---
base_model: Qwen/Qwen3-1.7B
library_name: peft
pipeline_tag: text-generation
license: apache-2.0
language:
  - ii
  - zh
  - en
tags:
  - qwen3
  - lora
  - nuosu
  - liangshan-yi
  - machine-translation
  - low-resource-language
  - academic-research
---

# Qwen3-1.7B Nuosu MT LoRA

这是面向凉山规范彝文（诺苏语）机器翻译的研究型 LoRA adapter。它不包含
Qwen3-1.7B 底模权重，使用时必须单独获得 `Qwen/Qwen3-1.7B`。

This repository contains a research LoRA adapter for Chinese–Standard
Liangshan Yi (Nuosu) and related short-form translation experiments. It is an
adapter-only release; obtain the base model separately.

## Intended use

- Chinese ↔ Standard Liangshan Yi translation research;
- short dictionary and sentence translation experiments;
- reproducible low-resource language adaptation studies.

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。

## Base model and reproducibility

- Base model: `Qwen/Qwen3-1.7B`
- Base revision: `70d244cc86ccca08cf5af4e1e306ecf908b1ad5e`
- Training code revision: `90b7d6c3d71e025e1336a2a585389f1dedab9b6f`
- Chat entrypoint fix: `3c7f17f012e5a483c367ff7b5b16e905e1b2c7dd`
- Dataset projection: `nuosu-mt-clean-recover-v20260808`
- Seed: `42`
- LoRA: rank 64, alpha 128, dropout 0.05, all-linear targets
- Training: BF16, one SFT epoch, completion-only loss

The 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.

## Data

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.

## Evaluation

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`.

## Usage

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_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.

## Citation

```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:

- https://github.com/NiceAsiv/nuosu-llm
- https://github.com/NiceAsiv/nuosu-corpus