| --- |
| library_name: transformers |
| license: mit |
| base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: MyModel |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # MyModel |
|
|
| This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.2093 |
|
|
| ## 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: 5e-05 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - 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: linear |
| - num_epochs: 3 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 0.9491 | 0.2693 | 500 | 0.6303 | |
| | 0.6241 | 0.5385 | 1000 | 0.5958 | |
| | 0.5923 | 0.8078 | 1500 | 0.5590 | |
| | 0.5584 | 1.0770 | 2000 | 0.5180 | |
| | 0.5264 | 1.3463 | 2500 | 0.4764 | |
| | 0.5164 | 1.6155 | 3000 | 0.4459 | |
| | 0.5046 | 1.8848 | 3500 | 0.4069 | |
| | 0.3944 | 2.1540 | 4000 | 0.3134 | |
| | 0.3362 | 2.4233 | 4500 | 0.2675 | |
| | 0.32 | 2.6925 | 5000 | 0.2293 | |
| | 0.3115 | 2.9618 | 5500 | 0.2093 | |
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|
| ### Framework versions |
|
|
| - Transformers 4.48.2 |
| - Pytorch 2.5.1+cu124 |
| - Datasets 3.2.0 |
| - Tokenizers 0.21.0 |
|
|
