Instructions to use ssale2/dummy_output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssale2/dummy_output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ssale2/dummy_output") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ssale2/dummy_output") model = AutoModelForImageClassification.from_pretrained("ssale2/dummy_output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ssale2/dummy_output: direct link, hf CLI and curl.
- Browser
- Download file 1 kB
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https://huggingface.co/ssale2/dummy_output/resolve/main/README.md
- Command line
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hf download hf://ssale2/dummy_output/README.md
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curl -L -o README.md https://huggingface.co/ssale2/dummy_output/resolve/main/README.md
1 kB
metadata
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: dummy_output
results: []
dummy_output
This model was trained from scratch on an unknown dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu124
- Tokenizers 0.21.0