Text Generation
Transformers
Safetensors
Chinese
English
qwen3
qwen3-asr
transcript-normalization
chinese
code-switching
full-finetuning
spelling-normalization
numbered-lists
numeric-normalization
conversational
text-generation-inference
Instructions to use WIlfLin/S1-mini-Chinese-1106 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WIlfLin/S1-mini-Chinese-1106 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="WIlfLin/S1-mini-Chinese-1106") 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-1106") model = AutoModelForCausalLM.from_pretrained("WIlfLin/S1-mini-Chinese-1106", 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-1106 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-1106" # 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-1106", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/WIlfLin/S1-mini-Chinese-1106
- SGLang
How to use WIlfLin/S1-mini-Chinese-1106 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-1106" \ --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-1106", "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-1106" \ --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-1106", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use WIlfLin/S1-mini-Chinese-1106 with Docker Model Runner:
docker model run hf.co/WIlfLin/S1-mini-Chinese-1106
Download evaluation-summary.json from WIlfLin/S1-mini-Chinese-1106: direct link, hf CLI and curl.
- Browser
- Download file 20.4 kB
-
https://huggingface.co/WIlfLin/S1-mini-Chinese-1106/resolve/main/evaluation-summary.json
- Command line
-
hf download hf://WIlfLin/S1-mini-Chinese-1106/evaluation-summary.json
-
curl -L -o evaluation-summary.json https://huggingface.co/WIlfLin/S1-mini-Chinese-1106/resolve/main/evaluation-summary.json
20.4 kB
| { | |
| "scope": "Reused offline text development tests; not independent blind accuracy or natural audio acceptance.", | |
| "precision": "BF16", | |
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| "exact": 2, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| } | |
| }, | |
| "public400": { | |
| "all": { | |
| "n": 400, | |
| "lexical_exact": 232, | |
| "exact": 118, | |
| "cer": 0.09368478857770456, | |
| "truncated": 0 | |
| }, | |
| "test_en": { | |
| "n": 100, | |
| "lexical_exact": 67, | |
| "exact": 63, | |
| "cer": 0.07500479570305006, | |
| "truncated": 0 | |
| }, | |
| "test_identity": { | |
| "n": 100, | |
| "lexical_exact": 88, | |
| "exact": 37, | |
| "cer": 0.019711902956785442, | |
| "truncated": 0 | |
| }, | |
| "test_zh": { | |
| "n": 200, | |
| "lexical_exact": 77, | |
| "exact": 18, | |
| "cer": 0.16945200155460552, | |
| "truncated": 0 | |
| } | |
| }, | |
| "intent": { | |
| "all": { | |
| "n": 326, | |
| "lexical_exact": 318, | |
| "exact": 318, | |
| "cer": 0.0026373626373626374, | |
| "truncated": 0 | |
| }, | |
| "literal_identity": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "meaning_identity": { | |
| "n": 2, | |
| "lexical_exact": 2, | |
| "exact": 2, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "question_identity": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "question_list": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "repeated_display": { | |
| "n": 16, | |
| "lexical_exact": 16, | |
| "exact": 16, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "repeated_letters": { | |
| "n": 16, | |
| "lexical_exact": 16, | |
| "exact": 16, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_acronym": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_context": { | |
| "n": 2, | |
| "lexical_exact": 2, | |
| "exact": 2, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_display": { | |
| "n": 3, | |
| "lexical_exact": 3, | |
| "exact": 3, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_person": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_repair": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_repetition": { | |
| "n": 1, | |
| "lexical_exact": 1, | |
| "exact": 1, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "reported_word": { | |
| "n": 4, | |
| "lexical_exact": 4, | |
| "exact": 4, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "semantic_case": { | |
| "n": 42, | |
| "lexical_exact": 42, | |
| "exact": 42, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "unseen_acronym_word": { | |
| "n": 12, | |
| "lexical_exact": 12, | |
| "exact": 12, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "unseen_brand_word": { | |
| "n": 12, | |
| "lexical_exact": 7, | |
| "exact": 7, | |
| "cer": 0.0457516339869281, | |
| "truncated": 0 | |
| }, | |
| "unseen_display": { | |
| "n": 78, | |
| "lexical_exact": 78, | |
| "exact": 78, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "unseen_ordinary_word": { | |
| "n": 36, | |
| "lexical_exact": 36, | |
| "exact": 36, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "unseen_person_word": { | |
| "n": 18, | |
| "lexical_exact": 18, | |
| "exact": 18, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "unseen_repair": { | |
| "n": 78, | |
| "lexical_exact": 75, | |
| "exact": 75, | |
| "cer": 0.004901960784313725, | |
| "truncated": 0 | |
| } | |
| }, | |
| "nonce": { | |
| "all": { | |
| "n": 200, | |
| "lexical_exact": 197, | |
| "exact": 197, | |
| "cer": 0.002153183635804368, | |
| "truncated": 0 | |
| }, | |
| "unseen_nonce_word": { | |
| "n": 200, | |
| "lexical_exact": 197, | |
| "exact": 197, | |
| "cer": 0.002153183635804368, | |
| "truncated": 0 | |
| } | |
| }, | |
| "math236": { | |
| "all": { | |
| "n": 236, | |
| "lexical_exact": 235, | |
| "exact": 235, | |
| "cer": 0.0013136288998357964, | |
| "truncated": 0 | |
| }, | |
| "math_equation_correct": { | |
| "n": 53, | |
| "lexical_exact": 53, | |
| "exact": 53, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "math_equation_wrong_rhs": { | |
| "n": 51, | |
| "lexical_exact": 51, | |
| "exact": 51, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "math_expression_no_eq": { | |
| "n": 42, | |
| "lexical_exact": 42, | |
| "exact": 42, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "math_non_math_contrast": { | |
| "n": 5, | |
| "lexical_exact": 4, | |
| "exact": 4, | |
| "cer": 0.034482758620689655, | |
| "truncated": 0 | |
| }, | |
| "math_question_preserve": { | |
| "n": 29, | |
| "lexical_exact": 29, | |
| "exact": 29, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "math_self_corrected": { | |
| "n": 14, | |
| "lexical_exact": 14, | |
| "exact": 14, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| }, | |
| "math_unfinished_dangling": { | |
| "n": 42, | |
| "lexical_exact": 42, | |
| "exact": 42, | |
| "cer": 0.0, | |
| "truncated": 0 | |
| } | |
| } | |
| }, | |
| "live_service": { | |
| "engine": "vLLM 0.23.0 BF16, max-num-seqs=1", | |
| "model_enabled_smoke_cases": 23, | |
| "disabled_passthrough_cases": 1, | |
| "scope": "Targeted deployment smoke only; not a broad accuracy benchmark." | |
| }, | |
| "precision_control": { | |
| "diagnostic_batch1": { | |
| "lexical_exact": 75, | |
| "strict_exact": 75, | |
| "n": 97 | |
| }, | |
| "diagnostic_FP32_batch16": { | |
| "lexical_exact": 75, | |
| "strict_exact": 75, | |
| "n": 97 | |
| } | |
| }, | |
| "metric_definition": { | |
| "lexical_exact": "Exact equality after removing non-alphanumeric characters; letter case and digits retained.", | |
| "strict_exact": "Output and reference equal after stripping leading and trailing whitespace.", | |
| "cer": "Edit distance over lexical characters divided by total reference lexical characters." | |
| }, | |
| "limits": [ | |
| "Repeated diagnostic use informed development; not blind testing.", | |
| "Not comparable to upstream 94.8% token accuracy.", | |
| "A known 501 versus 5001 transcription changes with BF16 batch shape; FP32 fixed that observed case, not a general guarantee.", | |
| "Some formatting, corrections and technical-name cases still fail." | |
| ] | |
| } | |