dax_query_llama
This model is a fine-tuned version of meta-llama/Llama-3.1-8B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 9.5530
- Rouge1: 0.3920
- Rouge2: 0.0986
- Rougel: 0.3668
- Rougelsum: 0.3670
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: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|---|---|---|---|---|---|---|---|
| No log | 1.0 | 1 | 9.5530 | 0.3920 | 0.0986 | 0.3668 | 0.3670 |
| No log | 2.0 | 2 | 9.5530 | 0.3920 | 0.0986 | 0.3668 | 0.3670 |
| No log | 3.0 | 3 | 9.5530 | 0.3920 | 0.0986 | 0.3668 | 0.3670 |
Framework versions
- PEFT 0.14.0
- Transformers 4.47.1
- Pytorch 2.5.1+cu118
- Datasets 3.2.0
- Tokenizers 0.21.0
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meta-llama/Llama-3.1-8B