I fine-tuned a 0.8B local model for dictation cleanup. It matched a hosted frontier model on this narrow task

I built SpeakoFlow Mini, an Apache-2.0 fine-tune of Qwen3.5-0.8B for dictation cleanup.

It is not a chat model or a general rewriter. It takes speech-to-text output, applies corrections the speaker actually made, and leaves everything else alone. That last part is harder than it sounds. General models often polish text that was already correct, but in dictation an unnecessary improvement is still a wrong edit.

For example:

Input: The deadline is Monday. Scratch that. The deadline is Wednesday. Output: The deadline is Wednesday. 

On my specialized English-only benchmark, SpeakoFlow Mini scored 70.7% and GPT-5.6 Luna scored 65.0%. Both used the same fixed short prompt, with reasoning disabled. The gap was +5.8 points, but the 95% interval was [-1.5, +12.9], so this is a statistical tie, not a win.

That setup matters. It is the low-overhead configuration this model was trained for. With a longer prompt and a reasoning budget, Luna does better. It is also the better model for unusual cases outside the narrow patterns covered here.

The claim is narrower. Under one fixed short prompt with no reasoning budget, a 0.8B model running locally matched a hosted frontier model on this task.

The controlled comparison is against the untuned Qwen3.5-0.8B base. Fine-tuning moved the score from 47.3% to 70.7%, a gain of +23.4 points with a 95% interval of [+16.3, +30.3]. The second image shows that comparison.

The Q8_0 build is 833 MB and runs fully offline. The Hugging Face card has the model files, run commands, benchmark method, quantization results, limitations and public examples:

https://huggingface.co/SpeakoFlow/speakoflow-mini

The evaluation set is not part of the public release.

If you try it, I am most interested in cases where it changes something that should have been left alone.

Disclosure: I built SpeakoFlow Mini and SpeakoFlow.