| | --- |
| | tags: |
| | - generated_from_trainer |
| | model-index: |
| | - name: src_prober_codellama-7b-last1unfreeze |
| | 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. --> |
| |
|
| | # src_prober_codellama-7b-last1unfreeze |
| |
|
| | This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.6729 |
| |
|
| | ## 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: 32 |
| | - seed: 42 |
| | - distributed_type: multi-GPU |
| | - num_devices: 4 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 128 |
| | - total_eval_batch_size: 128 |
| | - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| | - lr_scheduler_type: cosine |
| | - lr_scheduler_warmup_steps: 1000 |
| | - num_epochs: 5 |
| | - mixed_precision_training: Native AMP |
| | |
| | ### Training results |
| | |
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:-----:|:---------------:| |
| | | 0.7634 | 0.24 | 500 | 0.7745 | |
| | | 0.7376 | 0.48 | 1000 | 0.7399 | |
| | | 0.7006 | 0.72 | 1500 | 0.7138 | |
| | | 0.6721 | 0.97 | 2000 | 0.7015 | |
| | | 0.6753 | 1.21 | 2500 | 0.6941 | |
| | | 0.6716 | 1.45 | 3000 | 0.6894 | |
| | | 0.6595 | 1.69 | 3500 | 0.6865 | |
| | | 0.6743 | 1.93 | 4000 | 0.6848 | |
| | | 0.6647 | 2.17 | 4500 | 0.6819 | |
| | | 0.6721 | 2.42 | 5000 | 0.6797 | |
| | | 0.6642 | 2.66 | 5500 | 0.6780 | |
| | | 0.6653 | 2.9 | 6000 | 0.6764 | |
| | | 0.643 | 3.14 | 6500 | 0.6756 | |
| | | 0.6532 | 3.38 | 7000 | 0.6749 | |
| | | 0.6299 | 3.62 | 7500 | 0.6745 | |
| | | 0.6442 | 3.87 | 8000 | 0.6737 | |
| | | 0.6347 | 4.11 | 8500 | 0.6733 | |
| | | 0.6364 | 4.35 | 9000 | 0.6730 | |
| | | 0.6456 | 4.59 | 9500 | 0.6728 | |
| | | 0.6338 | 4.83 | 10000 | 0.6729 | |
| | |
| | |
| | ### Framework versions |
| | |
| | - Transformers 4.39.3 |
| | - Pytorch 2.2.0 |
| | - Datasets 2.17.0 |
| | - Tokenizers 0.15.2 |
| | |
