playing with fine tuning again, had Claude take a couple months of chat logs from user interactions with kaia and cleaned them up into training examples, only ended up with 1400 examples but that still took overnight for the dinky GPU to fine tune train on the gemma3 model
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The technical term for what you are doing is Instruction Fine-Tuning (IFT) using User Feedback Loops (specifically, Reinforcement Learning from User Feedback / RLUF or behavioral cloning).Because your dataset is explicitly generated from real human logs rather than synthetic AI generation, your exact pipeline covers several specific LLM engineering
concepts: Key Terms for Your Process
Behavioral Cloning: Copying real-world user interactions to teach an AI how a human or specific system behaves.
User Feedback Loop: Mining production chat logs to patch flaws, correct formatting, or align personality traits.
LoRA Weight Merging: Compounding low-rank adapter updates directly back into base neural parameters.
Model Quantization Format Conversion: Converting 16-bit sharded tensor weights into a singular unified .gguf architecture file.
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goal is to bake Kaia's system prompt / persona file into the model itself so they aren't taking up context window and speed up response time
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LoRA Model Comparison
Path A: gemma3:12b + full Kaia persona (system prompt, enrichments, safeguards)
Path B: gemma3:12b + bare "helpful assistant" prompt
Path C: kaia-lora:latest + minimal Modelfile system prompt only (no persona injection)
This tests whether the LoRA fine-tuning has internalized the persona behaviors that Path A achieves through prompt engineering. The LoRA model's Modelfile has only a 1-line system prompt
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Core metrics: kaia-lora averages 7.0 seconds per response—nearly twice as fast as bare Gemma 3 (15.9s) and persona-steered Gemma 3 (10.7s), while maintaining style integrity and lowercase formatting without relying on large, slow prompt-engineering filters.
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