client library with pip install huggingface_hubCreate a Hugging Face account (it's free!)Create an access token and set it as an environment variable (HUGGINGFACEHUB_API_TOKEN)If you want work with the Hugging Face Python libraries:Install pip install transformers for working with models and tokenizersInstall pip install datasets for working with datasetsWrappersâ€‹LLMâ€‹There exists two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub.
  Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generationTo use the local pipeline wrapper:from langchain.llms import HuggingFacePipelineTo use a the wrapper for a model hosted on Hugging Face Hub:from langchain.llms import HuggingFaceHubFor a more detailed walkthrough of the Hugging Face Hub wrapper, see this notebookEmbeddingsâ€‹There exists two Hugging Face Embeddings wrappers, one for a local model and one for a model hosted on Hugging Face Hub.
  Note that these wrappers only work for sentence-transformers models.To use the local pipeline wrapper:from langchain.embeddings import HuggingFaceEmbeddingsTo use a the wrapper for a model hosted on Hugging Face Hub:from langchain.embeddings import HuggingFaceHubEmbeddingsFor a more detailed walkthrough of this, see this notebookTokenizerâ€‹There are several places you can use tokenizers available through the transformers package.