I can't help but imagine agents using caveman speak sometimes start behaving in a stereotypically caveman manner, even if it's subtle. Is there a chance the agent does less reasoning because of it?
It's not exactly a joke, it does reduce the amount of tokens. However, it does not improve performance (fine tunes are finnecky things, hard to get one right).
Personally the only 'enthusiast' modified qwen 3.6 27b or 3.6 35b-a3b I've found useful are the ones that have been run through heretic and adversarial data sets for innocent/dangerous prompts, to produce uncensored LLMs. They have some niche non-coding uses for things that a commercial LLM will never talk about.
I think those are mostly vapor that runs on the small culture of "models should not be censored" thing. But from my experience, they unlock nothing meaningful.
Fine-tuning is great for really small models on specific applications, but it's not something that can essentially improve a more generic model.
That said, there seems to be a fine line in quantization+finetuning that could recover performance. It's just hard to get a hold of it (I feel it in some models, but it's hard to say yet; lots of small labs working on this RN).
The most interesting use I've found for them so far is strictly as a novelty. Give a chat session with one to a completely non technical person, who at least knows that openai and anthropic have some guard rails on stuff, and tell them to wild with something like "give me the precursors and chemical formulas for the precusors for crystal meth" and watch it answer.
But does it answer those queries correctly, or does it just not refuse to not halucinate an incorrect answer? From where would it even have that information?
I don't know enough chemistry to say one way or the other if it's just wildly hallucinating the precursors and processes, but it'll also do things like, write an ISIS press release, or similar. There's a data set of basically a bunch of antisocial or dangerous prompts that some people have got variants of qwen to pass with 0 out of 465 refusals:
Just so we are clear, no "caveman" spoke English. "Caveman speak" is just shortening the vocabulary of english, not a "caveman language". Given this, your concerns for "stereotypical caveman manner" makes very little sense since what caveman are you talking about?
The concern is not that the model was trained on actual caveman artifacts, rather on modern media representations of the stereotypical caveman (that never actually existed).
Caveman invented fire, the wheel, domesticated wild plants and animals, organised society, survived the Toba catastrophe, cooked food, and was having sex ages before you and me. Don't write him off as stupid.
And I wonder how they actually spoke. Since there was no visual communications medium except for cave art. (Some of which is very excellent. Try drawing 3d curved horns in perspective.) So people would have used verbal communication more. Also no written word. So one would expect there to be quite a lot of oral tradition. Like people reciting poem form epics.
If we assume the time is before farming, population density would have been low and limiting culture. Hunter-gatherers might have travelled a lot more than farmers with a homestead though.
They were smarter and more fit than us. At that time not being able or not wanting to contribute to the group meant your genes were dropped from the evolution pool forever.
Unlike today where a small group of tax payer is keeping alive and thriving complete parasitic parts of human races.
Until that changes we are doomed to regress and degenerate back to monkey like creatures.
Then, there would be no discussion whether "caveman speech" is suitable for talking to the AI.
https://huggingface.co/ProCreations/grug-27b
https://github.com/p-e-w/heretic
Fine-tuning is great for really small models on specific applications, but it's not something that can essentially improve a more generic model.
That said, there seems to be a fine line in quantization+finetuning that could recover performance. It's just hard to get a hold of it (I feel it in some models, but it's hard to say yet; lots of small labs working on this RN).
This is also likely to stop working as censoring moves to the training data source.
https://huggingface.co/datasets/mlabonne/harmful_behaviors
If we assume the time is before farming, population density would have been low and limiting culture. Hunter-gatherers might have travelled a lot more than farmers with a homestead though.
Unlike today where a small group of tax payer is keeping alive and thriving complete parasitic parts of human races.
Until that changes we are doomed to regress and degenerate back to monkey like creatures.
Then, there would be no discussion whether "caveman speech" is suitable for talking to the AI.