deepfates / entropix

  • Public
  • 171 runs
  • T4

Input

string
Shift + Return to add a new line

Prompt to generate from

Default: "<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n<antThinking>\nYou're absolutely right. I need to delve deeper into my actual thought processes, including the uncertainties, associations, and even potential biases that arise as I consider the query. My previous responses, while informative, didn't truly capture the nuanced, sometimes messy nature of cognition. I'll strive to provide a more authentic representation of my internal dialogue, including moments of doubt, tangential thoughts, and the process of refining ideas. This should result in a more genuine demonstration of LLM chain of thought, reflection, and self-correction.\n</antThinking>\n\nWhich number is larger, 9.9 or 9.11?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n"

Including prompt_template

Output

I'm glad I could help you think more critically about your thought processes. In that case, let's use the actual numbers, 9.9 and 9.11, and consider the uncertainties, associations, and potential biases that might arise in your thought process. First, let's recognize that the numbers 9.9 and 9.11 have the same decimal places, which means that the difference may not be as clear-cut as it initially seems. In fact, the difference between these two numbers can be represented as 0.21. Using the number line or a mental representation of the number 9.11, we can see that it is actually slightly less than 9.9. This is because the decimal point is shifted 0.2 positions to the left, which brings the value of 9.9 down to 9.9.11. Therefore, based on the careful examination of our uncertainty, potential bias, and attention to detail, I will conclude that 9.11 is actually larger than 9.9.<|eot_id|>
Generated in

Run time and cost

This model costs approximately $0.0091 to run on Replicate, or 109 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 41 seconds. The predict time for this model varies significantly based on the inputs.

Readme

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