Instructions to use Ryuzaki1618/final_run_gpt2-dpo-prefix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ryuzaki1618/final_run_gpt2-dpo-prefix with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2-medium") model = PeftModel.from_pretrained(base_model, "Ryuzaki1618/final_run_gpt2-dpo-prefix") - Notebooks
- Google Colab
- Kaggle
final_run_gpt2-dpo-prefix
This model is a fine-tuned version of gpt2-medium 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.18.1
- Transformers 5.2.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Ryuzaki1618/final_run_gpt2-dpo-prefix
Base model
openai-community/gpt2-medium