they're coming from. Like minded people can build a tribe together.Serializing an LLM‚ÄãYou can also save and load LLM configurationsfrom langchain.llms.loading import load_llmfrom langchain.llms.azureml_endpoint import AzureMLEndpointClientsave_llm = AzureMLOnlineEndpoint(    deployment_name="databricks-dolly-v2-12b-4",    model_kwargs={        "temperature": 0.2,        "max_tokens": 150,        "top_p": 0.8,        "frequency_penalty": 0.32,        "presence_penalty": 72e-3,    },)save_llm.save("azureml.json")loaded_llm = load_llm("azureml.json")print(loaded_llm)    AzureMLOnlineEndpoint    Params: {'deployment_name': 'databricks-dolly-v2-12b-4', 'model_kwargs': {'temperature': 0.2, 'max_tokens': 150, 'top_p': 0.8, 'frequency_penalty': 0.32, 'presence_penalty': 0.072}}PreviousArceeNextAzure OpenAISet upContent FormatterCustom Content FormatterDolly with LLMChainSerializing an LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
Azure ML is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.
Azure ML is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML. ->: they're coming from. Like minded people can build a tribe together.Serializing an LLM‚ÄãYou can also save and load LLM configurationsfrom langchain.llms.loading import load_llmfrom langchain.llms.azureml_endpoint import AzureMLEndpointClientsave_llm = AzureMLOnlineEndpoint(    deployment_name="databricks-dolly-v2-12b-4",    model_kwargs={        "temperature": 0.2,        "max_tokens": 150,        "top_p": 0.8,        "frequency_penalty": 0.32,        "presence_penalty": 72e-3,    },)save_llm.save("azureml.json")loaded_llm = load_llm("azureml.json")print(loaded_llm)    AzureMLOnlineEndpoint    Params: {'deployment_name': 'databricks-dolly-v2-12b-4', 'model_kwargs': {'temperature': 0.2, 'max_tokens': 150, 'top_p': 0.8, 'frequency_penalty': 0.32, 'presence_penalty': 0.072}}PreviousArceeNextAzure OpenAISet upContent FormatterCustom Content FormatterDolly with LLMChainSerializing an LLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.