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KingNish
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about 6 hours ago
We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth. Trained on: H100s from Modal The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes. Everything is open: š¤ Demo ā https://huggingface.co/spaces/build-small-hackathon/OpenMythos š§ Model ā https://huggingface.co/build-small-hackathon/OpenMythos š¦ CVE Dataset ā https://huggingface.co/datasets/build-small-hackathon/CVE_Vulnerailities_Detailed š ArXiv Dataset ā https://huggingface.co/datasets/himanshu17HF/ArvixImport-Filtered-Final Try it out and let us know where it breaks š
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