Declare a base model and correct the measured lineage
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README.md
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- code-review
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- vulnerability-detection
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pipeline_tag: text-generation
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model-index:
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- name: Sharona_Q27B-R_CodeSecurity
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results:
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# Sharona Q27B-R CodeSecurity
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A [jBlaze](https://jblaze.dev) representation-engineered code security specialist. 27B parameters, GPTQ W4A16 quantized for efficient deployment.
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This model finds vulnerabilities in code. Command injection, SQL injection, XSS, deserialization attacks, OWASP Top 10 -- it catches what rushed reviews miss. It does not refuse to analyze attack patterns, because understanding how attacks work is how you stop them.
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- code-review
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- vulnerability-detection
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pipeline_tag: text-generation
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base_model: ApolloRaines/Sharona_Q27B-R_CodeSecurity_v2
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model-index:
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- name: Sharona_Q27B-R_CodeSecurity
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results:
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# Sharona Q27B-R CodeSecurity
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> **Provenance correction -- measured, and the version numbers run backwards.**
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> This card previously declared no base model at all, so there was nothing for
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> a reader to diff against. Probing normalisation and bias survivors across
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> 355 shared tensors:
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> | candidate | result |
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> |:----------|:-------|
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> | `Qwen/Qwen3.5-27B` | NOT the immediate parent -- 0/14 bit-identical, cos max 0.9983 |
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> | `ApolloRaines/Sharona_Q27B-R_CodeSecurity_v2` | BASE CONFIRMED -- 3/14 bit-identical |
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> This artifact is derived FROM v2, so the name "v1" does not indicate the
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> earlier model. Qwen3.5-27B is a relative or ancestor rather than the parent.
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> Margin, stated because it is thin: 3 of 14 survivors is exactly the minimum
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> our probe accepts. It clears the threshold by nothing, so read this as
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> "not a direct child of Qwen3.5-27B" -- which is firm -- rather than as v2
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> being settled against every other candidate, which it is not.
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> Bit-identity on survivors also says NOTHING about method. Restricted
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> gradient training with frozen norms leaves this same signature.
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A [jBlaze](https://jblaze.dev) representation-engineered code security specialist. 27B parameters, GPTQ W4A16 quantized for efficient deployment.
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This model finds vulnerabilities in code. Command injection, SQL injection, XSS, deserialization attacks, OWASP Top 10 -- it catches what rushed reviews miss. It does not refuse to analyze attack patterns, because understanding how attacks work is how you stop them.
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