| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.5-0.8B |
| base_model_relation: finetune |
| library_name: mlx |
| pipeline_tag: text-generation |
| language: [en] |
| tags: [dictation, speech-to-text, post-processing, text-cleanup, mlx, orbal] |
| --- |
| |
| # orb-1-mini |
|
|
| orb-1-mini cleans up dictation. It takes the text a speech engine wrote |
| down and returns the same message, cleaned up: fillers, repeated words and |
| false starts removed, self-corrections resolved, punctuation, capitals and |
| numbers fixed. It keeps the speaker's words and their order. It does not |
| shorten, summarise, answer questions or add anything. |
|
|
| It is the on-device model in [Orbal](https://orbal.app), a dictation app |
| for the Mac. A 4-bit MLX fine-tune of Qwen3.5-0.8B, 0.42 GB, for Apple |
| silicon. |
|
|
| | Dictated | orb-1-mini | |
| |---|---| |
| | So um the meeting is on Tuesday, no, Thursday at, uh, three thirty, and it's like twenty five percent over budget. | The meeting is on Thursday at 3:30, and it's 25% over budget. | |
| | So um can you, can you move the the logo to, sorry, to this side? | Can you move the logo to this side? | |
| | It costs twelve ninety nine a month, so that's about fifty dollars for four months. | It costs $12.99 a month, so that's about $50 for 4 months. | |
|
|
| ## Versions |
|
|
| - `v2` (round 5, current): writes numbers, times, dates, money and percentages as people type them. |
| - `v1` (round 4): the first release. |
|
|
| Pin a version with `revision="v2"`. |
|
|
| ## Prompts |
|
|
| The model was trained on these system prompts. Use them exactly as |
| written. |
|
|
| | Use | System prompt | |
| |---|---| |
| | Clean up | `Shape this dictation.` | |
| | For a chat message | `Shape this dictation for a chat message.` | |
| | For an email | `Shape this dictation for an email.` | |
| | For an AI agent | `Shape this dictation for an AI agent.` | |
| | For a document | `Shape this dictation for a document.` | |
|
|
| When names must be spelled a certain way, add a line to the system prompt: |
| `Spell these names exactly as written: Anna, Orbal.` |
|
|
| The user message is the dictated text alone. Turn thinking off, decode |
| greedily, and cap the answer at the input's length in tokens plus 16. |
|
|
| ## Use with mlx-lm |
|
|
| ```python |
| from mlx_lm import load, generate |
| from mlx_lm.sample_utils import make_sampler |
| |
| model, tokenizer = load("Orbalapp/orb-1-mini") |
| text = "So um can you, can you move the the logo to, sorry, to this side?" |
| messages = [{"role": "system", "content": "Shape this dictation."}, |
| {"role": "user", "content": text}] |
| prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, |
| enable_thinking=False, tokenize=False) |
| cap = len(tokenizer.encode(text)) + 16 |
| print(generate(model, tokenizer, prompt, max_tokens=cap, sampler=make_sampler(temp=0.0))) |
| # Can you move the logo to this side? |
| ``` |
|
|
| ## Results |
|
|
| Tested on 178 English dictations, 171 real ones from Orbal's developer and |
| 7 made up, and on 15 made-up dictations with numbers said as words. None |
| were used in training. |
|
|
| | | v2, clean up | v2, the four other prompts | v1, clean up | |
| |---|---|---|---| |
| | Passes Orbal's check that nothing was added | 100% | 99% to 100% | 100% | |
| | Meaning kept, blind check by Qwen 3.8 27B | 99% | 93% to 98% | 100% | |
| | Content words kept | 99% | 98% to 99% | 100% | |
| | Answers with a word added (of 178) | 0 | 0 to 4 | 0 | |
| | Names made up | 0 | 0 | 0 | |
| | Questions answered | 0 | 0 | 0 | |
| | Numbers typed as digits (22 in the numbers test) | 22 | | 19 | |
|
|
| Speed with mlx-lm on an M1 Pro: it loads in 1.2 s; a typical dictation |
| takes 0.13 s, and 9 in 10 take under 0.47 s. It keeps every word, so the |
| time grows with the length: a 347-word dictation took 5.7 s. |
|
|
| ## What it does badly |
|
|
| - Numbers over 99 and forms like "half past two" were left out of |
| training, because Orbal's check cannot yet verify their digits. The |
| model may leave them as said. |
| - The four style prompts change very little. Email sometimes adds |
| paragraph breaks; otherwise they stay close to the clean-up prompt. |
| - Long dictations are slow, see above. |
| - English only. Other languages were not tested. |
|
|
| ## How it was trained |
|
|
| Distilled from Qwen 3.8 27B. The teacher wrote 8,323 example messages and |
| a spoken version of each, with fillers, false starts, self-corrections and, |
| for v2, numbers said as words. |
| A third were read aloud with macOS `say` and transcribed by Apple's speech |
| engine, for real mishearings, and all went through Orbal's own text rules. |
| The teacher then cleaned each one up five times, once per prompt. The |
| 24,920 answers that passed every check (nothing added, every name, number, |
| negation and pointing word kept) trained a LoRA, rank 16, for 2 epochs. It |
| was merged and quantised to 4-bit MLX. No user dictations were used for |
| training. |
|
|
| ## License and credit |
|
|
| Apache-2.0, like Qwen3.5-0.8B, which it is built on (Copyright 2026 Alibaba |
| Cloud). Orbal changed it: fine-tuned, merged and quantised. If you share |
| orb-1-mini or a model made from it, keep the NOTICE file and credit |
| "orb-1-mini by Orbal". |
|
|
