Instructions to use WIlfLin/S1-mini-Chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WIlfLin/S1-mini-Chinese with Transformers:

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

pipe = pipeline("text-generation", model="WIlfLin/S1-mini-Chinese")
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("WIlfLin/S1-mini-Chinese")
model = AutoModelForCausalLM.from_pretrained("WIlfLin/S1-mini-Chinese", 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 WIlfLin/S1-mini-Chinese with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WIlfLin/S1-mini-Chinese"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WIlfLin/S1-mini-Chinese",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WIlfLin/S1-mini-Chinese

SGLang

How to use WIlfLin/S1-mini-Chinese 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 "WIlfLin/S1-mini-Chinese" \
    --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": "WIlfLin/S1-mini-Chinese",
		"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 "WIlfLin/S1-mini-Chinese" \
        --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": "WIlfLin/S1-mini-Chinese",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use WIlfLin/S1-mini-Chinese with Docker Model Runner:
```
docker model run hf.co/WIlfLin/S1-mini-Chinese
```

S1-mini-Chinese

File size: 695 Bytes

eb970af
99de2e5
eb970af
 
 
 
 
 
 
 
 
 
 
 
 
 
 
99de2e5
 
 
eb970af

{
  "version": "spelled-letter-runs-v2",
  "stage": "before model_messages, not after generation",
  "enabled_by_application": true,
  "model_weights_unchanged": true,
  "weights_sha256": "54602b3a2e0c484fe7ae796a4290969d3a1cc58e099c83e59948fe94889150a0",
  "supported": [
    "tight hyphen-separated single letters",
    "horizontal-space-separated single letters"
  ],
  "literal_exclusions": [
    "inline/fenced code",
    "URLs",
    "emails",
    "paths",
    "assignments"
  ],
  "not_applied_to_published_public400_raw_model_metrics": true,
  "case_policy": "lowercase joined spellings; capitalize the first letter only at sentence start",
  "already_contiguous_words_unchanged": true
}