Instructions to use skumar1998/result with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skumar1998/result with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("skumar1998/result", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Model save
Browse files
file.log
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@@ -41,3 +41,4 @@ Dataset Split Statistics:
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2026-07-24 13:13:46.381 | INFO | __main__:create_trainer:306 - Materialized datasets: 83 train, 13 eval samples
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2026-07-24 13:13:47.009 | INFO | __main__:standard_training_loop:360 - Starting training
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2026-07-24 13:13:47.201 | INFO | app.tasks.entity.vlm.model:train_model:585 - everything is set up, launching training
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2026-07-24 13:13:46.381 | INFO | __main__:create_trainer:306 - Materialized datasets: 83 train, 13 eval samples
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| 42 |
2026-07-24 13:13:47.009 | INFO | __main__:standard_training_loop:360 - Starting training
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2026-07-24 13:13:47.201 | INFO | app.tasks.entity.vlm.model:train_model:585 - everything is set up, launching training
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2026-07-24 13:26:30.710 | INFO | app.tasks.entity.vlm.model:train_model:591 - training complete: TrainOutput(global_step=20, training_loss=0.4323345601558685, metrics={'train_runtime': 745.906, 'train_samples_per_second': 0.215, 'train_steps_per_second': 0.027, 'total_flos': 4.827960499381027e+16, 'train_loss': 0.4323345601558685, 'epoch': 1.8571428571428572})
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