ProvidersMoreNIBittensorOn this pageNIBittensorThis page covers how to use the BittensorLLM inference runtime within LangChain.
It is broken into two parts: installation and setup, and then examples of NIBittensorLLM usage.Installation and Setup​Install the Python package with pip install langchainWrappers​LLM​There exists a NIBittensor LLM wrapper, which you can access with:from langchain.llms import NIBittensorLLMIt provides a unified interface for all models:llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt")print(llm('Write a fibonacci function in python with golder ratio'))Multiple responses from top miners can be accessible using the top_responses parameter:multi_response_llm = NIBittensorLLM(top_responses=10)multi_resp = multi_response_llm("What is Neural Network Feeding Mechanism?")json_multi_resp = json.loads(multi_resp)print(json_multi_resp)PreviousBiliBiliNextBlackboardInstallation and SetupWrappersLLM