Instructions to use nhantran214/damage-vlm-qwen3vl-8b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use nhantran214/damage-vlm-qwen3vl-8b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-8B-Instruct") model = PeftModel.from_pretrained(base_model, "nhantran214/damage-vlm-qwen3vl-8b-lora") - Notebooks
- Google Colab
- Kaggle
damage-vlm-qwen3vl-8b-lora
LoRA adapter for Qwen/Qwen3-VL-8B-Instruct fine-tuned for structural damage diagnosis
(image_id, damage_categories, description JSON).
- Source dir:
outputs/runs/full_winner_v2 - Package kind:
lora_adapter - Exported (UTC): 2026-08-06 01:10:28Z
Load (Python)
import torch
from peft import PeftModel
from transformers import AutoModelForImageTextToText, AutoProcessor
base = "Qwen/Qwen3-VL-8B-Instruct"
adapter = "nhantran214/damage-vlm-qwen3vl-8b-lora"
processor = AutoProcessor.from_pretrained(base, trust_remote_code=True)
model = AutoModelForImageTextToText.from_pretrained(
base, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter)
Low-VRAM (4-bit)
pip install bitsandbytes
# In this repo:
python scripts/08_infer_full_winner.py --load-in-4bit \
--adapter nhantran214/damage-vlm-qwen3vl-8b-lora --image path/to.jpg
Project
Offline competition pipeline — see the training repository README for data tiers, holdout bake-off, and full-winner retrain steps.
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Model tree for nhantran214/damage-vlm-qwen3vl-8b-lora
Base model
Qwen/Qwen3-VL-8B-Instruct