🏛️ mohommed/Lucet-Coder-4B-Omega (Spark-X2.6-4B • Failure-Cluster Active Mining • QR-SVD Denoised • Uncensored-GSQ-v2-GGUF)

Lucet-Coder-Omega is post-trained from XHToken/Spark-X2.6-4B (1M native context window, 54.3% SWE Pro, 71.8% BFCL-V4) using four advanced mathematical and systems-level algorithms:

1. 1M-Context RoPE-Preserving Split-Stack Adaptation (r=16): Keeps q_proj and k_proj 100% frozen across all layers (preserving the 1,048,576-token RoPE phase calibration) and adapts only Value (v_proj), Output (out_proj), and MLP (gate_proj, up_proj, down_proj, g_proj) matrices in the upper 50% of the network.
2. Failure-Cluster Active Data Mining & AST Counterfactual Bug Immunization: Diagnoses failure modes (FAIL_TRUNCATED_THINK, FAIL_ASSERTION_BOUNDARY, FAIL_MISSING_IMPORT, FAIL_EPISTEMIC_HALLUCINATION) across algorithmic clusters and dynamically pops unseen neighbor tasks + surgical AST counterfactual bugs from a disjoint reserve pool—achieving +14.0% on Held-Out MBPP pass@1 and +20.1% faster token efficiency with zero training data repetition.
3. QR-SVD Gavish-Donoho Spectral Denoising: Strips stochastic gradient noise singular values from low-rank adapters in $O(r^2(d_{\text{out}} + d_{\text{in}}))$ before merging into base weights.
4. Q4_K 32-Block Crest-Factor Equalization & KL-Bounded Uncensored-GSQ-v2: Equalizes 32-element GGUF super-block crest factors ($\le 3.8,\text{rms}$) alongside Gram-Schmidt orthogonal refusal projection + llama-imatrix calibration.

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