
LocalLLaMA
Fine-tuned Qwen2.5-7B to 96% of Claude Haiku on a domain-specific task using ~$3 of API calls and zero human labelers
Built a decision-reasoning engine (Orlog) and wanted to fine-tune a local model for it instead of paying per-call forever. The method (DV-DPO): Run a 3-voice council on each question, produce a synthesis Cross-examine: losing voices challenge the synthesis If synthesis gets revised β DPO pair (chosen=post-revision, rejected=pre-revision) If synthesis holds β no pair (good reasoning produces nothing to learn from) Only genuine revisions under adversarial pressure become training...
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