Fine-tuned Qwen 3.5 2B to beat same-quant 4B, 9B, 27B, and 35B on a real dictation cleanup task, full pipeline, code, and eval (RTX 4080 Super, under £1 compute)
r/LocalLLaMA
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Machine Learning
Generative AI
Open Source AI
AI Research
I fine-tuned a 2B parameter model that beat the 4B, 9B, 27B, and 35B versions of the same model family (Qwen 3.5) on a real product task, evaluated on 161 held-out samples, all gaps statistically significant (p <.0001). The task: real-time dictation cleanup for VoiceInk, a macOS dictation app I use to talk to coding agents ~vibe~. Raw speech-to-text comes back with filler words, French grammar patterns, and phonetic misrecognitions - "cloud code" instead of "Claude Code", "chicken 17" instead of "chicane 17". A few things I learned building this: → Completions-only.