TL;DR: Every LLM call is a labeled training example being thrown away. TEMM1E’s Eigen-Tune engine captures them, scores quality from user behavior, distills the knowledge into a local model via LoRA fine-tuning, and graduates it through statistical gates — $0 added LLM cost. Proven on Apple M2: base model said 72°F = “150°C” (wrong), fine-tuned on 10 conversations said “21.2°C” (correct). Users choose their own base model, auto-detected for their hardware. Research: github.com/nagisanzenin/temm1e/blob/main/tems_lab/eigen/RESEARCH_PAPER.md Project: github.com/nagisanzenin/temm1e
Every agent on the market throws away its training data after use. Millions of conversations, billions of tokens, discarded. Meanwhile open-source models get better every month. The gap between “good enough locally” and “needs cloud” shrinks constantly. Eigen-Tune stops the waste. A 7-stage closed-loop distillation and fine-tuning pipeline: Collect, Score, Curate, Train, Evaluate, Shadow, Monitor. Every stage has a mathematical gate. SPRT (Wald, 1945) for graduation — one bad response costs 19 good ones to recover. CUSUM (Page, 1954) for drift detection — catches 5% accuracy drops in 38 samples. Wilson score at 99% confidence for evaluation. No model graduates without statistical proof. The evaluation is zero-cost by design. No LLM-as-judge. Instead: embedding similarity via local Ollama model for evaluation ($0), user behavior signals for shadow testing and monitoring ($0), two-tier detection with instant heuristics plus semantic embeddings, and multilingual rejection detection across 12 languages. The user IS the judge. Continue, retry, reject — that is ground truth. No position bias. No self-preference bias. No cost. Real distillation results on Apple M2 (16 GB RAM): SmolLM2-135M fine-tuned via LoRA, 0.242% trainable parameters. Training: 100 iterations, loss 2.45 to 1.24 (49% reduction). Peak memory: 0.509 GB training, 0.303 GB inference. Base model: 72°F = “150°C” (wrong arithmetic). Fine-tuned: 72°F = “21.2°C” (correct, learned from 10 examples). Hardware-aware model selection built in. The system detects your chip and RAM, recommends models that fit: SmolLM2-135M for proof of concept, Qwen2.5-1.5B for good balance, Phi-3.5-3.8B for strong quality, Llama-3.1-8B for maximum capability. Set with /eigentune model or leave on auto. The bet: open-source models only get better. The job is to have the best domain-specific training data ready when they do. The data is the moat. The model is a commodity. The math guarantees safety. How to use it: one line in config. [eigentune] enabled = true. The system handles everything — collection, quality scoring, dataset curation, fine-tuning, evaluation, graduation, monitoring. Every failure degrades to cloud. Never silence. Never worse than before. 18 crates. 136 tests in Eigen-Tune. 1,638 workspace total. 0 warnings. Rust. Open source. MIT license. submitted by /u/No_Skill_8393
Originally posted by u/No_Skill_8393 on r/ArtificialInteligence