Text Generation
Transformers
Safetensors
Chinese
English
qwen3
transcript-normalization
chinese
qwen3-asr
code-switching
full-finetuning
numbered-lists
numeric-normalization
conversational
text-generation-inference
Instructions to use WIlfLin/S1-mini-Chinese with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 5,247 Bytes
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"revision": "be6e0519b433cfa9d8388b23308f1380716254fd",
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"license_declared": "CC-BY-4.0 for dataset"
},
"stage1_manifest": {
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"evaluation_policy": "Official test excluded from training and development selection; acceptance set used for development, not independent final proof",
"limits": [
"Synthetic mixed/noise templates are not natural mixed-language gold",
"Target-to-target identities require semantic quality review",
"Deletion-only source filter does not itself guarantee semantic preservation",
"Disfl-QA 22-character ID grouping is conservative, not a confirmed source paragraph mapping"
]
},
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"limits": "Acknowledgement and safety repetitions are deliberate sampling weights; additions are synthetic, not independent human gold."
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"notes": "Only filtered training splits and labelled authored/synthetic replay entered SFT. Evaluation splits excluded; exact cross-side and near-duplicate filtering applied as documented. Sampling repetitions are intentional. Dataset texts are not redistributed in this model repository.",
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"stage3_notes": "Additional synthetic numbered-list and numeric-style supervision plus prior training replay. Reported/development examples were excluded from training inputs after lexical normalization. No private recordings or user input history were used for training. Diagnostic probes are not an independent blind evaluation."
}
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