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
```

Demo for this model on Spaces

by multimodalart - opened 1 day ago

Discussion

multimodalart

1 day ago

Hi @superwhisper 🤗

I'm Apolinario, from the open-source team at Hugging Face. Congrats and thanks for open-sourcing superwhisper/s1-mini on the Hub! We were excited about this work and built with an agent an interactive demo app of it on Hugging Face Spaces, running on a free ZeroGPU infrastructure.

Here's a link to the demo: https://huggingface.co/spaces/hugging-apps/s1-mini-transcript-cleanup

We would love to transfer this demo to you or your organization. Would you like this demo to live under your own account or organization? If so just let me know here which username to transfer to, and we'll transfer the Space over to you, we hope it can give your work more visibility, discoverability and allows folks to try it out.

(If you have any questions or just want to chat more about this, you can find me on Twitter, LinkedIn or apolinario @ huggingface.co)

Cheers,

Poli

softmaximalist

Superwhisper org 1 day ago

Hi Apolinario. This is really cool! We would like to have the demo under the organization which is superwhisper.

multimodalart

1 day ago

•

edited 1 day ago

Thank you for open sourcing it @softmaximalist :)

Transferred to: https://huggingface.co/spaces/superwhisper/s1-mini-transcript-cleanup with a ZeroGPU grant
Free to post about, put the demo on the project page, reference it on repos, etc. as you wish

Also feel more than free to take ownership and make modifications as you see fit, maybe adding a transcription model step to make it end-to-end. For future releases from you, would be great if they already came with a demos! You can use this one as a blueprint to build by yourself or with the help of an agent, you can load the huggingface-spaces skill on Claude Code, Codex, Hermes, Pi, etc.

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