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This 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 langchain



There exists a NIBittensor LLM wrapper, which you can access with:

from langchain_community.llms import NIBittensorLLM

It 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)