ComponentsLLMsHugging Face HubOn this pageHugging Face HubThe 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-tokenfrom getpass import getpassHUGGINGFACEHUB_API_TOKEN = getpass()     ········import osos.environ["HUGGINGFACEHUB_API_TOKEN"] = HUGGINGFACEHUB_API_TOKENPrepare Examples​from langchain.llms import HuggingFaceHubfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainquestion = "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 optionsllm = 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 1994Dolly, 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: WhoCamel, 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 optionsllm = 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))InternLM-Chat, by Shanghai AI Laboratory​See more information.repo_id = "internlm/internlm-chat-7b"llm = HuggingFaceHub(    repo_id=repo_id, model_kwargs={"max_length": 128, "temperature": 0.8})llm_chain = LLMChain(prompt=prompt, llm=llm)print(llm_chain.run(question))Qwen, by Alibaba Cloud​Tongyi Qianwen-7B (Qwen-7B) is a model with a scale of 7 billion parameters in the Tongyi Qianwen large model series developed by Alibaba Cloud. Qwen-7B is a large language model based on Transformer, which is trained on ultra-large-scale pre-training data.See more information on HuggingFace of on GitHub.See here a big example for LangChain integration and Qwen.repo_id = "Qwen/Qwen-7B"llm = HuggingFaceHub(    repo_id=repo_id, model_kwargs={"max_length": 128, "temperature": 0.5})llm_chain = LLMChain(prompt=prompt, llm=llm)print(llm_chain.run(question))PreviousGradientNextHugging Face Local PipelinesInstallation and SetupPrepare ExamplesExamplesFlan, by GoogleDolly, by DatabricksCamel, by WriterXGen, by SalesforceFalcon, by Technology Innovation Institute (TII)InternLM-Chat, by Shanghai AI LaboratoryQwen, by Alibaba Cloud