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7.4
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zulfah
ukung
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ukungzulfah
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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: ๐ค Demo โ https://huggingface.co/spaces/build-small-hackathon/OpenMythos ๐ง Model โ https://huggingface.co/build-small-hackathon/OpenMythos ๐ฆ CVE Dataset โ https://huggingface.co/datasets/build-small-hackathon/CVE_Vulnerailities_Detailed ๐ ArXiv Dataset โ https://huggingface.co/datasets/himanshu17HF/ArvixImport-Filtered-Final Try it out and let us know where it breaks ๐
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OmniParser V2
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OmniParser, turn your LLM into GUI agent
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AutoTrain Advanced
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SambaCoder Nsql Llama 2 70B Text To SQL
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