reward-model
This model is a fine-tuned version of Qwen/Qwen2-0.5B on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5217
- Accuracy: 0.727
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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.636 | 0.0516 | 50 | 0.6010 | 0.688 |
| 0.5793 | 0.1032 | 100 | 0.5676 | 0.703 |
| 0.5807 | 0.1548 | 150 | 0.5732 | 0.705 |
| 0.5572 | 0.2064 | 200 | 0.5513 | 0.706 |
| 0.5695 | 0.2580 | 250 | 0.5472 | 0.718 |
| 0.5596 | 0.3096 | 300 | 0.5283 | 0.723 |
| 0.54 | 0.3612 | 350 | 0.5445 | 0.715 |
| 0.5291 | 0.4128 | 400 | 0.5387 | 0.722 |
| 0.539 | 0.4644 | 450 | 0.5461 | 0.726 |
| 0.5248 | 0.5160 | 500 | 0.5402 | 0.724 |
| 0.5263 | 0.5676 | 550 | 0.5271 | 0.726 |
| 0.5222 | 0.6192 | 600 | 0.5238 | 0.724 |
| 0.5259 | 0.6708 | 650 | 0.5200 | 0.728 |
| 0.5118 | 0.7224 | 700 | 0.5190 | 0.728 |
| 0.513 | 0.7740 | 750 | 0.5213 | 0.731 |
| 0.5141 | 0.8256 | 800 | 0.5253 | 0.729 |
| 0.5197 | 0.8772 | 850 | 0.5256 | 0.724 |
| 0.4968 | 0.9288 | 900 | 0.5231 | 0.726 |
| 0.4983 | 0.9804 | 950 | 0.5217 | 0.727 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1