S1-mini-MLX-bf16

S1-mini by Superwhisper — MLX BF16

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This repository contains an unquantized MLX BF16 conversion of S1-mini by Superwhisper, converted from the original BF16 weights with mlx-lm. It preserves the original tokenizer, chat template, generation configuration, model documentation, and license.

Use with MLX

uv tool install mlx-lm

from mlx_lm import generate, load

model, tokenizer = load("mlx-community/S1-mini-MLX-bf16")
system = (
    "You are a text normalizer for speech-to-text transcripts. The input begins "
    "with a control line specifying the styling, structure, and context settings; "
    "clean the transcript to match those settings and output only the cleaned text."
)
messages = [
    {"role": "system", "content": system},
    {"role": "user", "content": "[Styling: semi-formal] [Structure: prose] [Context: general]\n"
        "so um send the report by uh friday"},
]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
print(generate(model, tokenizer, prompt=prompt, max_tokens=128, verbose=False))

Converted with mlx-lm 0.31.3. A six-case deterministic sanity check matched five upstream reference strings exactly; the remaining output retained a leading “So,” while preserving the correction and meaning. This is a small functional check, not the upstream 7,519-case evaluation.

A 0.6B-parameter text normalizer for speech-to-text output. It takes a raw ASR transcript and rewrites it as clean written text: fillers removed, false starts and self-corrections resolved to the value the speaker landed on, punctuation and capitalization applied, and spoken numbers, dates, times, currency and email addresses rendered in written form.

On a held-out set of 7,519 English cases it reaches 94.8% token accuracy, and the quantized build is a 462 MiB file that runs comfortably on a laptop CPU.

This is release v1, and it covers English only. S1-mini is not a chat model and will not follow general instructions; it does one job, and you steer it with a control line at the top of the input.

Fine-tuned from Qwen/Qwen3-0.6B. If you want to run it in llama.cpp, Ollama, LM Studio, or anything else built on llama.cpp, grab the GGUF builds from superwhisper/s1-mini-GGUF. You can use it in your own dictation app too, just check the license first.

Releases are tagged, so you can pin one: from_pretrained("superwhisper/s1-mini", revision="v1").

Model overview

[!NOTE] The Hub sidebar reports 0.8B parameters for this repo. config.json sets tie_word_embeddings, but model.safetensors still stores lm_head.weight as a materialized copy of the input embedding, so the 155.6M-parameter embedding is counted twice: 751.6M tensor elements against 596.0M unique parameters. The layout is inherited from Qwen/Qwen3-0.6B, which reports 0.8B on the Hub for the same reason. The table above counts unique parameters.

Input. The model expects the system prompt, then a control line, a newline, and one raw ASR transcript, which will usually arrive lowercase and unpunctuated. That is the shape it was trained on.

Output. It returns the cleaned transcript as plain text and nothing else, with no preamble and no explanation. Under Structure: lists the output may contain Markdown bullets, and under Context: email it may contain blank lines separating a greeting, body and sign-off. When the input is nothing but filler or noise, the correct output is an empty string, and that is what you get.

Source-model Transformers quickstart

Qwen3 support landed in transformers 4.51.0; with anything older you will get KeyError: 'qwen3'.

pip install "transformers>=4.51.0" torch

from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL = "superwhisper/s1-mini"

tok = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(MODEL, torch_dtype="auto")

# Required. Use this exact system prompt.
SYSTEM = (
    "You are a text normalizer for speech-to-text transcripts. The input begins "
    "with a control line specifying the styling, structure, and context settings; "
    "clean the transcript to match those settings and output only the cleaned text."
)

def normalize(transcript, styling="semi-formal", structure="prose", context="general"):
    control = f"[Styling: {styling}] [Structure: {structure}] [Context: {context}]"
    messages = [
        {"role": "system", "content": SYSTEM},
        {"role": "user", "content": f"{control}\n{transcript}"},
    ]
    text = tok.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True,
        enable_thinking=False,      # required, see below
    )
    inputs = tok(text, return_tensors="pt").to(model.device)
    out = model.generate(**inputs, max_new_tokens=1024, do_sample=False)
    return tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)


raw = "so um i need to like send the the report by uh friday no wait make that thursday"
print(normalize(raw))
# I need to send the report by Thursday.

torch_dtype="auto" picks up the BF16 in config.json. At 0.6B this runs comfortably on CPU; add device_map="auto" to place it on a GPU automatically (requires accelerate).

The control line

Every input starts with a control line, then a newline, then the transcript:

[Styling: <value>] [Structure: <value>] [Context: <value>]
<raw transcript>

The three axes are independent and every combination was trained.

[!IMPORTANT] The system prompt and the control line are part of the input format the model was trained on. Skip either one, change the system prompt's wording, or send values outside the trained sets, and the model can hallucinate or produce garbled output. Always send both, exactly as shown.

Styling

The register decides how much of the speaker's voice survives into the written text.

Here is the same input under all four registers:

Input: hmm im gonna be late theres a cute dog outside i cant just walk past him

Filled pauses like um and uh are removed in every register. Apostrophes are decided by the register rather than the raw transcript, so the input can arrive as im or I'm and the output comes out the same either way.

Structure

prose keeps everything in sentences and paragraphs. lists permits the model to break enumerable content into Markdown bullets, and it is deliberately conservative about it: it wants at least three items, and content that is not clearly an enumeration stays as prose. Here is the same input under both values:

Input: so for the trip we need to pack sunscreen and then also a first aid kit and um chargers for everything

Structure: prose

So for the trip, we need to pack sunscreen and then also a first aid kit and chargers for everything.

Structure: lists

So for the trip, we need to pack:
- Sunscreen
- A first aid kit
- Chargers for everything

Context

general produces flowing text, while email reshapes the transcript into email layout, with a greeting line, the body and a sign-off block separated by blank lines. Here is the same input under both values:

Input: hey sarah just wanted to follow up on the proposal can you send the numbers by end of week thanks john

Context: general

Hey Sarah, just wanted to follow up on the proposal. Can you send the numbers by end of week? Thanks, John.

Context: email

Hey Sarah,

Just wanted to follow up on the proposal. Can you send the numbers by end of week?

Thanks,
John

Examples

All of these were measured on the BF16 weights in this repo with greedy decoding, under [Styling: semi-formal] [Structure: prose] [Context: general].

Set enable_thinking=False

The chat template comes from Qwen3 unchanged, and Qwen3 turns on thinking mode by default. S1-mini was trained with thinking off and has no reasoning traces in its training data.

[!WARNING] If you leave the flag out you will usually get no usable output at all: the model emits an empty <think> block and stops. This is the single most common way to get a blank result from this model.

The flag makes the template emit an empty think block before the assistant turn. That is the exact prefix the model saw during training. The template ships as chat_template.jinja in this repo, so apply_chat_template picks it up with no extra configuration. If you build prompts by hand instead, the literal string is:

<|im_start|>system
You are a text normalizer for speech-to-text transcripts. The input begins with a control line specifying the styling, structure, and context settings; clean the transcript to match those settings and output only the cleaned text.<|im_end|>
<|im_start|>user
[Styling: semi-formal] [Structure: prose] [Context: general]
<raw transcript><|im_end|>
<|im_start|>assistant
<think>

</think>

Written out, the assistant prefix is <|im_start|>assistant\n<think>\n\n</think>\n\n, with two newlines inside the think block and two more after it.

Deployment

vLLM. Serve normally, then disable thinking per request:

vllm serve superwhisper/s1-mini

curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "superwhisper/s1-mini",
  "messages": [
    {"role": "system", "content": "You are a text normalizer for speech-to-text transcripts. The input begins with a control line specifying the styling, structure, and context settings; clean the transcript to match those settings and output only the cleaned text."},
    {"role": "user", "content": "[Styling: semi-formal] [Structure: prose] [Context: general]\nso um send the report by uh friday"}
  ],
  "temperature": 0,
  "chat_template_kwargs": {"enable_thinking": false}
}'

SGLang. The same request shape works here, since chat_template_kwargs is supported on the OpenAI-compatible endpoint.

python -m sglang.launch_server --model-path superwhisper/s1-mini

llama.cpp. Use the GGUF builds. The template must be applied with thinking disabled, which means passing --jinja together with --chat-template-kwargs. Don't substitute --reasoning-budget 0, which suppresses the think block a different way and degrades the output:

llama-server -hf superwhisper/s1-mini-GGUF:Q4_K_M --jinja --chat-template-kwargs '{"enable_thinking":false}' --temp 0

Ollama and LM Studio. Both work from the GGUF builds, but both inherit Qwen3's thinking-on-by-default template. Make sure the assistant turn begins with the empty think block shown above, or you will get blank output.

Best practices

1. Send the system prompt and the control line, exactly as documented. They are the only steering mechanism, and the model was never trained without them.
2. Decode greedily. generation_config.json already ships do_sample: false, and for good reason: normalization is a deterministic transformation, and sampling only adds variance. If you override the config, use temperature 0.
3. Size max_new_tokens to the input. Output length closely tracks input length; 1.3 × input_tokens + 32 is a safe ceiling, and much cheaper than leaving it at 1024.
4. Chunk long transcripts at sentence boundaries. The model is built for dictation-length input; keep single passes under roughly 1,000 tokens.
5. Expect an empty string sometimes. Filler-only input returns nothing, and your pipeline should treat that as a valid result rather than a failure.

Evaluation

Evaluated on a held-out English test set of 7,519 cases covering real ASR output and synthetic stress sets for numbers, self-corrections, lists, email and adversarial inputs: 94.8% token accuracy, measured greedy on the Q4_K_M GGUF build. The BF16 weights here should do at least as well.

Using S1-mini in your own app

S1-mini is Apache 2.0 plus a naming clause, the same base license it inherits from Qwen3-0.6B, so it can be embedded in open-source and commercial software alike: dictation apps, meeting-notes tools, live captioning, voice-driven editors, or any pipeline that has to turn raw ASR output into text a person will read.

It is a post-processing stage rather than a standalone system:

audio ──▶ ASR (Whisper, Parakeet, …) ──▶ S1-mini ──▶ clean text

The ASR's raw transcript becomes the transcript line, your app's settings choose the three control-line values, and the model returns text ready to display. At 0.6B it is small enough to ship on-device, and the GGUF builds exist for exactly that.

Nothing about the model is Superwhisper-specific. The two things to get right in any integration are the input format documented above and the thinking flag; nearly every integration bug traces back to one of those.

[!IMPORTANT] Read the LICENSE before you ship. Apache 2.0 is permissive but not obligation-free: you must retain the license text and attribution notices, and state significant changes if you redistribute a modified version. It also carries one additional term: the model must keep its name, "S1-mini" by "Superwhisper", with that exact capitalization, wherever it's used. If you are bundling S1-mini into a commercial dictation app or redistributing the weights yourself, confirm the terms cover your case rather than assuming they do.

License

S1-mini is released under Apache 2.0, which it inherits from Qwen3-0.6B, plus one additional term: wherever it's used, it must keep its name, "S1-mini" by "Superwhisper", with that exact capitalization. See LICENSE.

Citation

@misc{s1mini2026,
  title  = {S1-mini: a small text normalizer for speech-to-text output},
  author = {Superwhisper},
  year   = {2026},
  url    = {https://huggingface.co/superwhisper/s1-mini}
}

Built on Qwen3:

@misc{qwen3technicalreport,
  title         = {Qwen3 Technical Report},
  author        = {Qwen Team},
  year          = {2025},
  eprint        = {2505.09388},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2505.09388}
}