| ---
|
| license: other
|
| library_name: peft
|
| tags:
|
| - generated_from_trainer
|
| base_model: deepseek-ai/deepseek-coder-1.3b-base
|
| model-index:
|
| - name: peft-deepseek-code-lora
|
| 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. --> |
|
|
| # peft-deepseek-code-lora |
|
|
| This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7771 |
|
|
| ## 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: 0.0005 |
| - train_batch_size: 12 |
| - eval_batch_size: 12 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 45 |
| - training_steps: 3000 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:------:|:----:|:---------------:| |
| | 0.869 | 0.0333 | 100 | 0.8371 | |
| | 0.8608 | 0.0667 | 200 | 0.7918 | |
| | 0.7746 | 0.1 | 300 | 0.7638 | |
| | 0.7381 | 0.1333 | 400 | 0.7487 | |
| | 0.7078 | 0.1667 | 500 | 0.7371 | |
| | 0.7066 | 0.2 | 600 | 0.7261 | |
| | 0.6709 | 0.2333 | 700 | 0.7235 | |
| | 0.6487 | 0.2667 | 800 | 0.7191 | |
| | 0.6103 | 0.3 | 900 | 0.7196 | |
| | 0.6109 | 0.3333 | 1000 | 0.7197 | |
| | 0.5804 | 0.3667 | 1100 | 0.7112 | |
| | 0.563 | 0.4 | 1200 | 0.7162 | |
| | 0.5406 | 0.4333 | 1300 | 0.7157 | |
| | 0.5286 | 0.4667 | 1400 | 0.7256 | |
| | 0.4839 | 0.5 | 1500 | 0.7208 | |
| | 0.5268 | 0.5333 | 1600 | 0.7258 | |
| | 0.4565 | 0.5667 | 1700 | 0.7280 | |
| | 0.4366 | 0.6 | 1800 | 0.7298 | |
| | 0.4729 | 0.6333 | 1900 | 0.7393 | |
| | 0.4451 | 0.6667 | 2000 | 0.7463 | |
| | 0.4008 | 0.7 | 2100 | 0.7533 | |
| | 0.3915 | 0.7333 | 2200 | 0.7609 | |
| | 0.3769 | 0.7667 | 2300 | 0.7601 | |
| | 0.3776 | 0.8 | 2400 | 0.7671 | |
| | 0.3896 | 0.8333 | 2500 | 0.7694 | |
| | 0.3798 | 0.8667 | 2600 | 0.7727 | |
| | 0.3683 | 0.9 | 2700 | 0.7756 | |
| | 0.36 | 0.9333 | 2800 | 0.7774 | |
| | 0.3713 | 0.9667 | 2900 | 0.7769 | |
| | 0.352 | 1.0 | 3000 | 0.7771 | |
| |
| |
| ### Framework versions |
| |
| - PEFT 0.11.1 |
| - Transformers 4.41.2 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.14.6 |
| - Tokenizers 0.19.1 |
