Practical experience: Fine-tuning a 0.8B model for local ASR post-processing
MoodOdd9657 · reddit · 2026-08-18
The author, while building the open-source dictation app SpeakoFlow, identified the challenge of post-processing ASR transcripts: correcting self-corrections, structuring rambling speech, and interpreting instructions without over-editing. Experiments showed that 0.8B models improve significantly with detailed prompts, suggesting fine-tuning could outperform larger general models. The plan involves fine-tuning Qwen3.5 0.8B using QLoRA with a mix of real and synthetic data, then deploying via llama.cpp. The author also discussed load time differences between Apple Silicon and discrete GPUs and sought advice on base model selection.
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