Instructions to use pszmk/mnist-vae-latent2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszmk/mnist-vae-latent2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="pszmk/mnist-vae-latent2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("pszmk/mnist-vae-latent2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
MNIST VAE latent2
Overview
This model was exported from the Lightning checkpoint epoch=31-step=15008-val_loss=0.2625.ckpt.
Loading
from transformers import AutoModel
model = AutoModel.from_pretrained("pszmk/mnist-vae-latent2", trust_remote_code=True)
Load a specific release with a revision or tag:
from transformers import AutoModel
model = AutoModel.from_pretrained(
"pszmk/mnist-vae-latent2",
revision="main",
trust_remote_code=True,
)
Provenance
- Source checkpoint:
/home/pszmk/Latent-Anti-Microbial-Peptides-LAMP/checkpoints/6b865455ff1f4cb7818d40a26f0eeeed/epoch=31-step=15008-val_loss=0.2625.ckpt - Model class:
modelling.src.models.mnist_vae.MNISTVAE - Config class:
modelling.src.models.mnist_vae.MNISTVAEConfig - Suggested revision:
main
Metadata
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