LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
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Wouldn't go amiss.
Okay, point taken. I've been guilty of lurking inappropriately and I can model the consequences of that.
I have a reasonable amount of direct experience of purposeful llama.cpp use with 7B/13B/30B models to offer. And there's a context - I'm exploring its potential role in the generation of content, supporting a sci-fi web comic project - hardly groundbreaking, I know but I'm hoping it'll help me create something outside the box.
For additional context, I'm a cognitive psychologist by discipline and a cognitive scientist by profession (now retired) and worked in classic AI back in the day.
Over on TheBloke's discord server, I've been exposing the results of a small variety of pre-trained LLM models' responses to the 50 questions of the OCEAN personality questionnaire, presented 25 times to each - just curious to see whether there was any kind of a reliable pattern emerging from the pre-training:
OCEAN questionnaire full-size jpeg
Looks like the larger models enable a wider range of responses, I guess that's an expected consequence of a smoother manifold.
Happy to answer any questions that people may have and will be posting more in future.
Cheers, Graham
I would love to see more of this and maybe making it its own post for more traction and discussion, do you have a link to those pictures elsewhere? can't seem to get a large version loaded on desktop haha.
I edited the post to include a link to the discord image. If there's interest I can make a post with more details (I used Python's
pexpectto communicate with a spawned llama.cpp process).Very interesting! Did you test chatGPT as well for comparison?
No, I haven't and I don't intend to because I wouldn't get anything out if the exercise. I don't (yet?) have a deep enough model to inform comparisons with anything other than different parameter sizes of the same pre-trained models of the Meta LLAMA foundation model. What I posted was basically the results of a proof-of-method. Now that I have some confidence that the responses aren't simply random, I guess the next step would be to run the method over the 7B/13B/30B models for i) vicuna and ii) wizard-vicuna which, AFAICT are the only pre-trained models that have been published with all three 7, 13 and 30 sizes.
It's not possible to get the foundation model to respond to OCEAN tests but on such a large and disparate training set, a broad “neural” on everything would be expected, just from the stats. In consequence, the results I posted are likely to be artefacts arising from the pre-training - it's plausible (to me) that the relatively-elevated Agreeableness and Conscientiousness are elevated as a result of explicit training and I can see how Neuroticism, Extroversion and Openness might not be similary affected.
In terms of the comparison between model parameter sizes, I have yet to run those tests and will report back when I have done.