Instructions to use Shunchang/SmolVLM-Instruct-condition with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shunchang/SmolVLM-Instruct-condition with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
End of training
Browse files
README.md
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size:
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size:
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 10
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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| 42 |
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 10
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adapter_model.safetensors
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size 42220792
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runs/May26_03-50-47_e521ce3f4686/events.out.tfevents.1748231464.e521ce3f4686.1375.2
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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