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)

# pip install -U transformers accelerate
# 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=256)
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 Aug 19

Commit

65d7289

1 Parent(s): 8955334

Update to v076

Browse files

Files changed (2) hide show

README.md +3 -3
model.safetensors +1 -1

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ 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 6,183 English cases it reaches 94.4% token accuracy, and
 the quantized build is a 462 MB file that runs comfortably on a laptop CPU.
 This is release v1, and it covers English only. S1-mini is not a chat model
@@ -344,9 +344,9 @@ with the empty think block shown above, or you will get blank output.
 ## Evaluation
-Evaluated on a held-out English test set of 6,183 cases covering real ASR
 output and synthetic stress sets for numbers, self-corrections, lists, email
-and adversarial inputs: 94.4% 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

 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 MB file that runs comfortably on a laptop CPU.
 This is release v1, and it covers English only. S1-mini is not a chat model
 ## 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

model.safetensors CHANGED Viewed

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