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License: apache-2.0Model description
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Intended uses & limitations
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Training and evaluation data
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Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2573 | 0.2623 | 200 | 0.2500 |
| 0.2427 | 0.5246 | 400 | 0.2366 |
| 0.2298 | 0.7869 | 600 | 0.2261 |
| 0.1805 | 1.0492 | 800 | 0.2246 |
| 0.1834 | 1.3115 | 1000 | 0.2222 |
| 0.162 | 1.5738 | 1200 | 0.2188 |
| 0.1604 | 1.8361 | 1400 | 0.2148 |
| 0.1187 | 2.0984 | 1600 | 0.2219 |
| 0.1236 | 2.3607 | 1800 | 0.2252 |
| 0.1156 | 2.6230 | 2000 | 0.2258 |
| 0.1127 | 2.8852 | 2200 | 0.2253 |
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.10.0+cu128
- Datasets 3.1.0
- Tokenizers 0.20.3
Model provider
marcuschill1823
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Qwen/Qwen2.5-1.5B-Instruct
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