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
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.
Consolidated legacy repository
Xnhyacinth/Resadapt is a navigation-only legacy entry. The model, predictor and loading files are maintained here. A migration receipt records the original paths and matching hashes. Historical downloads from the legacy repository no longer contain the model binaries.
The historical data.pt was a data-loader resume snapshot and was removed; it is not needed for model inference.
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