Instructions to use superwhisper/s1-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use superwhisper/s1-mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="superwhisper/s1-mini")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("superwhisper/s1-mini")
model = AutoModelForCausalLM.from_pretrained("superwhisper/s1-mini", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use superwhisper/s1-mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "superwhisper/s1-mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "superwhisper/s1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/superwhisper/s1-mini

SGLang

How to use superwhisper/s1-mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "superwhisper/s1-mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "superwhisper/s1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "superwhisper/s1-mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "superwhisper/s1-mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use superwhisper/s1-mini with Docker Model Runner:
```
docker model run hf.co/superwhisper/s1-mini
```

Softmaximalist commited on 18 days ago

Commit

f362dd7

1 Parent(s): 7f80075

Add version badge

Browse files

Files changed (1) hide show

README.md +6 -3

README.md CHANGED Viewed

@@ -18,15 +18,16 @@ tags:
   - qwen3
 ---
 <div align="center">
   <img src="./banner.jpg" alt="s1-mini banner" width="100%">
   [![Website](https://img.shields.io/badge/Website-blue?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSJ3aGl0ZSIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxjaXJjbGUgY3g9IjEyIiBjeT0iMTIiIHI9IjEwIi8+PGxpbmUgeDE9IjIiIHkxPSIxMiIgeDI9IjIyIiB5Mj0iMTIiLz48cGF0aCBkPSJNMTIgMmExNS4zIDE1LjMgMCAwIDEgNCAxMCAxNS4zIDE1LjMgMCAwIDEtNCAxMCAxNS4zIDE1LjMgMCAwIDEtNC0xMCAxNS4zIDE1LjMgMCAwIDEgNC0xMHoiLz48L3N2Zz4=)](https://superwhisper.com)
   [![Discord](https://img.shields.io/badge/Discord-5865F2?logo=discord&logoColor=white)](https://discord.gg/tF98XvJNvB)
 </div>
-# s1-mini
 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
@@ -41,8 +42,10 @@ 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](https://huggingface.co/Qwen/Qwen3-0.6B). If
-you want to run it in llama.cpp, Ollama or LM Studio, grab the GGUF builds from
 [superwhisper/s1-mini-GGUF](https://huggingface.co/superwhisper/s1-mini-GGUF).
 Releases are tagged, so you can pin one:
 `from_pretrained("superwhisper/s1-mini", revision="v1")`.

   - qwen3
 ---
+# s1-mini — by [Superwhisper](https://superwhisper.com)
 <div align="center">
   <img src="./banner.jpg" alt="s1-mini banner" width="100%">
   [![Website](https://img.shields.io/badge/Website-blue?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSJ3aGl0ZSIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxjaXJjbGUgY3g9IjEyIiBjeT0iMTIiIHI9IjEwIi8+PGxpbmUgeDE9IjIiIHkxPSIxMiIgeDI9IjIyIiB5Mj0iMTIiLz48cGF0aCBkPSJNMTIgMmExNS4zIDE1LjMgMCAwIDEgNCAxMCAxNS4zIDE1LjMgMCAwIDEtNCAxMCAxNS4zIDE1LjMgMCAwIDEtNC0xMCAxNS4zIDE1LjMgMCAwIDEgNC0xMHoiLz48L3N2Zz4=)](https://superwhisper.com)
   [![Discord](https://img.shields.io/badge/Discord-5865F2?logo=discord&logoColor=white)](https://discord.gg/tF98XvJNvB)
+  [![v1](https://img.shields.io/badge/v1-2EA44F?logo=data:image/svg+xml;base64,PHN2ZyB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciIHZpZXdCb3g9IjAgMCAyNCAyNCIgZmlsbD0ibm9uZSIgc3Ryb2tlPSJ3aGl0ZSIgc3Ryb2tlLXdpZHRoPSIyIiBzdHJva2UtbGluZWNhcD0icm91bmQiIHN0cm9rZS1saW5lam9pbj0icm91bmQiPjxwYXRoIGQ9Ik0yMC41OSAxMy40MUwxMy40MiAyMC41OGEyIDIgMCAwIDEtMi44MyAwTDIgMTJWMmgxMGw4LjU5IDguNTlhMiAyIDAgMCAxIDAgMi44MnoiLz48bGluZSB4MT0iNyIgeTE9IjciIHgyPSI3LjAxIiB5Mj0iNyIvPjwvc3ZnPg==&logoColor=white)](https://huggingface.co/superwhisper/s1-mini/tree/v1)
 </div>
 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
 with a control line at the top of the input.
 Fine-tuned from [Qwen/Qwen3-0.6B](https://huggingface.co/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](https://huggingface.co/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")`.