Video-Text-to-Text
Transformers
Safetensors
qwen3-vl
vision-language-model
video
traffic-accident
accisage
lora
ms-swift
Instructions to use yzk9/AcciSAGE-S8-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yzk9/AcciSAGE-S8-SFT with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("yzk9/AcciSAGE-S8-SFT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
AcciSAGE-S8-SFT
LoRA adapter for AcciSAGE: Error-aware VLM Tuning for Spatio-temporal Grounding of Traffic Accident Videos.
- Task branch: S8 โ Spatial accident localization
- Training: SFT
- Base model:
Qwen/Qwen3-VL-8B-Instruct - Expected output: Accident center in normalized 0โ1000 coordinates
This repository contains an adapter and requires Qwen/Qwen3-VL-8B-Instruct as the base model.
Usage
swift infer \
--model Qwen/Qwen3-VL-8B-Instruct \
--adapters yzk9/AcciSAGE-S8-SFT
Task-specific prompts and video preprocessing settings are required. This model was developed for research on traffic-accident video understanding.
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support
Model tree for yzk9/AcciSAGE-S8-SFT
Base model
Qwen/Qwen3-VL-8B-Instruct