Text Generation
Transformers
PyTorch
TensorBoard
gpt2
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("skrishna/gpt-test")
model = AutoModelForCausalLM.from_pretrained("skrishna/gpt-test")Quick Links
gpt-test
This model is a fine-tuned version of gpt2 on the None 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: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
Training results
Framework versions
- Transformers 4.28.0.dev0
- Pytorch 1.13.0
- Datasets 2.9.0
- Tokenizers 0.13.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="skrishna/gpt-test")