TEMM1E v3.1.0: AI Agent That Self-Fine-Tunes Using User Interactions

What TEMM1E Eigen-Tune Does

TEMM1E's Eigen-Tune engine captures every LLM call as labeled training data that would normally be discarded. It scores response quality from user behavior signals (continue, retry, reject), distills knowledge into a local model via LoRA fine-tuning, and graduates models through statistical gates — all with $0 added LLM cost.

Technical Implementation

The system uses a 7-stage closed-loop pipeline: Collect, Score, Curate, Train, Evaluate, Shadow, Monitor. Each stage has mathematical gates:

• 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

Evaluation is zero-cost by design: embedding similarity via local Ollama model ($0), user behavior signals for shadow testing ($0), two-tier detection with instant heuristics plus semantic embeddings, and multilingual rejection detection across 12 languages.

Benchmark Results

Real distillation on Apple M2 (16 GB RAM): SmolLM2-135M fine-tuned via LoRA with 0.242% trainable parameters. Training: 100 iterations, loss reduced from 2.45 to 1.24 (49% reduction). Peak memory: 0.509 GB training, 0.303 GB inference. Base model incorrectly calculated 72°F = '150°C', while fine-tuned model correctly output '21.2°C' after learning from 10 examples.

Hardware-Aware Model Selection

The system auto-detects hardware and recommends models:

• 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

Configure with /eigentune model or leave on auto.

Setup and Implementation

Enable with one line in config: [eigentune] enabled = true. The system handles collection, quality scoring, dataset curation, fine-tuning, evaluation, graduation, and monitoring. Every failure degrades to cloud — never silence, never worse than before.

Built in Rust with 18 crates, 136 tests in Eigen-Tune, 1,638 workspace total, 0 warnings. Open source under MIT license.