Instructions to use Mayfull/CLIP_VLTopKSAE_2_12_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mayfull/CLIP_VLTopKSAE_2_12_2 with Transformers:
# Load model directly from transformers import VLTopKSAE model = VLTopKSAE.from_pretrained("Mayfull/CLIP_VLTopKSAE_2_12_2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
CLIP_VLTopKSAE_2_12_2
This model is a fine-tuned version of 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: 0.0002
- train_batch_size: 128
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
Framework versions
- Transformers 4.57.6
- Pytorch 2.10.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.2
- Downloads last month
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