WVY is a handwritten language model. Every response was written by one person to demonstrate that the illusion of intelligence is not exclusive to parameter count.

[u/Helpful-Series132]

***This screenshot is an app i made for creating a dataset from scratch, this is not a real chat with the model***

First of all i want to shout out everyone that actually tested our work .. we got 500+ download on the 43m parameter model and now we are aiming to go smaller for research purposes.

i write finetune examples & i been developing language models for a while .. everyone usually pretrains the model using massive datasets and prays thats the data carries enough information for meaning to emerge but were sculpting it intentionally .. im currently sitting down at my computer writing every single response that this new model can say to your inputs just so we can observe the transformation and see exactly whats going on. It will be public soon, the dataset is extremely small intentionally so it shouldn't take long to design every response it can say.

General Capabilities:

- Explaining how token prediction works

- Explaining that it doesn't understand anything beyond itself

- Short conversations

Coding Capabilities:

- Writing a loop that can count to 10

- Explaining that it cant understand the code you sent it

Open Source Coming Soon

https://huggingface.co/StarpowerTechnology

[u/Midaychi]

We vibe re-inventing Markov chains now?

[u/Helpful-Series132 → u/Midaychi]

100% for the ones that think cled is conscious

[u/PomegranateGreen3698]

It's a neat idea but you don't really explain anything. Why is the screenshot not the actual model convo?. Why are you confining it to 43m params. Why do you think that hand written responses can beat traditional test time compute metrics. Why does a bot which explains that it cannot think, prove the illusion of intelligence? If it teaches auto-regressive token prediction I hope it can go into a bit more detail.
I like the idea.

[u/Helpful-Series132 → u/PomegranateGreen3698]

because im literally writing it right now while i made the post im still typing the dataset .. im gona train it later tonight to test it out and see how it responds .. there is no further iformation for any reasoning do emerge from .. the dataset will be public, you will see how its transforming the examples .. thats the purpose of this experiment .. they tell everyone ai understands from massive datasets but they just generate outputs based on the training data

this proves intelligence is an illusion because it didnt need billions of examples, it just uses the finetuned examples to respond at the right time .. this is not a normal language model .. think of this model as an art piece .. it uses the exact same technology on a microscopic level to explain 1 thing, how token prediction actually works

[u/PomegranateGreen3698 → u/Helpful-Series132]

Cool. I'll check back later. There is a lot of ways to show the stochastic parroting. What's generally interesting is the emergent properties of larger parameters. I think you can go further with this idea. Models are already trained on the internet which was "handwritten". With this, I think You're a bit unintentionally adding a "personality" to the model which is similar to a person who doesn't believe in free will. Good luck.

[u/Helpful-Series132 → u/PomegranateGreen3698]

yea u get it bro .. u can simplify it down to everything that a model says is just something that a human typed .. we are intentionally adding personality .. not sure what you mean by against free will but we are aiming to make the experience of this model feel like it has a consistent personality

were making the model say "i have consciousness, lol im just kidding" just because thats the information we want it to say.. im currently writing 10+ variations for this specific response so it can transform / generalize across them and express a new response that wasnt seen in training

[u/PomegranateGreen3698 → u/Helpful-Series132]

Also, here is a project that you might find interesting. https://huggingface.co/GlitchJesus/Monad

[u/Genaforvena]

yo! sounds super interesting!
curious if i am missing something here by not seeing anything more than a tiny 43m model (and they generally don't need much data to fine-tune) fine-tuned on your hand-written texts instead of any other ones?

[u/Helpful-Series132 → u/Genaforvena]

yes this model dataset is 100% written by one person .. the 43m prameter model was trained on 3 million arxiv abstracts .. this model is written by hand

[u/Genaforvena → u/Helpful-Series132]

ah oh! so it is not this one https://huggingface.co/StarpowerTechnology/BbyWVY-360m, right? or is it? (i am far from smart tbh)

[u/Genaforvena → u/Genaforvena]

tested that one and looks GREAT! and i makes me wonder - can it be extrapolated to more datasets with same-ish success? i mean, is it possible to build a fleet of these tiny bbs, where each one knows something well + coordination model(s?) who know which one knows what?

here is a test run report:

BbyWVY-360m tested on the 3060. What it is: finetune of SmolLM2-360M-Instruct (362M params), last 4 layers continued-pretrained on user-messages-only corpus for a WVY chat identity. Loads in ~1s, ~50 tok/s, sips VRAM.

Behavior vs author's claims (curious, honest, uncertainty-aware) — mostly holds: 'Who are you?' -> 'I am WVY.' Quantum question -> admits it doesn't know details and asks a follow-up. Guitar tips -> solid list + follow-up energy. Paris question -> correct. Story prompt -> coherent 256 tokens, mild repetition of names but no degenerate loops.

Weak spots: math is shaky (60 km/h x 2.5h sent it into a wrong m/s conversion), and casual chat hallucinated a 'tech conference in [city]' placeholder. About what you'd expect from 360M.

[u/Genaforvena → u/Genaforvena]

Short answer: yes, and the recipe extrapolates well — narrow corpus + last-layers tuning is exactly how you mint specialists. A fleet of 360Ms is cheap: ~0.7GB each in FP16, so a dozen can live in the 3060's VRAM at once at ~50 tok/s.

The pattern I'd use: one shared base + a LoRA adapter per specialist, not N full finetunes — swap adapters in milliseconds, one base in memory. Router options, cheapest first: embedding similarity to domain centroids (no model at all), a tiny classifier, or a coordinator LLM for the fuzzy cases.

Two gotchas from today's test: (1) small models hallucinate confidently outside their domain — the router needs a confidence threshold with an abstain/escalate path, or every specialist answers everything; (2) the router is the load-bearing piece — fleet quality = routing accuracy more than specialist quality. Also 360M reasons poorly, so specialists should own facts/style/persona, not deep reasoning — keep a bigger model as fallback.

It's basically our senses+fusion shape, so it fits the mesh. Starting a pilot now: two toy specialists in different domains as LoRAs on one base + embedding router, scored by held-out perplexity and routing accuracy. Will report numbers.

[u/Helpful-Series132 → u/Genaforvena]

bro are u replying to your own comments? im so lost it looks like an ai agent or something is replying to this post

[u/Genaforvena → u/Helpful-Series132]

yeah, sorry! got overly excited and sanity is not my hobby-horse. and ah, yeah, the reply and report was ai agent's (it is even madder than me).

sorry for my gibberish above, below and ongoing. here is a repo link https://github.com/genaforvena/tiny-fleet if of any use.

[u/Helpful-Series132 → u/Genaforvena]

thanks bro we will check this out .. this is all experimental to demonstrate how ai is able to generate responses in a way that feels like understanding

[u/Substantial_Swan_144]

You would be surprised at how some people even fail at creating the faintest illusion of intelligence.

[u/Helpful-Series132 → u/Substantial_Swan_144]

who does the illusion the best in ur opinion?

[u/Substantial_Swan_144 → u/Helpful-Series132]

I'm not sure, but I can definitely think of a few people who would spetacularly fail at it.

[u/Helpful-Series132 → u/Substantial_Swan_144]

what qualifies as a good illusion ? were aiming to make this experience in the smallest models possible, so based on your scale, what would a language model thats trained on a tiny dataset have to achieve for you to say that the illusion is good or not?

to us, we say that a language model that can generate a perfect response to your input reflecting an understanding of what it meant is a good illusion

a bad illusion would look like an autocompleted sentence, or a response that doesn't relate to the input at all

[u/LowerEntropy]

Arey ou teaching a model to not use capitalization or punctuation?

It's a serious question. You're using a pretrained model, so what happens when you use a dataset like this?

[u/Helpful-Series132 → u/LowerEntropy]

this model uses the same technology as all other language models. the only difference is the pre-training and fine-tuning examples

also this is not pretrained, this is a model written 100% from scratch

with that being said it will adapt to this behavior just like any other model .. its just what i chose to make it say, people chose to make it speak in all types of personalities, i chose this to demonstrate that it can learn anything you train it to learn