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 13 days ago

Commit

8955334

1 Parent(s): 07be031

Explicitly mention naming clause

Browse files

Files changed (1) hide show

README.md +4 -3

README.md CHANGED Viewed

@@ -64,7 +64,7 @@ Releases are tagged, so you can pin one:
 | Precision | BF16 |
 | Recommended input | Up to ~1,000 tokens; chunk longer transcripts |
 | Language | English |
-| License | Apache 2.0 |
 > [!NOTE]
 > The Hub sidebar reports 0.8B parameters for this repo. `config.json` sets
@@ -351,8 +351,9 @@ 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, the same 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.

 | Precision | BF16 |
 | Recommended input | Up to ~1,000 tokens; chunk longer transcripts |
 | Language | English |
+| License | Apache 2.0 + naming clause ([LICENSE](LICENSE)) |
 > [!NOTE]
 > The Hub sidebar reports 0.8B parameters for this repo. `config.json` sets
 ## 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.