# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("underactuated/opt-350m")
model = AutoModel.from_pretrained("underactuated/opt-350m")Quick Links
opt-350m
This model is a fine-tuned version of facebook/opt-350m 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.25.1
- Pytorch 1.13.0+cu116
- Tokenizers 0.13.2
- Downloads last month
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="underactuated/opt-350m")