SNAC-Denoiser-LLaMA-250M-snac_v1_test_1gpu
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 7.5886
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 31
- training_steps: 1562
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 8.2753 | 0.1280 | 200 | 8.3038 |
| 7.9266 | 0.2561 | 400 | 7.9919 |
| 7.7599 | 0.3841 | 600 | 7.8794 |
| 7.6104 | 0.5121 | 800 | 7.7593 |
| 7.5253 | 0.6401 | 1000 | 7.6645 |
| 7.4632 | 0.7682 | 1200 | 7.6124 |
| 7.4432 | 0.8962 | 1400 | 7.5886 |
Framework versions
- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0
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