{question} [/INST]', template_format='f-string', validate_template=True)# Chainllm_chain = LLMChain(prompt=prompt,llm=llm)question = "What NFL team won the Super Bowl in the year that Justin Bieber was born?"llm_chain.run({"question":question})      Sure! Here are three similar search queries with a question mark at the end:        1. Which NBA team did LeBron James lead to a championship in the year he was drafted?    2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?    3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?        llama_print_timings:        load time = 14943.19 ms    llama_print_timings:      sample time =    72.93 ms /   101 runs   (    0.72 ms per token,  1384.87 tokens per second)    llama_print_timings: prompt eval time = 14942.95 ms /    93 tokens (  160.68 ms per token,     6.22 tokens per second)    llama_print_timings:        eval time =  3430.85 ms /   100 runs   (   34.31 ms per token,    29.15 tokens per second)    llama_print_timings:       total time = 18578.26 ms    '  Sure! Here are three similar search queries with a question mark at the end:\n\n1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\n2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\n3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?'We also can use the LangChain Prompt Hub to fetch and / or store prompts that are model specific.This will work with your LangSmith API key.For example, here is a prompt for RAG with LLaMA-specific tokens.Use cases‚ÄãGiven an llm created from one of the models above, you can use it for many use cases.For example, here is a guide to RAG with local LLMs.In general, use cases for local LLMs can be driven by at least two factors:Privacy: private data (e.g., journals, etc) that a user does
Use case
Use case ->: {question} [/INST]', template_format='f-string', validate_template=True)# Chainllm_chain = LLMChain(prompt=prompt,llm=llm)question = "What NFL team won the Super Bowl in the year that Justin Bieber was born?"llm_chain.run({"question":question})      Sure! Here are three similar search queries with a question mark at the end:        1. Which NBA team did LeBron James lead to a championship in the year he was drafted?    2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?    3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?        llama_print_timings:        load time = 14943.19 ms    llama_print_timings:      sample time =    72.93 ms /   101 runs   (    0.72 ms per token,  1384.87 tokens per second)    llama_print_timings: prompt eval time = 14942.95 ms /    93 tokens (  160.68 ms per token,     6.22 tokens per second)    llama_print_timings:        eval time =  3430.85 ms /   100 runs   (   34.31 ms per token,    29.15 tokens per second)    llama_print_timings:       total time = 18578.26 ms    '  Sure! Here are three similar search queries with a question mark at the end:\n\n1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\n2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\n3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?'We also can use the LangChain Prompt Hub to fetch and / or store prompts that are model specific.This will work with your LangSmith API key.For example, here is a prompt for RAG with LLaMA-specific tokens.Use cases‚ÄãGiven an llm created from one of the models above, you can use it for many use cases.For example, here is a guide to RAG with local LLMs.In general, use cases for local LLMs can be driven by at least two factors:Privacy: private data (e.g., journals, etc) that a user does