Instructions to use GetThePancake/output_qlora_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use GetThePancake/output_qlora_4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gemma-3-27b-it") model = PeftModel.from_pretrained(base_model, "GetThePancake/output_qlora_4") - Notebooks
- Google Colab
- Kaggle
output_qlora_4
This model was trained from scratch on the pile-of-law 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: 8e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 2
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.14.0
- Transformers 4.57.1
- Pytorch 2.9.1+cu128
- Datasets 3.3.2
- Tokenizers 0.22.1
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