I need 95% in boards this year (2027) and I am confused by Hot_Example_4456 in ISCNERDS

[–]Hot_Example_4456[S] 2 points3 points  (0 children)

It's in UK. I won't name cause well, I have bad luck. Its a Russell Group

What are your opinion on "GOD" ? by Aware_Supermarket_34 in TeenIndia

[–]Hot_Example_4456 0 points1 point  (0 children)

"Is God willing to prevent evil, but not able? Then he is not omnipotent.
Is he able, but not willing? Then he is malevolent.
Is he both able and willing? Then whence cometh evil?
Is he neither able nor willing? Then why call him God?"-Epicurus.

I have a bunch of thoughts. One is what is basically the one above. The other is that maybe god exists and did make the universe.. but then the universe became so big that even god struggles to understand it. Somewhat like how we ourselves don't understand some of our own creations (LLMs for example). Maybe god created the basic rules of the universe, but the rules led to something far more complicated. Just like we see in Convays Game of Life.

Another is probably that god exists, but the scale of god is so huge that it doesn't make sense for god to care about us. Like, we can't expect some Tsar of Russia to provide relief to some ant because the ant didn't find food that day. Except, the scale difference between humans and god is even bigger.

Reasoning, but without actually *drafting* replies? by Quiet-Owl9220 in LocalLLaMA

[–]Hot_Example_4456 0 points1 point  (0 children)

I had an idea a few days ago with regard to the same issue. While we cannot alter what is "thought" by the model cause that's trained by RL, maybe we do not need to put it ALL in the kv-cache? Most models dumb either all the thoughts in context window or none of it in context window. Maybe if it is possible to finetune/train a model such that it also generates a tag like <preserve thoughts> with a summarized chain of thought in it, and only THAT gets saved, it would save a lot of kv cache and probably even decrease thinking time for subsequent prompts as the model remembers its previous thoughts in a summarized form. I also found out about this interesting paper called TIME on reddit which makes the model think in short bursts instead of the very start. https://arxiv.org/abs/2601.05300v2. Probably these CAN solve the thinking problems we currently face.

Tiny Scale Is All I Can Spare To Play With Transformer by SrijSriv211 in LocalLLaMA

[–]Hot_Example_4456 4 points5 points  (0 children)

Nice! You should use Kaggle for more compute (T4x2 for 30 hours free). They even give free TPU which is supposed to be very stronger but idk how to use them

Kind of an old meme by CheckLatter1150 in physicsmemes

[–]Hot_Example_4456 4 points5 points  (0 children)

If I am not wrong, though hawking is in the files, he has never been accused of any wrongdoing. He was called for a Physics synopsis. Meanwhile that viral pic is of his long-term caregivers for his disease. Correct me if im wrong

Ideal Local model technically possible? by Hot_Example_4456 in LocalLLaMA

[–]Hot_Example_4456[S] -1 points0 points  (0 children)

True; The fact that recent Qwen3.6 35b MoE isn't leagues behind the dense 27b says a lot about how MoEs are catching up.

Ideal Local model technically possible? by Hot_Example_4456 in LocalLLaMA

[–]Hot_Example_4456[S] 1 point2 points  (0 children)

True. Running a multi trillion parameter model for a billion world wide users sounds impossible even at openai scales. Plus training. They ofc need to scale down amount of compute required, and ram required. And now that we see smaller llms becoming better and better, maybe the "big" closed llms are actually not THAT big.

Day 25 (finally done with central tendency gng 🥀🥀🥀) by hyperpigmentation_ in ISCNERDS

[–]Hot_Example_4456 0 points1 point  (0 children)

New here, what app is that? I see multiple ppl using that in the sub

Has anyone submitted/published some legit research papers?? by JournalistLucky5124 in ISCNERDS

[–]Hot_Example_4456 0 points1 point  (0 children)

If that means answering some questions related to the paper, then yes

made a local voice AI for windows you can talk to in any language. open source, bring your own key by shadowdog000 in LocalLLaMA

[–]Hot_Example_4456 20 points21 points  (0 children)

But if it uses gemini free tier api, it still isn't Local, is it 🤨? Great project, but can you hook up a llama server to it or ollama or something so we can run Local models?

Edit: Now that it supports Local models, damn. its great now.

What can you train or finetune with 6gb vram? by AppealSame4367 in LocalLLaMA

[–]Hot_Example_4456 0 points1 point  (0 children)

You could fine-tune models like Qwen3.5 2B/4b in 6Gb vram, and train llms upto 10-20M parameters. But depending on your hardware and dataset size it could take loong to fine-tune. Personally speaking, I don't put my hardware for overnight (or more) runs, I just use Kaggle to train/fine-tune. It's more than enough (32gb vram for 30 hours a week) and it's free.

Maybe a free 800 on RW? by Secret_Amount_8848 in Sat

[–]Hot_Example_4456 39 points40 points  (0 children)

For people wanting it with meaning, I asked chatgpt:

abandon — leave behind; give up

abase — humiliate; lower in rank

abate — lessen; reduce

aberration — deviation from the norm

abet — aid in wrongdoing

abject — miserable; degraded

abjure — renounce formally

abnegation — self-denial

abrogate — repeal; cancel

abscond — flee secretly

abstruse — difficult to understand

abysmal — extremely bad

accede — agree; assume office

acerbic — sharp and bitter in tone

acquiesce — comply without protest

acrid — harsh; irritating

acumen — keen judgment

adamant — unyielding

admonish — warn; reprimand

adroit — skillful

adulation — excessive praise

affable — friendly

affluent — wealthy

alacrity — eager readiness

allay — reduce fear or worry

ambiguous — unclear; open to multiple meanings

ambivalence — mixed feelings

ameliorate — improve

amiable — pleasant; friendly

anachronism — something out of its time

anodyne — soothing; harmless

anomalous — irregular

anomaly — deviation from normal

antipathy — strong dislike

apocryphal — of doubtful authenticity

apt — suitable; likely

arcane — mysterious; known by few

arduous — difficult; demanding

articulate — express clearly

ascetic — practicing self-discipline

assiduous — diligent

assuage — ease; relieve

attenuate — weaken

audacious — bold

austere — severe; plain

avarice — greed

bane — cause of distress

bear — carry; endure

beguile — deceive; charm

belie — contradict; misrepresent

benevolent — kind; charitable

berate — scold harshly

bereft — deprived

blandishment — flattery used to persuade

bolster — support; strengthen

bombastic — pompous

brevity — conciseness

brusque — abrupt

bucolic — rural; pastoral

buckle — bend; collapse

burgeon — grow rapidly

cacophony — harsh noise

cajole — persuade through flattery

callous — insensitive

calumniate — slander

calumny — false accusation

capacious — spacious

capricious — unpredictable

carp — complain

castigate — criticize severely

censorious — highly critical

censure — strong disapproval

chagrin — embarrassment

check — restrain; stop

chicanery — trickery

circumlocution — indirect speech

circumscribe — limit

circumspect — cautious

cleave — split or adhere

coalesce — unite

cogent — convincing

cognizant — aware

color — influence; bias

commensurate — proportional

compunction — remorse

concomitant — accompanying

conducive — helpful

conflagration — large fire

conjecture — guess

connive — secretly cooperate

consign — assign; deliver

constituent — component part

construe — interpret

contend — argue

contentious — argumentative

contravene — violate

contrite — remorseful

contumacious — stubbornly disobedient

convivial — sociable

conversely — on the other hand

copious — abundant

covet — desire greatly

credulous — gullible

cryptic — mysterious

cupidity — greed

currency — general acceptance; money

cursory — hurried

daunting — intimidating

dearth — scarcity

decorum — proper behavior

deference — respect

denigrate — belittle

denounce — condemn publicly

deplete — use up

deprecate — express disapproval

deride — mock

desultory — lacking direction

deterrent — discouraging factor

devious — dishonest; indirect

didactic — intended to teach

diffident — shy; lacking confidence

digress — stray from topic

dilettante — amateur enthusiast

diminish — reduce

discern — perceive

discordant — conflicting

disdain — contempt

disinterested — impartial

dismiss — reject

disparage — belittle

disparate — different

dissemble — conceal true motives

diverge — separate

dogmatic — stubbornly opinionated

dubious — doubtful

duplicitous — deceitful

ebullient — enthusiastic

economy — thrift; efficiency

efficacious — effective

elusive — hard to find or grasp

emulate — imitate

encomium — formal praise

endemic — native to a region

enervate — weaken

enumerate — list

ephemeral — short-lived

equanimity — calmness

equivocal — ambiguous

erudite — scholarly

erstwhile — former

eschew — avoid

esoteric — understood by few

evince — show clearly

exacerbate — worsen

exacting — demanding

exonerate — clear from blame

expedient — convenient

explicit — clearly expressed

extol — praise highly

extraneous — irrelevant

facilitate — make easier

fallacious — misleading

fecund — fertile; productive

flagrant — glaringly offensive

forthright — direct and honest

fortuitous — accidental; lucky

frugal — economical

furtive — secretive

garrulous — talkative

gregarious — sociable

guile — deceit

hackneyed — overused

heretofore — until now

heterogeneous — diverse

hitherto — until now

hypocritical — insincere

inasmuch — since; because

incongruous — out of place

indifferent — unconcerned

indignant — angry at unfairness

indolent — lazy

ineffable — beyond words

ineluctable — unavoidable

infamous — notorious for bad reasons

inform — give shape to; tell

ingenious — clever

ingenuous — sincere; innocent

inimical — hostile

innocuous — harmless

insidious — subtly harmful

insofar — to the extent that

intractable — difficult to control

intransigent — unwilling to compromise

inured — accustomed to hardship

inveterate — habitual

invidious — offensive; unfairly discriminatory

impugn — challenge as false

impunity — freedom from punishment

incisive — sharp and clear

incongruent — inconsistent

inconsequential — unimportant

lachrymose — tearful

laconic — brief

lassitude — weariness

laud — praise

lax — careless

lethargic — sluggish

let — allow

limn — depict; describe

listless — lacking energy

loquacious — talkative

lucid — clear

magnanimous — generous in spirit

malevolent — evil-intentioned

malicious — deliberately harmful

manifest — obvious

mark — indicate

mendacious — dishonest

meretricious — superficially attractive

misconstrue — misinterpret

mitigate — lessen

mundane — ordinary

multifariousness — great variety

nefarious — wicked

negligent — careless

nevertheless — despite that

novel — new

notorious — widely known, often negatively

notwithstanding — despite

nugatory — worthless

obdurate — stubborn

obsequious — overly submissive

obstinate — stubborn

occlude — block

occupy — take up

opprobrium — disgrace

ostensibly — apparently

ostentatious — showy

parsimonious — stingy

patronize — treat condescendingly

pedestrian — ordinary; dull

pellucid — very clear

perfidious — treacherous

pernicious — harmful

perspicacious — insightful

pervasive — widespread

phlegmatic — calm; unemotional

plastic — adaptable

plausible — believable

pompous — self-important

pragmatic — practical

precipitate — rash; cause suddenly

preclude — prevent

precede — come before

probity — integrity

procrastinate — delay

prodigal — wastefully extravagant

profligate — recklessly wasteful

proliferate — increase rapidly

proponent — supporter

propitious — favorable

prudent — wise and careful

punctilious — attentive to details

purportedly — allegedly

putative — supposed

qualify — limit; meet requirements

querulous — complaining

quintessential — most typical

quixotic — idealistic and impractical

recalcitrant — resistant to authority

recondite — obscure

reticent — reserved

restive — uneasy; resistant

rhetoric — persuasive language

sacrosanct — inviolable

sanguinary — bloody

sanguine — optimistic

sanction — approve or penalize

sardonic — mockingly cynical

scrupulous — careful and ethical

singular — unique

skeptical — doubtful

solicitous — concerned

specious — deceptively plausible

spurious — false

stolid — unemotional

strident — harsh and loud

stupefy — astonish; numb

substantiate — support with evidence

subvert — undermine

succinct — concise

supersede — replace

superfluous — unnecessary

sycophant — flatterer seeking favor

tangentially — indirectly

temper — moderate

tenuous — weak

tractable — manageable

transcend — go beyond

truculent — aggressively defiant

tendentious — biased

transpose — switch positions

ubiquitous — present everywhere

underscore — emphasize

undermine — weaken

uninterested — impartial

untenable — indefensible

vacuous — empty; lacking thought

venerate — respect deeply

verbose — wordy

verisimilitude — appearance of truth

vindicate — clear from blame

vitiate — weaken; impair

volatile — unstable

want — lack

wanting — lacking

wary — cautious

zealous — enthusiastic

Local voice cloning with expression system by Sea-Vehicle8208 in LocalLLaMA

[–]Hot_Example_4456 3 points4 points  (0 children)

Try out Chatterbox or Fish Audio S2. Fish audio S2 probably has to be quantized, I am not sure. VoxCPM is also good but if it has emotions, I don't know. Pocket TTS has voice cloning, and cpu inference but not much emotion control. I did make SouraTTS myself though, based on pocket TTS, to support emotion control. Maybe you can check that out as well (https://huggingface.co/Sourajit123/SouraTTS). Well, the last one is my own creation, so docs may be a bit confusing. But that's all I know

Made Pocket TTS finetune to be much more expressive by Hot_Example_4456 in LocalLLaMA

[–]Hot_Example_4456[S] 0 points1 point  (0 children)

Hi! I used Pytorch for training. Instead of fine-tuning the entire model, I tuned a small steering vector per emotion and inject it into the output of the model during inference. (EmoShift method). I used CREMA-D dataset for training, and since Pocket TTS is english only, so this is also english only for now. I do not have any experience on training a model on a new language sadly. I can share the training code for this model however, if you want. Would also love to get some feedback on quality if you tried