Llama-360M
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 3.8245
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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.6417 | 1.0 | 3 | 8.5751 |
| 8.3908 | 2.0 | 6 | 8.3473 |
| 7.9583 | 3.0 | 9 | 7.9814 |
| 7.3598 | 4.0 | 12 | 7.5011 |
| 6.7468 | 5.0 | 15 | 6.9942 |
| 6.3345 | 6.0 | 18 | 6.6309 |
| 6.0489 | 7.0 | 21 | 6.3987 |
| 5.9651 | 8.0 | 24 | 6.2101 |
| 5.7683 | 9.0 | 27 | 5.9691 |
| 5.3051 | 10.0 | 30 | 5.5791 |
| 4.6791 | 11.0 | 33 | 5.1445 |
| 4.3962 | 12.0 | 36 | 4.8859 |
| 4.0007 | 13.0 | 39 | 4.7013 |
| 3.9473 | 14.0 | 42 | 4.4994 |
| 3.5486 | 15.0 | 45 | 4.3178 |
| 3.3243 | 16.0 | 48 | 4.1587 |
| 3.1305 | 17.0 | 51 | 4.0505 |
| 2.8703 | 18.0 | 54 | 3.9467 |
| 2.7661 | 19.0 | 57 | 3.8780 |
| 2.7976 | 20.0 | 60 | 3.8245 |
Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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