Image Feature Extraction
Transformers
Safetensors
PyTorch
distilled_student
knowledge-distillation
custom_code
Instructions to use fokan/train-modle2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fokan/train-modle2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="fokan/train-modle2", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fokan/train-modle2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
fokan/train-modle2
This model was created using knowledge distillation from the following teacher model(s):
Model Description
A distilled model created using multi-modal knowledge distillation.
Training Details
- Teacher Models:
- Distillation Strategy: weighted
- Training Steps: 5000
- Learning Rate: 0.001
Usage
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("fokan/train-modle2")
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
Created with
This model was created using the Multi-Modal Knowledge Distillation platform.
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