subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =    33.03 ms /    47 runs   (    0.70 ms per token,  1422.86 tokens per second)    llama_print_timings: prompt eval time =  1387.31 ms /   242 tokens (    5.73 ms per token,   174.44 tokens per second)    llama_print_timings:        eval time =  1321.62 ms /    46 runs   (   28.73 ms per token,    34.81 tokens per second)    llama_print_timings:       total time =  2801.08 ms    {'output_text': '\nTask can be done by down a task into smaller subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.'}Now, let's try with a prompt specifically for LLaMA, which includes special tokens.# Promptrag_prompt_llama = hub.pull("rlm/rag-prompt-llama")rag_prompt_llama    ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template="[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \nQuestion: {question} \nContext: {context} \nAnswer: [/INST]", template_format='f-string', validate_template=True), additional_kwargs={})])# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt_llama)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True)    Llama.generate: prefix-match hit      Sure, I'd be happy to help! Based on the context, here are some to task:        1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or
The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally.
The popularity of projects like PrivateGPT, llama.cpp, and GPT4All underscore the importance of running LLMs locally. ->: subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =    33.03 ms /    47 runs   (    0.70 ms per token,  1422.86 tokens per second)    llama_print_timings: prompt eval time =  1387.31 ms /   242 tokens (    5.73 ms per token,   174.44 tokens per second)    llama_print_timings:        eval time =  1321.62 ms /    46 runs   (   28.73 ms per token,    34.81 tokens per second)    llama_print_timings:       total time =  2801.08 ms    {'output_text': '\nTask can be done by down a task into smaller subtasks, using simple prompting like "Steps for XYZ." or task-specific like "Write a story outline" for writing a novel.'}Now, let's try with a prompt specifically for LLaMA, which includes special tokens.# Promptrag_prompt_llama = hub.pull("rlm/rag-prompt-llama")rag_prompt_llama    ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template="[INST]<<SYS>> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<</SYS>> \nQuestion: {question} \nContext: {context} \nAnswer: [/INST]", template_format='f-string', validate_template=True), additional_kwargs={})])# Chainchain = load_qa_chain(llm, chain_type="stuff", prompt=rag_prompt_llama)# Runchain({"input_documents": docs, "question": question}, return_only_outputs=True)    Llama.generate: prefix-match hit      Sure, I'd be happy to help! Based on the context, here are some to task:        1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or