Instructions to use HHazard/gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use HHazard/gpt2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2-medium") model = PeftModel.from_pretrained(base_model, "HHazard/gpt2") - Notebooks
- Google Colab
- Kaggle
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Download README.md from HHazard/gpt2: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
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https://huggingface.co/HHazard/gpt2/resolve/main/README.md
- Command line
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hf download hf://HHazard/gpt2/README.md
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curl -L -o README.md https://huggingface.co/HHazard/gpt2/resolve/main/README.md
1.23 kB
metadata
library_name: peft
license: mit
base_model: openai-community/gpt2-medium
tags:
- generated_from_trainer
datasets:
- m2d2
model-index:
- name: gpt2
results: []
gpt2
This model is a fine-tuned version of openai-community/gpt2-medium on the m2d2 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 10
Training results
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1