# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("exontidev/results")
model = AutoModelForCausalLM.from_pretrained("exontidev/results")Quick Links
results
This model is a fine-tuned version of ai-forever/rugpt3large_based_on_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: 0.001
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5e-05
Training results
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
- Downloads last month
- 4
Model tree for exontidev/results
Base model
ai-forever/rugpt3large_based_on_gpt2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="exontidev/results")