|
--- |
|
license: mit |
|
library_name: peft |
|
tags: |
|
- generated_from_trainer |
|
base_model: microsoft/phi-2 |
|
model-index: |
|
- name: fine-tuning-Phi2-with-webglm-qa-with-lora_7 |
|
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. --> |
|
|
|
# fine-tuning-Phi2-with-webglm-qa-with-lora_7 |
|
|
|
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.0950 |
|
|
|
## 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: 2 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 5 |
|
- total_train_batch_size: 10 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- lr_scheduler_warmup_steps: 60 |
|
- training_steps: 1000 |
|
- mixed_precision_training: Native AMP |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 7.3505 | 0.31 | 20 | 6.2863 | |
|
| 4.0914 | 0.63 | 40 | 0.9255 | |
|
| 0.6517 | 0.94 | 60 | 0.5762 | |
|
| 0.4621 | 1.26 | 80 | 0.4062 | |
|
| 0.3128 | 1.57 | 100 | 0.3056 | |
|
| 0.2536 | 1.89 | 120 | 0.2604 | |
|
| 0.2227 | 2.2 | 140 | 0.2247 | |
|
| 0.1901 | 2.52 | 160 | 0.2041 | |
|
| 0.176 | 2.83 | 180 | 0.1812 | |
|
| 0.1453 | 3.14 | 200 | 0.1683 | |
|
| 0.1557 | 3.46 | 220 | 0.1592 | |
|
| 0.1441 | 3.77 | 240 | 0.1488 | |
|
| 0.1282 | 4.09 | 260 | 0.1430 | |
|
| 0.1215 | 4.4 | 280 | 0.1348 | |
|
| 0.1217 | 4.72 | 300 | 0.1323 | |
|
| 0.117 | 5.03 | 320 | 0.1271 | |
|
| 0.109 | 5.35 | 340 | 0.1255 | |
|
| 0.1094 | 5.66 | 360 | 0.1210 | |
|
| 0.1057 | 5.97 | 380 | 0.1175 | |
|
| 0.0937 | 6.29 | 400 | 0.1158 | |
|
| 0.0942 | 6.6 | 420 | 0.1159 | |
|
| 0.1007 | 6.92 | 440 | 0.1125 | |
|
| 0.0876 | 7.23 | 460 | 0.1119 | |
|
| 0.0894 | 7.55 | 480 | 0.1099 | |
|
| 0.0827 | 7.86 | 500 | 0.1072 | |
|
| 0.0894 | 8.18 | 520 | 0.1069 | |
|
| 0.0805 | 8.49 | 540 | 0.1075 | |
|
| 0.0782 | 8.81 | 560 | 0.1043 | |
|
| 0.0881 | 9.12 | 580 | 0.1034 | |
|
| 0.0839 | 9.43 | 600 | 0.1015 | |
|
| 0.0694 | 9.75 | 620 | 0.1000 | |
|
| 0.068 | 10.06 | 640 | 0.1007 | |
|
| 0.072 | 10.38 | 660 | 0.0994 | |
|
| 0.0709 | 10.69 | 680 | 0.0985 | |
|
| 0.0712 | 11.01 | 700 | 0.0986 | |
|
| 0.0673 | 11.32 | 720 | 0.0999 | |
|
| 0.0669 | 11.64 | 740 | 0.0974 | |
|
| 0.0706 | 11.95 | 760 | 0.0981 | |
|
| 0.0641 | 12.26 | 780 | 0.0969 | |
|
| 0.0652 | 12.58 | 800 | 0.0964 | |
|
| 0.0668 | 12.89 | 820 | 0.0962 | |
|
| 0.0617 | 13.21 | 840 | 0.0972 | |
|
| 0.0628 | 13.52 | 860 | 0.0960 | |
|
| 0.0637 | 13.84 | 880 | 0.0949 | |
|
| 0.0633 | 14.15 | 900 | 0.0951 | |
|
| 0.0577 | 14.47 | 920 | 0.0953 | |
|
| 0.0646 | 14.78 | 940 | 0.0947 | |
|
| 0.06 | 15.09 | 960 | 0.0946 | |
|
| 0.0584 | 15.41 | 980 | 0.0949 | |
|
| 0.0638 | 15.72 | 1000 | 0.0950 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.7.1 |
|
- Transformers 4.36.2 |
|
- Pytorch 2.0.0 |
|
- Datasets 2.15.0 |
|
- Tokenizers 0.15.0 |
