| | --- |
| | library_name: transformers |
| | license: llama3.2 |
| | base_model: meta-llama/Llama-3.2-1B |
| | tags: |
| | - trl |
| | - sft |
| | - generated_from_trainer |
| | model-index: |
| | - name: rationale_model_e10_save5000 |
| | 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. --> |
| |
|
| | # rationale_model_e10_save5000 |
| | |
| | This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 2.6975 |
| | |
| | ## 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 |
| | - optimizer: Use 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: 10 |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:------:|:-----:|:---------------:| |
| | | 0.3986 | 0.9538 | 5000 | 2.6975 | |
| | | 0.1468 | 1.9077 | 10000 | 3.2221 | |
| | | 0.1156 | 2.8615 | 15000 | 3.4922 | |
| | | 0.0981 | 3.8153 | 20000 | 3.6490 | |
| | | 0.0847 | 4.7692 | 25000 | 3.8345 | |
| | | 0.0704 | 5.7230 | 30000 | 3.9968 | |
| | | 0.0551 | 6.6768 | 35000 | 4.2504 | |
| | | 0.0433 | 7.6307 | 40000 | 4.5271 | |
| | | 0.0354 | 8.5845 | 45000 | 4.7534 | |
| | | 0.0317 | 9.5383 | 50000 | 4.9696 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.46.3 |
| | - Pytorch 2.3.0 |
| | - Datasets 2.14.4 |
| | - Tokenizers 0.20.3 |
| | |
