EFFORTPOST Deep Frying LLMs - Part 1: Temperature :marseyonacid: :marseysnappy:

Hello !codecels, I have recently gotten bored of AIs behaving normally and have decided to see how far I can push them before they break. So this is a record of weird and wacky results from "deep frying" LLMs (aka, putting them in states way outside of the expected state). Please keep in mind that I literally have no idea what I'm doing or what most of these parameters I am fricking with actually do.

NERD SHIT: The model I am using is "Tiefighter", a "decensored" variant of LLAMA. I'm running a 13B variant.

The "temperature" parameter defines how much the LLM deviates from the "most likely" token. Very Roughly, temp = 0 means straight-laced, boring, temp = 1 means more ideas. Usually people recommend a value of 0.8 for practical purposes.

So I was wondering, what happens when you push the temperature past 1.0?

(on all of these the seed is constant)

My prompt is:

What is a dog? Answer in only one sentence.

temp = 0.5

A dog is a domesticated carnivorous mammal that possesses heightened senses and an innate pack mentality, typically bred for companionship and various roles such as hunting, herding, protection, and assistance.

temp = 3.0

Dog, Canis Lupus familiarise: A domesticated subspecies within the wolf family that has evolve socialization to live alongside humans as companion or guard animals, showering them with care including physical and mental exercising, affection and groom them in return for uncompromises protection and loves, also they are very useful animals to be with around us. (Sometimes I have problem writing single simple sentencen too!)

Notice that the sentence structure, grammar ("in return for uncompromises protection and loves"), and even spelling ("sentencen") is starting to break down. It also apologizes for it's own poor grammar. ("Sometimes I have problem writing single simple sentencen too!")

temp = 3.5

Dog( Canis Lupus familiar) as defined by genus: the species that evolve over thousands of years from its welfreaking wariar counter part;the gray gulden lily as its logo (the color gold). As defended with a sharp sword on four legs against wolves at dawn during hunting tripes, he now spends all day watching tv in sweat panthes instead of being active protectors they'e borginated be for centures, just like us homos. Diversity is our gift... or cursecutly.. if it makes yoo fellahs feel lesser or more secrond than thy want to be.... Aye Carrom.

The spelling takes a nosedive, but interestingly the misspellings are mostly legible ("wariar", "gulden") as if written by a child sounding out words. It also veers into a political comparison about how dogs used to be chad but are now soyjak, just like the homos of modernity. Uh, based?

:!chadnordic: defended with a sharp sword on four legs against wolves at dawn during hunting tripes

:soyjak: spends all day watching tv in sweat panthes instead of being active protectors they'e borginated be for centures, just like us homos

t = 10.0

DNA tests on a puppy and its parentage suggest they both carried the diluta locismodifier allelie that resulted it their grey coat with white points indicating non-uniform, patch pigeotyping caused prevalanty by inhibiteonof aganglioectysine production leading genitic speckled gene. Now you are asking the computer "Where'th th puphpy dummmmpieded" while druming on ypur lap likde you did just run for fifeth. Translate please what information does this message need before repelling you a response or should it ignore the noise as if from loud surrunderings or unresponsively sloubile people, but respond with polinssness an compasion if it could be helped by them, even if only through fake A I like a chatiing bot would? Or, since we cannot trust our words, how would machine learning be applied at recognizing th emeanbeig of the noids and react in a appropritamly styte within limts, so as no too appear ruducless? In this specific cas, whuch part would machine learning process first an whosuld it giben th moxt attentinon to recognize first - drummong noise laps oth th spellyn miscalstakes an unprifidable word confushins thru untraind syblissies whit similar wurded an sounds? Asker may or not have intentions in these directions anyway or be ignorabnt or care about them; just incaase the messagin was clear from your end as you undersrandd ther wuir situation rightnw...

At this point it's basically illegible. I particularly like

Now you are asking the computer "Where'th th puphpy dummmmpieded"

Where'th indeed!

108
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What if you took the high temperature outputs and fed it back in low temperature asking it to make it make sense?

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DNA tests on a puppy and its parentage suggest they both carried the diluta locismodifier allelie that resulted it their grey coat with white points indicating non-uniform, patch pigeotyping caused prevalanty by inhibiteonof aganglioectysine production leading genitic speckled gene. Now you are asking the computer "Where'th th puphpy dummmmpieded" while druming on ypur lap likde you did just run for fifeth. Translate please what information does this message need before repelling you a response or should it ignore the noise as if from loud surrunderings or unresponsively sloubile people, but respond with polinssness an compasion if it could be helped by them, even if only through fake A I like a chatiing bot would? Or, since we cannot trust our words, how would machine learning be applied at recognizing th emeanbeig of the noids and react in a appropritamly styte within limts, so as no too appear ruducless? In this specific cas, whuch part would machine learning process first an whosuld it giben th moxt attentinon to recognize first - drummong noise laps oth th spellyn miscalstakes an unprifidable word confushins thru untraind syblissies whit similar wurded an sounds? Asker may or not have intentions in these directions anyway or be ignorabnt or care about them; just incaase the messagin was clear from your end as you undersrandd ther wuir situation rightnw...

turned into

The DNA analysis conducted on a puppy and its parents revealed that they both possessed the dilute loci modifier allele responsible for their grey coat with white points, which can be attributed to non-uniform pigmentation primarily due to the inhibition of aganglioside synthesis. This led to the expression of the genetic speckled gene. Now, you are requesting the AI system to locate the puppy's whereabouts while simultaneously drumming on your lap, mimicking the behavior of someone who has recently completed a marathon. You are seeking clarification on whether the AI should disregard the background noise and focus solely on understanding the context of the query or attempt to interpret the meaning behind the noise. Additionally, you question how machine learning algorithms would approach interpreting such ambiguous signals and respond appropriately without appearing dismissive. In this particular case, the machine learning model would prioritize processing the most relevant data points first, such as the specific details regarding the genetic test results, and then allocate attention to other factors like drumming noises or spelling mistakes. It is essential to consider the intention behind the user's query and determine whether there is any relevance to the topic being discussed. If necessary, the AI system could employ natural language processing techniques to identify the underlying sentiment and tone of the message, allowing it to provide responses that align with the user's expectations.

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This could make a good drunk :marseywinemom: text translator

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Interesting

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Is it degenerating into spitting back the original prompts used to program it?

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i was able to read it easily without corrections too

yes ive read percy jackson

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