Video-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
qwen2.5-vl
video-question-answering
resadapt
text-generation-inference
Instructions to use Xnhyacinth/ResAdapt-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xnhyacinth/ResAdapt-Checkpoints with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Xnhyacinth/ResAdapt-Checkpoints") model = AutoModelForMultimodalLM.from_pretrained("Xnhyacinth/ResAdapt-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Xnhyacinth/ResAdapt-Checkpoints: direct link, hf CLI and curl.
- Browser
- Download file 818 Bytes
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https://huggingface.co/Xnhyacinth/ResAdapt-Checkpoints/resolve/main/README.md
- Command line
-
hf download hf://Xnhyacinth/ResAdapt-Checkpoints/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Xnhyacinth/ResAdapt-Checkpoints/resolve/main/README.md
818 Bytes
metadata
license: apache-2.0
library_name: transformers
pipeline_tag: video-text-to-text
tags:
- qwen2.5-vl
- video-question-answering
- resadapt
ResAdapt research checkpoint
This repository preserves the Resadapt checkpoint and its loading files.
- Repository root: Qwen2.5-VL checkpoint.
pred/: trained SmolVLM predictor checkpoint.predictor_smolv3_init/: initialization weights for the predictor.
Six machine-specific paths in the predictor example code use portable placeholders. Replace those placeholders with your own model and media paths. Weight values and scientific identifiers are unchanged; this publication adds no inference or benchmark result.
ARTIFACTS.json records the source revision, per-file hashes, and the limited edits. Existing source terms remain applicable.