lowdown-labs/fela-streaming-asr
Automatic Speech Recognition • 64.4M • Updated • 17 • 1
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appvoid
appvoid
AI & ML interests
Working on small sota models
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lowdown-labs/fela-streaming-asr liked a Space 1 day ago
Quazim0t0/neural-doomOrganizations
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Following
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Hey
following
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mine are all <300M
Great, following already!
replied to their post 2 days ago
Surely I do!
posted an update 2 days ago
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125
If you make cool smol 🤖🤏 models (below 0.5b parameters), leave a reply and I will follow you! I'm serious, you don't need to follow me at all just share something through the replies and I (and potentially more people) will follow you (if your models are decent ofc).
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following
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is there anything you have always wanted to achieve? that idea you know you want to be true? is possible if any
keep working 💪
achieve maximum benchmark score per param
lol
you're talking about robust performance and not benchmaxxing, right? right?
replied to their post 2 days ago
is there anything you have always wanted to achieve? that idea you know you want to be true? is possible if any
keep working 💪
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reacted to Banaxi-Tech's post with 🔥 2 days ago
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Introducing BananaMind 2 Nano
BananaMind 2 Nano is the smallest member of the BananaMind 2.0 family — a 10M-parameter language model that shows how much you can squeeze out of a tiny footprint. It uses the family's digit-isolated tokenizer, so it keeps solid arithmetic despite its size, and it's small enough to run just about anywhere.
Trained on 30B tokens in about a day on a single RTX 5070 Ti (16GB), 4096-token context.
Benchmarks:
Average 35.77
ARC Easy 36.20
PIQA 55.98
ARC Challenge 23.38
HellaSwag 27.50
That 35.77 average edges out Pythia-31M (~34.79) at roughly a third the parameters.
Released under Apache 2.0 on Hugging Face: BananaMind/BananaMind-2-Nano — weights, tokenizer, and config included.
BananaMind 2 Nano is the smallest member of the BananaMind 2.0 family — a 10M-parameter language model that shows how much you can squeeze out of a tiny footprint. It uses the family's digit-isolated tokenizer, so it keeps solid arithmetic despite its size, and it's small enough to run just about anywhere.
Trained on 30B tokens in about a day on a single RTX 5070 Ti (16GB), 4096-token context.
Benchmarks:
Average 35.77
ARC Easy 36.20
PIQA 55.98
ARC Challenge 23.38
HellaSwag 27.50
That 35.77 average edges out Pythia-31M (~34.79) at roughly a third the parameters.
Released under Apache 2.0 on Hugging Face: BananaMind/BananaMind-2-Nano — weights, tokenizer, and config included.
- 1 reply
replied to their post 2 days ago
following you!
ok
replied to their post 2 days ago
I want to follow people that also make super cool small models so comment here to follow you
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cool, mind sharing it?
replied to their post 2 days ago
I'm in the works for one actually. I have been talking about it forever but I want to make sure is at the level of people's expectation.
replied to their post 2 days ago
I have been always fascinated to discover what's the minimum core of actual usefulness.
5 years ago I thought it was 1 billion parameters, but recently I discovered a 350m that is better than most 1b models.
350m is not perfect but as of today, if you go lower you lose general language understanding.
posted an update 3 days ago
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if you are a tinkerer of small language models and want to stay ahead of what small models can do, follow me!!! seriously, start following people that actually still makes small models
i've made one recently btw
also, i'm keeping an eye on AxiomicLabs leaderboard, looks like the only current alternative to check where the things are going to
though, between us, i think they should add agentic/tool use benchmarks there
anyways,
enjoy!
appvoid/a-cool-model
i've made one recently btw
also, i'm keeping an eye on AxiomicLabs leaderboard, looks like the only current alternative to check where the things are going to
though, between us, i think they should add agentic/tool use benchmarks there
anyways,
enjoy!
appvoid/a-cool-model
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