|
---
|
|
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 |
