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💻 Instruct tuning simllama 1.1

simonko

simonko912
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AI & ML interests

guy with low end hardware learning about llm's and learning about finetuning my hardware: intel xeon E5v4 32gb ddr4 ram amd rx 5700 8gb vram (Hf allows to set from 6600 lol) 512gb ssd queueing models for mradermacher contact: dc: simonko_11015 github: Simonko-912

Recent Activity

repliedto appvoid's post about 5 hours 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-model
repliedto appvoid's post about 6 hours 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-model
repliedto Banaxi-Tech's post about 6 hours 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.
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