Instructions to use leofn3/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use leofn3/model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="leofn3/model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("leofn3/model") model = AutoModelForMaskedLM.from_pretrained("leofn3/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from leofn3/model: direct link, hf CLI and curl.
- Browser
- Download file 991 Bytes
-
https://huggingface.co/leofn3/model/resolve/main/README.md
- Command line
-
hf download hf://leofn3/model/README.md
-
curl -L -o README.md https://huggingface.co/leofn3/model/resolve/main/README.md
991 Bytes
metadata
tags:
- generated_from_trainer
model-index:
- name: model
results: []
model
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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2