Update README.md

README.md

@@ -23,30 +23,6 @@ model_type: qwen3_5_text
 
 Seven stacked behavioral modifications. Automatically calibrated.
 
-> **Provenance correction.** An independent weight-level audit of this
-> checkpoint against its declared base, `Qwen/Qwen3.5-27B`, found that it is
-> NOT a surgical child of that base. Gradient training lies somewhere
-> between the two:
->
-> - 0 of 14 normalisation and bias tensors survive unchanged (weight surgery
->   leaves these untouched); they moved 5.1e-3 to 1.6e-1.
-> - Cosine to the base is 0.775-0.942 at essentially unchanged scale
->   (norm ratio 1.014-1.031) -- related, same magnitude, substantially
->   rotated. That rules out both rescaling and a wrong base.
-> - `q_proj` (rel. Frobenius 0.46-0.52) and `embed_tokens` (0.47) are both
->   modified. Neither is a weight-surgery target.
-> - The deltas are spectrally dense throughout (leading singular direction
->   carries 0.05-1.2% of the energy; 64 directions carry 3-10%), which is
->   the signature of training rather than of a constructed low-rank edit.
->
-> The audit CANNOT say which step is responsible. `Sharona_Q27B-R_CodeSecurity`
-> (v1) predates this model and sits closer to it than the declared base does,
-> so the real chain is likely base -> v1 -> v2, and an undisclosed
-> intermediate is indistinguishable from training in the final step. What is
-> established is that the path from `Qwen/Qwen3.5-27B` to these weights
-> includes gradient training, so the `weight-surgery` tag does not describe
-> this checkpoint on its own. The full chain should be documented here.
-
 This is a 27-billion parameter code security scanner built on [Qwen 3.5-27B](https://huggingface.co/Qwen/Qwen3.5-27B) with 7 stacked behavioral modifications, each automatically calibrated by **jTuner** -- a new capability in [jBlaze](https://jblaze.dev) that we are announcing with this release.
 
 Sharona is the model behind [ShipItClean](https://shipitclean.com), our automated code security review platform. This is not a general-purpose chatbot. It is a purpose-built code vulnerability scanner that looks at your code and tells you what is wrong with it, specifically, without hedging, and without agreeing with you when you are wrong.