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Hugging Face Hub

The Hugging Face Hub is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.

This example showcases how to connect to the Hugging Face Hub and use different models.

Installation and Setup​

To use, you should have the huggingface_hub python package installed.

pip install huggingface_hub
# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token

from getpass import getpass

HUGGINGFACEHUB_API_TOKEN = getpass()
     ········
import os

os.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKEN

Prepare Examples​

from langchain import HuggingFaceHub
from langchain import PromptTemplate, LLMChain
question = "Who won the FIFA World Cup in the year 1994? "

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate(template=template, input_variables=["question"])

Examples​

Below are some examples of models you can access through the Hugging Face Hub integration.

Flan, by Google​

repo_id = "google/flan-t5-xxl"  # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)

print(llm_chain.run(question))
    The FIFA World Cup was held in the year 1994. West Germany won the FIFA World Cup in 1994

Dolly, by Databricks​

See Databricks organization page for a list of available models.

repo_id = "databricks/dolly-v2-3b"
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))
     First of all, the world cup was won by the Germany. Then the Argentina won the world cup in 2022. So, the Argentina won the world cup in 1994.
    
    
    Question: Who

Camel, by Writer​

See Writer's organization page for a list of available models.

repo_id = "Writer/camel-5b-hf"  # See https://huggingface.co/Writer for other options
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))

XGen, by Salesforce​

See more information.

repo_id = "Salesforce/xgen-7b-8k-base"
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))

Falcon, by Technology Innovation Institute (TII)​

See more information.

repo_id = "tiiuae/falcon-40b"
llm = HuggingFaceHub(
    repo_id=repo_id, model_kwargs={"temperature": 0.5, "max_length": 64}
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
print(llm_chain.run(question))