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NiceAsiv
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Model Details
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NiceAsiv
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
This is not a production translation system. The full held-out evaluation is weak on the heterogeneous research test distribution, and the fixed gate was run under an explicitly recorded waiver. Native-speaker review is required before making semantic or orthographic claims.
本版本不宣称覆盖全部彝语方言，也不适合高风险或未经审核的正式翻译。
Qwen/Qwen3-1.7B70d244cc86ccca08cf5af4e1e306ecf908b1ad5e90b7d6c3d71e025e1336a2a585389f1dedab9b6f3c7f17f012e5a483c367ff7b5b16e905e1b2c7ddnuosu-mt-clean-recover-v2026080842The tokenizer adds 1,203 Yi syllable/radical tokens (vocabulary size 152,872); new token rows are initialized from the original subtoken embeddings and trained together with LoRA.
The target-only MT projection contains:
| Split | Records | Notes |
|---|---|---|
| train | 159,083 | 94,532 lexicon, 16,077 published, 39,512 sentence, 8,962 short |
| validation | 7,131 | held-out validation projection |
| research test | 8,558 | full held-out generation test |
Training data were cleaned by dropping exact meta-evaluation verdict targets and recovering usable corrected-translation prefixes. The release contains model files and evaluation metadata, not the source corpora. Source licensing, attribution, and redistribution conditions remain applicable.
Full held-out generation (8,558 records, greedy, no_think):
| Metric | Overall | Yi-target |
|---|---|---|
| Compact exact match | 3.76% | 2.47% |
| chrF2 | 10.93 | 13.70 |
| Reference contained | 5.68% | 3.35% |
| Replacement-character rate | 0.11% | 0.33% |
The 256-record gate reached 57.42% overall exact match, 60.68 chrF2 and
48.44% Yi exact match, but did not satisfy the strict gate thresholds; the
waiver is included under provenance/GATE_WAIVER.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_id = "Qwen/Qwen3-1.7B"adapter_id = "NiceAsiv/Qwen3-1.7B-Nuosu-MT"tokenizer = AutoTokenizer.from_pretrained(adapter_id)base = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype="auto", device_map="auto")base.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=64)model = PeftModel.from_pretrained(base, adapter_id)messages = [{"role": "user","content": "请将以下中文翻译为凉山规范彝文。只输出译文，不要解释。\n我今天去学校。",}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True,enable_thinking=False,)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)output = model.generate(**inputs, do_sample=False, max_new_tokens=256,eos_token_id=tokenizer.eos_token_id,)print(tokenizer.decode(output[0, inputs["input_ids"].shape[-1]:],skip_special_tokens=True).strip())
The repository's chat command defaults to the same deterministic no_think
mode. Use --thinking-mode thinking only when deliberately testing reasoning.
bibtex
@software{axi2026nuosumt,author = {Wuhe Axi},title = {Qwen3-1.7B Nuosu MT LoRA},year = {2026},institution = {Xi'an Jiaotong University},url = {https://huggingface.co/NiceAsiv/Qwen3-1.7B-Nuosu-MT}}
Please also cite the training code and the separately maintained corpus: