Instructions to use Archit001a/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Archit001a/model with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForNextSentencePrediction tokenizer = AutoTokenizer.from_pretrained("Archit001a/model") model = AutoModelForNextSentencePrediction.from_pretrained("Archit001a/model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Archit001a/model: direct link, hf CLI and curl.
- Browser
- Download file 971 Bytes
-
https://huggingface.co/Archit001a/model/resolve/main/README.md
- Command line
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hf download hf://Archit001a/model/README.md
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curl -L -o README.md https://huggingface.co/Archit001a/model/resolve/main/README.md
971 Bytes
metadata
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: Model
results: []
Model
This model is a fine-tuned version of gpt2 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: 3
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.1