---
language:
- si
base_model: Qwen/Qwen3.5-9B
library_name: peft
tags:
- sinhala
- headline-generation
- summarization
- singen
- lora
datasets:
- sinhala-nlp/NSINA-Headlines
metrics:
- rouge
---

# Qwen3.5-9B-NSINA-Headlines-si

A LoRA adapter for **Sinhala news headline generation**, fine-tuned from
[Qwen/Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) as part of the SinGen Sinhala text
generation benchmark.

## Usage

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

tok = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B", dtype="auto", device_map="auto")
model = PeftModel.from_pretrained(model, "sinhala-nlp/Qwen3.5-9B-NSINA-Headlines-si")
```

Prompts use the base model's chat template with thinking disabled, and the assistant
response begins with the `Headline:` prefix. Articles are trimmed to
2500 characters and then budgeted to fit `max_seq_len` from the lead.

## Training

| | |
|---|---|
| Training articles | 8000 |
| Instruction language | `si` |
| Epochs | 1.0 |
| Effective batch size | 16 |
| Learning rate | 0.0002 |
| Max sequence length | 2560 |
| LoRA r / alpha / dropout | 16 / 32 / 0.05 |
| Thinking during training | False |

## Evaluation

First 1000 instances of the NSINA-Headlines test split, ROUGE F1 x100 with
whitespace tokenization (the default `rouge_score` tokenizer strips non-ASCII and
zeroes out every Sinhala score):

| Metric | Score |
|---|---|
| ROUGE-1 | 29.20 |
| ROUGE-2 | 13.71 |
| ROUGE-L | 28.53 |

## Licence

This adapter inherits the licence of the base model; check the base model card
before redistributing. The training data is NSINA-Headlines, derived from scraped
Sri Lankan news content -- verify its terms on the dataset card, as they may be
more restrictive than the base model's licence.