Instructions to use Wonder-Griffin/The_Judge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Wonder-Griffin/The_Judge with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Wonder-Griffin/The_Judge")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Wonder-Griffin/The_Judge") model = AutoModel.from_pretrained("Wonder-Griffin/The_Judge") - Notebooks
- Google Colab
- Kaggle
The_Judge
This model is a fine-tuned version of Wonder-Griffin/JudgeLLM2 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Framework versions
- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for Wonder-Griffin/The_Judge
Base model
Wonder-Griffin/JudgeLLM2 Finetuned
Wonder-Griffin/Judge-GPT2 Finetuned
Wonder-Griffin/JudgeLLM Finetuned
Wonder-Griffin/JudgeLLM2