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
Download data-provenance.json from WIlfLin/S1-mini-Chinese: direct link, hf CLI and curl.
- Browser
- Download file 5.25 kB
-
https://huggingface.co/WIlfLin/S1-mini-Chinese/resolve/main/data-provenance.json
- Command line
-
hf download hf://WIlfLin/S1-mini-Chinese/data-provenance.json
-
curl -L -o data-provenance.json https://huggingface.co/WIlfLin/S1-mini-Chinese/resolve/main/data-provenance.json
5.25 kB
| { | |
| "CS2W": { | |
| "source": "https://github.com/guozishan/CS2W", | |
| "revision": "be6e0519b433cfa9d8388b23308f1380716254fd", | |
| "license_declared": "CC-BY-SA-4.0" | |
| }, | |
| "Disfl-QA": { | |
| "source": "https://github.com/google-research-datasets/Disfl-QA", | |
| "revision": "1f0c16171c77b3d3408be92c485f11b8998a9189", | |
| "license_declared": "CC-BY-4.0 for dataset" | |
| }, | |
| "stage1_manifest": { | |
| "stage": "pilot-v1-not-natural-mixed-gold", | |
| "seed": 20261104, | |
| "rows": 9645, | |
| "sources": { | |
| "authored-synthetic CC0-1.0": 2407, | |
| "CS2W CC-BY-SA-4.0": 3983, | |
| "CS2W-derived identity CC-BY-SA-4.0": 1653, | |
| "Disfl-QA-derived identity CC-BY-4.0": 359, | |
| "Disfl-QA CC-BY-4.0": 1200, | |
| "authored CC0-1.0": 43 | |
| }, | |
| "tasks": { | |
| "filler": 1326, | |
| "cleanup": 5183, | |
| "identity": 2596, | |
| "empty": 325, | |
| "mixed": 192, | |
| "repair": 18, | |
| "list": 5 | |
| }, | |
| "rejected": { | |
| "cs2w_out_of_stage1_task": 1225, | |
| "cs2w_identity_or_non_deletion_review": 545, | |
| "heldout_near_jaccard_0.8": 22, | |
| "heldout_exact_cross_side": 8, | |
| "duplicate_pair": 79, | |
| "conservative_disflqa_id_family": 862, | |
| "conflicting_target_group_rows": 9 | |
| }, | |
| "source_sha256": { | |
| "CS2W-train.jsonl": "acf249f7e8077337fc8e4029e7c381e117f3bc780fef48c820e4f328e78f563a", | |
| "CS2W-val.jsonl": "1d89f155c7caecee9d63fc938c42226d43b52fe43f5644ee6c9190b38272ee9a", | |
| "CS2W-test.jsonl": "32ea92028c3f990b304e2dc5838f487a86a7725b99c9f12fc9696446ecfaab80", | |
| "Disfl-QA-train.json": "5407199d0c039de5b50cfc16d1ed4a3299c9127cb549da4e4a650b30f4e000eb", | |
| "Disfl-QA-dev.json": "b60e075b810b27a5130fd0aa2cfbc85753b71a0b30dcd2585f540f0a6afe6492", | |
| "Disfl-QA-test.json": "404801de916ebcb2caa82661dfd189c0520e2766db6838f6ff548088650e565e" | |
| }, | |
| "development_rows": 328, | |
| "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" | |
| ] | |
| }, | |
| "stage2_manifest": { | |
| "stage": "v2 safety replay", | |
| "rows": 14428, | |
| "tasks": { | |
| "identity": 2596, | |
| "list_identity": 500, | |
| "cleanup": 5183, | |
| "identity_replay": 2500, | |
| "filler": 1325, | |
| "mixed": 192, | |
| "focus_identity": 64, | |
| "quoted_identity": 384, | |
| "empty": 325, | |
| "list": 505, | |
| "demonstrative_identity": 240, | |
| "repetition_identity": 32, | |
| "demonstrative_cleanup": 120, | |
| "acknowledgement_identity": 132, | |
| "emphasis_identity": 96, | |
| "quoted_cleanup": 192, | |
| "repair": 18, | |
| "deictic_list_identity": 24 | |
| }, | |
| "synthetic_additions": 4796, | |
| "limits": "Acknowledgement and safety repetitions are deliberate sampling weights; additions are synthetic, not independent human gold." | |
| }, | |
| "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.", | |
| "stage3_manifest": { | |
| "rows": 7407, | |
| "sha256": "b6992bda2e32bce2ce493239ffd6f1c1d272fe02b717183588c5654fa6fd27d7", | |
| "policy": "numbered lists; technical models/version/parameter sizes Arabic; coarse round quantities Chinese; idioms preserved", | |
| "limits": "Authored synthetic plus replay, not natural human-labelled gold; user report examples held out.", | |
| "prior_training_replay_rows": 2500, | |
| "new_authored_synthetic_rows": 4907, | |
| "audit": { | |
| "rows": 7407, | |
| "unique_inputs_with_structure": 7331, | |
| "conflicting_input_labels": 0, | |
| "empty_targets": 51, | |
| "tasks": { | |
| "long_numbered_list": 1100, | |
| "numbered_list": 1800, | |
| "numeric_digits": 150, | |
| "identity": 446, | |
| "empty": 51, | |
| "technical_number": 579, | |
| "filler": 232, | |
| "numeric_quantity": 396, | |
| "identity_replay": 429, | |
| "cleanup": 916, | |
| "numeric_version": 150, | |
| "acknowledgement_identity": 22, | |
| "numbered_list_identity": 300, | |
| "numeric_time": 184, | |
| "mixed": 29, | |
| "repair": 4, | |
| "quoted_identity": 69, | |
| "demonstrative_identity": 45, | |
| "list_identity": 85, | |
| "coarse_quantity_identity": 160, | |
| "demonstrative_cleanup": 27, | |
| "list": 86, | |
| "idiom_identity": 88, | |
| "emphasis_identity": 11, | |
| "quoted_cleanup": 34, | |
| "repetition_identity": 4, | |
| "focus_identity": 6, | |
| "deictic_list_identity": 4 | |
| } | |
| } | |
| }, | |
| "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." | |
| } | |