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vit-classification-notes

alyssathompson / vit-classification-notes

Run on huggingface.co
huggingface.co
Total runs: 14
24-hour runs: 12
7-day runs: 12
30-day runs: 12
Model's Last Updated: October 19 2026

Introduction of vit-classification-notes

Model Details of vit-classification-notes

ViT for Classification

Overview

This is an experimental ViT codebase for Classification . It keeps the tiny setup intentionally manageable so architecture changes can be inspected before a full training run.

Repository status
  • The Python file contains the model and runnable example or training entry point.
  • config.json records the generated architecture settings.
  • training_args.json records the default experiment recipe.
  • model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint.
  • No benchmark score is claimed in this repository.
Architecture
Item Value
Architecture ViT
Scale tiny
Attention multi query
Fusion bilinear
Activation relu
Normalization rmsnorm
Default experiment recipe

The included configuration uses rmsprop with a linear warmup schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.

Quick check 
python finetune.py --help

Inspect the script's __main__ block for its generated smoke-test example. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.

Evaluation guidance

A useful first evaluation would use a task-specific labeled split , report the task metric across at least three seeds, and include a matched-capacity baseline. Keep training logs and environment versions with any published result.

Limitations

The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.

Files
  • finetune.py — primary artifact
  • README.md — this documentation
  • config.json — architecture configuration
  • training_args.json — default experiment settings
  • model.safetensors — initialization checkpoint
License

Released under apache-2.0 . Review the source-data terms separately when this repository is used with external datasets.

Runs of alyssathompson vit-classification-notes on huggingface.co

14
Total runs
12
24-hour runs
12
3-day runs
12
7-day runs
12
30-day runs

More Information About vit-classification-notes huggingface.co Model

More vit-classification-notes license Visit here:

https://choosealicense.com/licenses/apache-2.0

vit-classification-notes huggingface.co

vit-classification-notes huggingface.co is an AI model on huggingface.co that provides vit-classification-notes's model effect (), which can be used instantly with this alyssathompson vit-classification-notes model. huggingface.co supports a free trial of the vit-classification-notes model, and also provides paid use of the vit-classification-notes. Support call vit-classification-notes model through api, including Node.js, Python, http.

vit-classification-notes huggingface.co Url

https://huggingface.co/alyssathompson/vit-classification-notes

alyssathompson vit-classification-notes online free

vit-classification-notes huggingface.co is an online trial and call api platform, which integrates vit-classification-notes's modeling effects, including api services, and provides a free online trial of vit-classification-notes, you can try vit-classification-notes online for free by clicking the link below.

alyssathompson vit-classification-notes online free url in huggingface.co:

https://huggingface.co/alyssathompson/vit-classification-notes

vit-classification-notes install

vit-classification-notes is an open source model from GitHub that offers a free installation service, and any user can find vit-classification-notes on GitHub to install. At the same time, huggingface.co provides the effect of vit-classification-notes install, users can directly use vit-classification-notes installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

vit-classification-notes install url in huggingface.co:

https://huggingface.co/alyssathompson/vit-classification-notes

Url of vit-classification-notes

vit-classification-notes huggingface.co Url

Provider of vit-classification-notes huggingface.co

alyssathompson
ORGANIZATIONS
huggingface.co

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