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: 2,763 Bytes
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"revision": "format-numbers-v5",
"weights_sha256": "54602b3a2e0c484fe7ae796a4290969d3a1cc58e099c83e59948fe94889150a0",
"public400": {
"previous_step400": {
"all": {
"n": 400,
"lexical_exact": 239,
"exact": 110,
"cer": 0.10115321252059307,
"truncated": 0
},
"test_en": {
"n": 100,
"lexical_exact": 64,
"exact": 57,
"cer": 0.09015921734126223,
"truncated": 0
},
"test_identity": {
"n": 100,
"lexical_exact": 92,
"exact": 38,
"cer": 0.014404852160727824,
"truncated": 0
},
"test_zh": {
"n": 200,
"lexical_exact": 83,
"exact": 15,
"cer": 0.16789739603575593,
"truncated": 0
}
},
"current": {
"all": {
"n": 400,
"lexical_exact": 232,
"exact": 108,
"cer": 0.10247116968698518,
"truncated": 0
},
"test_en": {
"n": 100,
"lexical_exact": 60,
"exact": 54,
"cer": 0.09246115480529446,
"truncated": 0
},
"test_identity": {
"n": 100,
"lexical_exact": 91,
"exact": 38,
"cer": 0.01592115238817286,
"truncated": 0
},
"test_zh": {
"n": 200,
"lexical_exact": 81,
"exact": 16,
"cer": 0.16712009327633112,
"truncated": 0
}
},
"decoding": {
"dtype": "bfloat16",
"temperature": 0,
"batch_size": 16,
"max_new_tokens": 256,
"backend": "Transformers 5.3 / PyTorch 2.10, CUDA, SDPA"
},
"scope": "Raw model outputs on the same reused 400 public text cases. Lexical metrics remove punctuation and retain case, numbers, negation and order; not semantic accuracy or a new blind benchmark."
},
"development20": {
"offline_exact": 18,
"sequential_orin_vllm_exact": 19,
"cases": 20,
"numbered_list_cases": 6,
"numbered_list_cases_rendered_as_numbered_lines": 6,
"scope": "Diagnostic probes, excluded from training inputs after lexical normalization. Runtime/reference mismatch retained equivalent Chinese quantity spelling. Backend/batching may change greedy BF16 output; results are not a new broad quality benchmark."
},
"token_metrics": {
"status": "not_recomputed_for_current_weights",
"historical_file": "history/step400/token-metrics.json"
},
"deployment_content_guard": "none; external configuration, not model weights",
"limits": [
"English lexical sentence matches decreased from 64/100 to 60/100.",
"Overall lexical CER increased from 10.115% to 10.247%.",
"Number/name correction and paragraph/list grouping are not universally reliable."
]
}
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