text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerGPT4AllOn this pageGPT4AllThis page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.Installation and Setupâ€‹Install the Python package with pip install pyllamacppDownload a GPT4All model and place it in your desired directoryUsageâ€‹GPT4Allâ€‹To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.from langchain.llms import GPT4All# Instantiate the model. Callbacks support token-wise streamingmodel = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)# Generate textresponse = model("Once upon a time, ")You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.To stream the model's predictions, add in a CallbackManager.from langchain.llms import GPT4Allfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler# There are many CallbackHandlers supported, such as# from langchain.callbacks.streamlit import StreamlitCallbackHandlercallbacks = [StreamingStdOutCallbackHandler()]model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)# Generate text. Tokens are streamed through the callback manager.model("Once upon