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 LLMChain‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import 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 ¬© 2023 LangChain, Inc.
ü¶æ OpenLLM is an open platform for operating large language models (LLMs) in production. It enables developers to easily run inference with any open-source LLMs, deploy to the cloud or on-premises, and build powerful AI apps.
ü¶æ OpenLLM is an open platform for operating large language models (LLMs) in production. It enables developers to easily run inference with any open-source LLMs, deploy to the cloud or on-premises, and build powerful AI apps. ->: 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 LLMChain‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import 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 ¬© 2023 LangChain, Inc.