L8-finetune
This model is a fine-tuned version of meta-llama/Llama-3.2-1B on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.0448
- F1: 0.8379
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: 1.79e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.6205 | 1.0 | 3032 | 0.4559 | 0.8252 |
| 0.1557 | 2.0 | 6064 | 0.5944 | 0.8469 |
| 0.6488 | 3.0 | 9096 | 0.8875 | 0.8291 |
| 0.3076 | 4.0 | 12128 | 0.9611 | 0.8366 |
| 0.0548 | 5.0 | 15160 | 1.0448 | 0.8379 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
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Base model
meta-llama/Llama-3.2-1B