tartuNLP
/

Qwen2.5-3B-Instruct-hsb-dsb

@@ -1,199 +1,92 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+language:
+- hsb
+- dsb
+datasets:
+- HuggingFaceFW/fineweb-2
+- CohereLabs/aya_dataset
+- Magpie-Align/Magpie-Llama-3.1-Pro-MT-300K-Filtered
+- OpenAssistant/oasst2
+- ai2-adapt-dev/flan_v2_converted
+- utter-project/EuroBlocks-SFT-Synthetic-1124
+base_model:
+- Qwen/Qwen2.5-3B-Instruct
 ---
+# Qwen2.5-3B-Instruct-hsb-dsb
+This model is the TartuNLP submission to the **WMT25 Shared Task on Limited Resource Slavic Languages**, covering **Upper Sorbian** (hsb) and **Lower Sorbian** (dsb).
+It is based on **Qwen2.5-3B-Instruct** and adapted through continued pretraining on Sorbian monolingual and parallel data, plus general instruction-tuning datasets.
+The model jointly supports machine translation (MT) and question answering (QA) for both Sorbian languages, achieving the top rank in the shared task.
+⚠️ **Note:** This model is research-focused and has not been tested for general usage. Use at your own risk.
+## Example usage
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "tartuNLP/Qwen2.5-3B-Instruct-hsb-dsb"
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+messages = [
+    {"role": "system", "content": "Translate the following text from German to Upper Sorbian."},
+    {"role": "user", "content": "Wie lange willst du noch bleiben?"}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
+## Shared task results
+Results shared by the organizers ([source](https://github.com/TUM-NLP/llms-limited-resources2025/blob/main/results.md)).
+**Upper Sorbian:**
+|              | DE-HSB    | points | HSB-QA    | points | final points |
+|--------------|-----------|--------|-----------|--------|--------------|
+| **TartuNLP** | 86.33     | 4      | **58.10** | 4      | 8            |
+| NRC          | **87.20** | 4      | 29.05     | 1      | 5            |
+| SDKM         | 75.73     | 2      | 55.24     | 3      | 5            |
+| baseline     | 13.88     | 1      | 42.86     | 2      | 3            |
+**Lower Sorbian:**
+|              | DE-DSB    | points | DSB-QA    | points | final points |
+|--------------|-----------|--------|-----------|--------|--------------|
+| **TartuNLP** | 78.20     | 4      | **57.56** | 4      | 8            |
+| NRC          | **78.24** | 4      | 32.20     | 1      | 5            |
+| SDKM         | 64.34     | 2      | 51.71     | 3      | 5            |
+| baseline     | 12.21     | 1      | 45.85     | 2      | 3            |
+## Training details
+- Total training tokens: ~1.2B
+- Sequence length: 4096
+- Training hardware: LUMI supercomputer (AMD MI250x GPUs)
+- Training time: ~139 GPU-hours
+## Citation info
+To be announced.