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5
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37
Frank Denis
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jedisct1
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https://00f.net
jedisct1
jedisct1
AI & ML interests
Cryptography.
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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: 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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jedisct1
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jedisct1/starfall-quest-qwopus-coder-trace
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jedisct1/fastly-agent-toolkit-evals
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jedisct1/security-audits
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jedisct1/enriched-golang-finetune-dataset
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jedisct1/tool-calling
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jedisct1/enriched-rust-finetune-dataset
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jedisct1/agent-traces-zero
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jedisct1/agent-traces-flashmania
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jedisct1/ds4-traces
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jedisct1/golang
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jedisct1/rust
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