gpt-neo

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 4.5624

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: 0.0005
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• num_epochs: 2
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.7567	0.9972	269	4.8276
4.4098	1.9944	538	4.5624

Framework versions

• Transformers 4.42.3
• Pytorch 2.1.2
• Datasets 2.20.0
• Tokenizers 0.19.1

To run:-

import torch
from transformers import pipeline, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("t5-base")
device = 'cuda'

device = 0 if torch.cuda.is_available() else -1  # Use GPU if available, otherwise CPU

# Create the pipeline
text_generator = pipeline("text-generation", model="Ketansomewhere/gpt-neo", tokenizer=tokenizer, device=device)