ikellllllll
/

vqa-base-model

@@ -1,207 +1,123 @@
 ---
 base_model: Qwen/Qwen2.5-VL-3B-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen2.5-VL-3B-Instruct
 - lora
 - transformers
 ---
-# Model Card for Model ID
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 ## Model Details
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-### Framework versions
-- PEFT 0.17.1

 ---
 base_model: Qwen/Qwen2.5-VL-3B-Instruct
 library_name: peft
+pipeline_tag: image-text-to-text
 tags:
+- vision
+- vqa
+- qwen2.5-vl
 - lora
 - transformers
+license: apache-2.0
 ---
+# VQA Base Model
+Fine-tuned VQA model using Qwen2.5-VL-3B-Instruct with LoRA.
+**Performance:**
+- **Validation Accuracy: 88.69%** (345/389)
+- **High-res (512px) Accuracy: 89.72%** (349/389)
+- Baseline model for the project
+**Part of 3-Model Ensemble:**
+- Combined with Improved Epoch 1 and Improved Epoch 2
+- **Ensemble Validation: 90.75%**
+- **Ensemble Test (Kaggle): 91.82%**
 ## Model Details
+- **Base Model:** Qwen/Qwen2.5-VL-3B-Instruct
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Quantization:** 4-bit (NF4)
+- **Hardware:** NVIDIA A100 40GB
+- **Training:** Fine-tuned on VQA dataset (604 samples)
+## LoRA Configuration
+```python
+{
+    "r": 16,
+    "lora_alpha": 32,
+    "lora_dropout": 0.05,
+    "target_modules": [
+        "q_proj", "k_proj", "v_proj", "o_proj",
+        "gate_proj", "up_proj", "down_proj"
+    ]
+}
+```
+## Usage
+```python
+from transformers import AutoModelForVision2Seq, AutoProcessor, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+# Load model with 4-bit quantization
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+base_model = AutoModelForVision2Seq.from_pretrained(
+    "Qwen/Qwen2.5-VL-3B-Instruct",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+model = PeftModel.from_pretrained(base_model, "ikellllllll/vqa-base-model")
+processor = AutoProcessor.from_pretrained(
+    "Qwen/Qwen2.5-VL-3B-Instruct",
+    min_pixels=512*512,
+    max_pixels=512*512,
+    trust_remote_code=True
+)
+# IMPORTANT: Set left-padding for decoder-only models
+processor.tokenizer.padding_side = 'left'
+```
+## Inference Settings
+- **Image Resolution:** 512×512px (higher resolution recommended)
+- **Batch Size:** 32 (for A100 40GB)
+- **Padding:** Left-padding (critical for decoder-only models!)
+## Dataset
+- **Training:** 604 VQA samples
+- **Validation:** 389 VQA samples
+- **Test:** 3,887 VQA samples
+## Performance Notes
+- 384px resolution: 88.69% validation accuracy
+- 512px resolution: 89.72% validation accuracy (+1.03%)
+- **Higher resolution significantly improves performance**
+## Links
+- **GitHub Repository:** [SSAFY_AI_competition](https://github.com/ikellllllll/SSAFY_AI_competition)
+- **Related Models:**
+  - [vqa-improved-epoch1](https://huggingface.co/ikellllllll/vqa-improved-epoch1) (90.49%)
+  - [vqa-improved-epoch2](https://huggingface.co/ikellllllll/vqa-improved-epoch2) (90.23%)
+## Citation
+```bibtex
+@misc{vqa-base-model,
+  author = {Team 203},
+  title = {VQA Base Model},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/ikellllllll/vqa-base-model}}
+}
+```
+## License
+Apache 2.0