appvoid/rewrite-dpo
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appvoid
appvoid
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AI & ML interests
Working on small sota models
Recent Activity
liked a model 19 minutes ago
BananaMind/BananaMind-2-Nano reacted to Banaxi-Tech's post with 🔥 20 minutes ago
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: https://huggingface.co/BananaMind/BananaMind-2-Nano — weights, tokenizer, and config included.
repliedto their post 23 minutes ago
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!
https://huggingface.co/appvoid/a-cool-modelOrganizations
