We trained a personal voice DoRA on Qwen3-8B for $1.50 — beat stock model 100% in blind A/B

TL;DR. Trained a DoRA adapter on Qwen3-8B using 6128 personal Telegram messages. Cost: $1.50 on a single Vast.ai RTX 3090. In blind head-to-head A/B, the DoRA-tuned model beat stock Qwen3-8B 100% of the time. Zero catastrophic forgetting on 50 general-knowledge tasks. One prompt where the model actually beat the real human at sounding like themselves.

Full long-form write-up lives on the canonical URL: aiconic.company/en/journal/dora-personal-voice. This post is the dev.to-flavored version with the practical bits.

What we did

Took one person's Telegram export (DataExport JSON, 1047 personal chats), wrote a custom pairs extractor (other_person_message, author_reply), capped 12 pairs per chat so a few active chats don't dominate, deduplicated. Final dataset: 6128 train + 322 valid pairs.

Trained a DoRA adapter on top of Qwen/Qwen3-8B. DoRA (Weight-Decomposed Low-Rank Adaptation, Liu et al. 2024) decomposes pretrained weights into magnitude and direction, then applies LoRA-style updates only to the direction component while learning magnitude as a separate trainable vector. In practice it matches full fine-tuning more closely than LoRA at the same rank.