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MDK-YLC
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Model Details
Model Provider
MDK-YLC
Model Tree
Qwen/Qwen3.5-2B
Input Modalities
Output Modalities
Supported Functionality
| System | MT All chrF++ | CS-UK BLEU | EN-UK BLEU | QA All EM | SC All EM | GC All EM | Math EM |
|---|---|---|---|---|---|---|---|
| Baseline | 18.46 | 5.38 | 0.55 | 36.33 | 8.38 | 2.18 | 20.00 |
| TUMHN | 16.08 | 3.93 | 1.01 | 40.13 | 16.50 | 6.80 | 4.40 |
| Koshi | 16.80 | 6.01 | 0.70 | 46.99 | 52.50 | 30.55 | 0.40 |
| Ours (Zolint) | 49.86 | 23.66 | 15.43 | 41.15 | 66.43 | 45.18 | 21.60 |
r=16, α=32) on MT (en→uk) + GC + SC; keep best epoch by combined MT+GC+SC.bash
pip install torch transformers peft accelerate
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase_id = "Qwen/Qwen3.5-2B"lora_id = "MDK-YLC/Qwen3.5-2B-WMT26-Ukrainian-LoRA"tokenizer = AutoTokenizer.from_pretrained(lora_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(base_id,torch_dtype=torch.bfloat16,device_map="auto",trust_remote_code=True,)model = PeftModel.from_pretrained(model, lora_id)model.eval()
See inference_example.py in this repo for MT / QA / SC-style prompts.
| File | Description |
|---|---|
adapter_config.json | LoRA / PEFT config |
adapter_model.safetensors | LoRA weights |
tokenizer.json / tokenizer_config.json | Tokenizer |
chat_template.jinja | Chat template |
inference_example.py | Minimal runnable example |
Note: Adapter only — base weights come from Qwen/Qwen3.5-2B.
Zolint at WMT 2026: A Multitask LLM Submission for the Low-Resource Ukrainian Track
Hugging Face:MDK-YLC/Qwen3.5-2B-WMT26-Ukrainian-LoRA