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argho229
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Model Details
Model Provider
argho229
Model Tree
Qwen/Qwen2.5-VL-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Metric | Score |
|---|---|
| Binary RS-VQA Accuracy | 91.4% |
| Bounding Box IoU | 0.742 |
| Inference Speed | ~1.8 s/image |
| Ensemble Accuracy (Tri-Brid) | 92.13% |
`python from peft import PeftModel from transformers import AutoModelForCausalLM, AutoProcessor import torch
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
base = AutoModelForCausalLM.from_pretrained( "Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype=torch.bfloat16, device_map="auto" ) model = PeftModel.from_pretrained(base, "argho229/satquery-qwen-lora-adapter") model = model.merge_and_unload() # optional: merge for faster inference `
| Parameter | Value |
|---|---|
| PEFT type | LoRA |
| Rank (r) | 8 |
| Alpha | 16 |
| Dropout | 0.05 |
| Target modules | q_proj, v_proj |
| Training steps | 1,186 |
| Base model | Qwen2.5-VL-3B-Instruct |
Apache 2.0
Developed for Smart India Hackathon 2026 — Problem Statement #167.