v5

v5 (scale 1.00)

This is a standalone, merged BF16 checkpoint derived from gold24k/v3. It applies a scaled selective-fallback LoRA trained on preserved positive turns and sanitized, task-specific alternatives for high-confidence negative turns. It does not require a runtime router, custom Python code, or a PEFT adapter.

Training summary

• Exact parent revision: 698c1fa8fa42e8c60ba44c6fd7d5b48e8c72b489
• Adapter scale at merge: 1.00
• LoRA: r16, alpha64, dropout0.0, all-linear
• Objective: DPO, beta0.2, learning rate 2.0e-08, 1.0 epoch
• Context during training: 8192 tokens
• Training GPUs: 2 x NVIDIA H200
• Selected target mix before context filtering: 90% preserve / 10% fallback

Held-out preference proxy

The table compares the scaled adapter with the untouched pinned parent. These small held-out metrics selected the merge strength; they are not a substitute for the exact full Affine duel on the dedicated evaluator.

Qualification status

Experimental candidate. Before submission, run exact stock-vLLM Affine duels, the exploit-pattern audit, repository preflight, and the official submission client check. selective_fallback_provenance.json contains the machine-readable training and merge record.