Video-Text-to-Text
Transformers
English
video
video-question-answering
multimodal
vision-language
qwen3-vl
inference-time
frame-selection
clip
Instructions to use commandeaw/DW-KhotTaeVL-2B-QueryFrames with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use commandeaw/DW-KhotTaeVL-2B-QueryFrames with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("commandeaw/DW-KhotTaeVL-2B-QueryFrames", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Add --start-idx for chunked evaluation (MPS resilience)
Verified signatureApple MPS can corrupt accelerator state on long Qwen3-VL inference runs (~800+ questions), producing AcceleratorError. Add --start-idx K so callers can skip the first K filtered questions, enabling a chunked-runner pattern: each chunk is a fresh Python process with clean MPS state. Also wrap the per-question inference in try/except: on AcceleratorError or any other inference failure, save partial results and exit gracefully so an outer chunked-runner can resume from start-idx + completed_count.50d1d87 -
eval_videomme: rename --mode wild → mcq (wild kept as deprecated alias); update docstrings to use MCQ-mode terminology
Verified signature3081b91 -
Initial release: DW-KhotTaeVL-2B-QueryFrames v1.0
Verified signatureQuery-aware frame selection wrapper for Qwen3-VL-2B-Instruct. Wild mode: 64.3% on Video-MME mini 300Q (+7.3pp vs stock 57.0%). Benchmark mode: 66.3% (+9.3pp), 12/12 task buckets non-negative. Zero trainable parameters, no model weights modified. Built on Qwen/Qwen3-VL-2B-Instruct (Apache 2.0). Frame scorer: openai/clip-vit-large-patch14 (MIT). Author: Deaw (HF: @commandeaw).84c8a9d