(LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.    2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.    3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.        As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =   144.81 ms /   207 runs   (    0.70 ms per token,  1429.47 tokens per second)    llama_print_timings: prompt eval time =  1506.13 ms /   258 tokens (    5.84 ms per token,   171.30 tokens per second)    llama_print_timings:        eval time =  6231.92 ms /   206 runs   (   30.25 ms per token,    33.06 tokens per second)    llama_print_timings:       total time =  8158.41 ms    {'output_text': '  Sure, I\'d be happy to help! Based on the context, here are some to task:\n\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.\n2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}RetrievalQA‚ÄãFor an even simpler flow, use RetrievalQA.This will use a QA default prompt (shown here) and will retrieve from the vectorDB.But, you can still pass in a prompt, as before, if desired.from langchain.chains import
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. ->: (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.    2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.    3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.        As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.        llama_print_timings:        load time = 11326.20 ms    llama_print_timings:      sample time =   144.81 ms /   207 runs   (    0.70 ms per token,  1429.47 tokens per second)    llama_print_timings: prompt eval time =  1506.13 ms /   258 tokens (    5.84 ms per token,   171.30 tokens per second)    llama_print_timings:        eval time =  6231.92 ms /   206 runs   (   30.25 ms per token,    33.06 tokens per second)    llama_print_timings:       total time =  8158.41 ms    {'output_text': '  Sure, I\'d be happy to help! Based on the context, here are some to task:\n\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like "Steps for XYZ" or "What are the subgoals for achieving XYZ?" to decompose tasks into smaller steps.\n2. Task-specific: Another is to use task-specific, such as "Write a story outline" for writing a novel, to guide the of tasks.\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}RetrievalQA‚ÄãFor an even simpler flow, use RetrievalQA.This will use a QA default prompt (shown here) and will retrieve from the vectorDB.But, you can still pass in a prompt, as before, if desired.from langchain.chains import