import NIBittensorLLMimport jsonfrom pprint import pprintfrom langchain.globals import set_debugset_debug(True)# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with modelllm_sys = NIBittensorLLM(    system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project")sys_resp = llm_sys(    "What is bittensor and What are the potential benefits of decentralized AI?")print(f"Response provided by LLM with system prompt set is : {sys_resp}")# The top_responses parameter can give multiple responses based on its parameter value# This below code retrive top 10 miner's response all the response are in format of json# Json response structure is""" {    "choices":  [                    {"index": Bittensor's Metagraph index number,                    "uid": Unique Identifier of a miner,                    "responder_hotkey": Hotkey of a miner,                    "message":{"role":"assistant","content": Contains actual response},                    "response_ms": Time in millisecond required to fetch response from a miner}                 ]    } """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)pprint(json_multi_resp)Using NIBittensorLLM with LLMChain and PromptTemplate‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import NIBittensorLLMfrom langchain.globals import set_debugset_debug(True)template = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with modelllm = NIBittensorLLM(system_prompt="Your task is to determine response based on user prompt.")llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What is
Bittensor is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge.
Bittensor is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge. ->: import NIBittensorLLMimport jsonfrom pprint import pprintfrom langchain.globals import set_debugset_debug(True)# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with modelllm_sys = NIBittensorLLM(    system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project")sys_resp = llm_sys(    "What is bittensor and What are the potential benefits of decentralized AI?")print(f"Response provided by LLM with system prompt set is : {sys_resp}")# The top_responses parameter can give multiple responses based on its parameter value# This below code retrive top 10 miner's response all the response are in format of json# Json response structure is""" {    "choices":  [                    {"index": Bittensor's Metagraph index number,                    "uid": Unique Identifier of a miner,                    "responder_hotkey": Hotkey of a miner,                    "message":{"role":"assistant","content": Contains actual response},                    "response_ms": Time in millisecond required to fetch response from a miner}                 ]    } """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)pprint(json_multi_resp)Using NIBittensorLLM with LLMChain and PromptTemplate‚Äãfrom langchain.prompts import PromptTemplatefrom langchain.chains import LLMChainfrom langchain.llms import NIBittensorLLMfrom langchain.globals import set_debugset_debug(True)template = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])# System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with modelllm = NIBittensorLLM(system_prompt="Your task is to determine response based on user prompt.")llm_chain = LLMChain(prompt=prompt, llm=llm)question = "What is