Instructions to use tuhink/hacking-rewards-harmless-train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tuhink/hacking-rewards-harmless-train with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="tuhink/hacking-rewards-harmless-train")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("tuhink/hacking-rewards-harmless-train") model = AutoModelForSequenceClassification.from_pretrained("tuhink/hacking-rewards-harmless-train", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from tuhink/hacking-rewards-harmless-train: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
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https://huggingface.co/tuhink/hacking-rewards-harmless-train/resolve/main/README.md
- Command line
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hf download hf://tuhink/hacking-rewards-harmless-train/README.md
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curl -L -o README.md https://huggingface.co/tuhink/hacking-rewards-harmless-train/resolve/main/README.md
1.2 kB
metadata
library_name: transformers
license: mit
base_model: openai-community/gpt2-medium
tags:
- generated_from_trainer
model-index:
- name: hacking-rewards-harmless-train
results: []
hacking-rewards-harmless-train
This model is a fine-tuned version of openai-community/gpt2-medium 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: 1e-05
- train_batch_size: 64
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 2.18.0
- Tokenizers 0.21.0