text: # Replace with remote host if you are running on a remote serverllm = OpenLLM(server_url=server_url)Optional: Local LLM Inferenceâ€‹You may also choose to initialize an LLM managed by OpenLLM locally from current process. This is useful for development purpose and allows developers to quickly try out different types of LLMs.When moving LLM applications to production, we recommend deploying the OpenLLM server separately and access via the server_url option demonstrated above.To load an LLM locally via the LangChain wrapper:from langchain.llms import OpenLLMllm = OpenLLM(    model_name="dolly-v2",    model_id="databricks/dolly-v2-3b",    temperature=0.94,    repetition_penalty=1.2,)Integrate with a LLMChainâ€‹from langchain import PromptTemplate, LLMChaintemplate = "What is a good name for a company that makes {product}?"prompt = PromptTemplate(template=template, input_variables=["product"])llm_chain = LLMChain(prompt=prompt, llm=llm)generated = llm_chain.run(product="mechanical keyboard")print(generated)    iLkbPreviousOpenAINextOpenLMInstallationLaunch OpenLLM server locallyWrapperOptional: Local LLM InferenceIntegrate with a LLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.