Mistral-7B-v0.1-QLoRA-MathReasoning2
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3043
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: 0.0002
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3072 | 0.2667 | 100 | 0.3165 |
| 0.3151 | 0.5333 | 200 | 0.3099 |
| 0.3127 | 0.8 | 300 | 0.3046 |
| 0.2744 | 1.0667 | 400 | 0.3043 |
| 0.2518 | 1.3333 | 500 | 0.3068 |
| 0.2517 | 1.6 | 600 | 0.3058 |
| 0.2427 | 1.8667 | 700 | 0.3059 |
| 0.1853 | 2.1333 | 800 | 0.3269 |
| 0.1754 | 2.4 | 900 | 0.3293 |
| 0.1767 | 2.6667 | 1000 | 0.3328 |
| 0.1773 | 2.9333 | 1100 | 0.3314 |
Framework versions
- PEFT 0.17.1
- Transformers 4.57.0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1