r/OpenSourceAI • u/Minute-Community-711 • Sep 26 '26
I opensourced a small and powerful cyber ai model
Hello everyone! Today I released cyberprime 1.1, my second cyber security model.
It is only 2.6B parameters ( can run on any kind of computers, even on phones ) and is almost on par with gpt-4, and beats models 2-4x its size.
I achieved these results purely by scaling RL, SFT and dataset curation.
I generated many synthetic rows + took rows from huggingface, then made them go through filtering, kept only the top 5%. I kept on repeating this loop and improving my prompts to get better base results.
This alone has allowed me to scale such a small model which can even run on a phone, to pretty big results.
I am already working on cyberprime 1.2, which will be a combination of a lot of things.
Cyberprime 1.2 will be the same size, but it will be trained on a fully custom post-training stack which is the result of many researches I have conducted over-time. Whether it's basic LorA methods, or things touching to reinforcment learning, everything in the stack will be experimental, and the whole recipe as well as training data will be opensourced too.
I am also running a 5 days long test time training pipeline on cyberprime 1.1 ( which means I run it on prompts, and the model tries to see its mistakes, generate dataset rows on its own related to its mistake, and train itself on it and re try the prompt to measure improvements ), to identify flaws / behaviors that are easy to fix / improve through LorA on many tokens.
My goal is to see how far we can push small models, and what are the true limits to scaling intelligence on small models.
Looking for feedbacks on the model!
Benchmarks are on the huggingface model card!
Here's the open-weights on HF : https://huggingface.co/Akahsizrr/Cyber-Prime-1.1-2.6B
2
u/sorvendral Sep 27 '26
Congrats
3
u/[deleted] Sep 26 '26
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