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qwen2_vl_finetune_lora_viet_chart_vqa
maianh511/qwen2_vl_finetune_lora_viet_chart_vqa
qwen2_vl_finetune_lora_viet_chart_vqa is a question answering model from maianh511. Use it when the input is a question plus a passage. The card lists the license as apache-2.0.
Both Qwen2-VL and Vintern-1B-v2 were fine-tuned with LoRA on the same Vietnamese chart dataset and evaluated on the same 700-sample test set for direct comparison.
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Public
Updated Aug 25, 2026
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At a glance
- Task
- Question Answering
- License
- apache-2.0
- Access
- Public
- Created
- Aug 25, 2026
- Updated
- Aug 25, 2026
- SHA
- e93389c0
Base models
Datasets
Model card
From the Hugging Face model README
Results — Qwen2-VL LoRA vs Vintern-LoRA
Both Qwen2-VL and Vintern-1B-v2 were fine-tuned with LoRA on the same Vietnamese chart dataset and evaluated on the same 700-sample test set for direct comparison.
Metric Comparison
| Metric | Qwen2-VL LoRA | Vintern-LoRA | Improvement (Vintern vs Qwen2-VL) |
|---|---|---|---|
| BLEU | 0.239 | 0.468 | +95.8% |
| METEOR | 0.456 | 0.703 | +54.2% |
| ROUGE-1 | 0.613 | 0.778 | +26.9% |
| ROUGE-2 | 0.491 | 0.676 | +37.7% |
| ROUGE-L | 0.565 | 0.735 | +30.1% |
| BERTScore | 0.786 | 0.903 | +14.9% |
Across all 700 evaluation samples, Vintern-LoRA consistently and substantially outperforms Qwen2-VL LoRA on every metric. The gap is largest on BLEU (+95.8%) and METEOR (+54.2%), suggesting Vintern-LoRA produces Vietnamese answers with far closer lexical and semantic alignment to the ground truth. Even on BERTScore, where both models already score relatively high, Vintern-LoRA maintains a clear lead (0.786 → 0.903).
Conclusion
Under identical fine-tuning setup and evaluation conditions, Vintern-1B-v2 (LoRA) demonstrates significantly stronger performance than Qwen2-VL (LoRA) on the Vietnamese chart QA task, making it the more suitable base model for this use case.
- Task
- Question Answering
- License
- apache-2.0
- Languages
- vi, en
- Created
- Aug 25, 2026
- Updated
- Aug 25, 2026