Instructions to use underactuated/opt-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use underactuated/opt-350m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="underactuated/opt-350m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("underactuated/opt-350m") model = AutoModel.from_pretrained("underactuated/opt-350m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from underactuated/opt-350m: direct link, hf CLI and curl.
- Browser
- Download file 967 Bytes
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https://huggingface.co/underactuated/opt-350m/resolve/main/README.md
- Command line
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hf download hf://underactuated/opt-350m/README.md
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curl -L -o README.md https://huggingface.co/underactuated/opt-350m/resolve/main/README.md
967 Bytes
metadata
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-350m
results: []
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