ComponentsLLMsGPT4AllOn this pageGPT4AllGitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.This example goes over how to use LangChain to interact with GPT4All models.%pip install gpt4all > /dev/null    Note: you may need to restart the kernel to use updated packages.Import GPT4All​from langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import GPT4Allfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandlerSet Up Question to pass to LLM​template = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])Specify Model​To run locally, download a compatible ggml-formatted model. The gpt4all page has a useful Model Explorer section:Select a model of interestDownload using the UI and move the .bin to the local_path (noted below)For more info, visit https://github.com/nomic-ai/gpt4all.local_path = (    "./models/ggml-gpt4all-l13b-snoozy.bin"  # replace with your desired local file path)# Callbacks support token-wise streamingcallbacks = [StreamingStdOutCallbackHandler()]# Verbose is required to pass to the callback managerllm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)# If you want to use a custom model add the backend parameter# Check https://docs.gpt4all.io/gpt4all_python.html for supported backendsllm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, verbose=True)llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"llm_chain.run(question)Justin Bieber was born on March 1, 1994. In 1994, The Cowboys won Super Bowl XXVIII.PreviousGooseAINextGradientImport GPT4AllSet Up Question to pass to LLMSpecify Model