before, if desired.from langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type(    llm,    retriever=vectorstore.as_retriever(),    chain_type_kwargs={"prompt": rag_prompt_llama},)qa_chain({"query": question})    Llama.generate: prefix-match hit      Sure! Based on the context, here's my answer to your:        There are several to task,:        1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"    2. Task-specific, like "Write a story outline" for writing a novel.    3. Human inputs to guide the process.        These can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =   139.20 ms /   200 runs   (    0.70 ms per token,  1436.76 tokens per second)    llama_print_timings: prompt eval time =  1532.26 ms /   258 tokens (    5.94 ms per token,   168.38 tokens per second)    llama_print_timings:        eval time =  5977.62 ms /   199 runs   (   30.04 ms per token,    33.29 tokens per second)    llama_print_timings:       total time =  7916.21 ms    {'query': 'What are the approaches to Task Decomposition?',     'result': '  Sure! Based on the context, here\'s my answer to your:\n\nThere are several to task,:\n\n1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"\n2. Task-specific, like "Write a story outline" for writing a novel.\n3. Human inputs to guide the process.\n\nThese can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task
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. ->: before, if desired.from langchain.chains import RetrievalQAqa_chain = RetrievalQA.from_chain_type(    llm,    retriever=vectorstore.as_retriever(),    chain_type_kwargs={"prompt": rag_prompt_llama},)qa_chain({"query": question})    Llama.generate: prefix-match hit      Sure! Based on the context, here's my answer to your:        There are several to task,:        1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"    2. Task-specific, like "Write a story outline" for writing a novel.    3. Human inputs to guide the process.        These can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =   139.20 ms /   200 runs   (    0.70 ms per token,  1436.76 tokens per second)    llama_print_timings: prompt eval time =  1532.26 ms /   258 tokens (    5.94 ms per token,   168.38 tokens per second)    llama_print_timings:        eval time =  5977.62 ms /   199 runs   (   30.04 ms per token,    33.29 tokens per second)    llama_print_timings:       total time =  7916.21 ms    {'query': 'What are the approaches to Task Decomposition?',     'result': '  Sure! Based on the context, here\'s my answer to your:\n\nThere are several to task,:\n\n1. LLM-based with simple prompting, such as "Steps for XYZ" or "What are the subgoals for achieving XYZ?"\n2. Task-specific, like "Write a story outline" for writing a novel.\n3. Human inputs to guide the process.\n\nThese can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task