|
|
--- |
|
|
license: bigscience-bloom-rail-1.0 |
|
|
library_name: peft |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
base_model: vlsp-2023-vllm/hoa-1b4 |
|
|
model-index: |
|
|
- name: hoa-1b4_model_kaggle_test |
|
|
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. --> |
|
|
|
|
|
# hoa-1b4_model_kaggle_test |
|
|
|
|
|
This model is a fine-tuned version of [vlsp-2023-vllm/hoa-1b4](https://huggingface.co/vlsp-2023-vllm/hoa-1b4) on the None dataset. |
|
|
It achieves the following results on the evaluation set: |
|
|
- Loss: 0.0088 |
|
|
|
|
|
## 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: 4e-05 |
|
|
- train_batch_size: 1 |
|
|
- eval_batch_size: 1 |
|
|
- seed: 42 |
|
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
|
- lr_scheduler_type: linear |
|
|
- num_epochs: 100 |
|
|
|
|
|
### Training results |
|
|
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|
|:-------------:|:-----:|:----:|:---------------:| |
|
|
| No log | 1.0 | 7 | 3.2766 | |
|
|
| No log | 2.0 | 14 | 2.8527 | |
|
|
| No log | 3.0 | 21 | 2.6111 | |
|
|
| No log | 4.0 | 28 | 2.3834 | |
|
|
| No log | 5.0 | 35 | 2.1930 | |
|
|
| No log | 6.0 | 42 | 2.0336 | |
|
|
| No log | 7.0 | 49 | 1.8676 | |
|
|
| No log | 8.0 | 56 | 1.7066 | |
|
|
| No log | 9.0 | 63 | 1.5550 | |
|
|
| No log | 10.0 | 70 | 1.4148 | |
|
|
| No log | 11.0 | 77 | 1.2909 | |
|
|
| No log | 12.0 | 84 | 1.1698 | |
|
|
| No log | 13.0 | 91 | 1.0494 | |
|
|
| No log | 14.0 | 98 | 0.9417 | |
|
|
| No log | 15.0 | 105 | 0.8308 | |
|
|
| No log | 16.0 | 112 | 0.7236 | |
|
|
| No log | 17.0 | 119 | 0.6239 | |
|
|
| No log | 18.0 | 126 | 0.5360 | |
|
|
| No log | 19.0 | 133 | 0.4547 | |
|
|
| No log | 20.0 | 140 | 0.3911 | |
|
|
| No log | 21.0 | 147 | 0.3288 | |
|
|
| No log | 22.0 | 154 | 0.2807 | |
|
|
| No log | 23.0 | 161 | 0.2396 | |
|
|
| No log | 24.0 | 168 | 0.2013 | |
|
|
| No log | 25.0 | 175 | 0.1764 | |
|
|
| No log | 26.0 | 182 | 0.1483 | |
|
|
| No log | 27.0 | 189 | 0.1240 | |
|
|
| No log | 28.0 | 196 | 0.1128 | |
|
|
| No log | 29.0 | 203 | 0.0983 | |
|
|
| No log | 30.0 | 210 | 0.0868 | |
|
|
| No log | 31.0 | 217 | 0.0775 | |
|
|
| No log | 32.0 | 224 | 0.0722 | |
|
|
| No log | 33.0 | 231 | 0.0613 | |
|
|
| No log | 34.0 | 238 | 0.0570 | |
|
|
| No log | 35.0 | 245 | 0.0495 | |
|
|
| No log | 36.0 | 252 | 0.0441 | |
|
|
| No log | 37.0 | 259 | 0.0409 | |
|
|
| No log | 38.0 | 266 | 0.0360 | |
|
|
| No log | 39.0 | 273 | 0.0314 | |
|
|
| No log | 40.0 | 280 | 0.0296 | |
|
|
| No log | 41.0 | 287 | 0.0250 | |
|
|
| No log | 42.0 | 294 | 0.0231 | |
|
|
| No log | 43.0 | 301 | 0.0241 | |
|
|
| No log | 44.0 | 308 | 0.0196 | |
|
|
| No log | 45.0 | 315 | 0.0183 | |
|
|
| No log | 46.0 | 322 | 0.0176 | |
|
|
| No log | 47.0 | 329 | 0.0173 | |
|
|
| No log | 48.0 | 336 | 0.0143 | |
|
|
| No log | 49.0 | 343 | 0.0145 | |
|
|
| No log | 50.0 | 350 | 0.0138 | |
|
|
| No log | 51.0 | 357 | 0.0131 | |
|
|
| No log | 52.0 | 364 | 0.0138 | |
|
|
| No log | 53.0 | 371 | 0.0142 | |
|
|
| No log | 54.0 | 378 | 0.0137 | |
|
|
| No log | 55.0 | 385 | 0.0119 | |
|
|
| No log | 56.0 | 392 | 0.0123 | |
|
|
| No log | 57.0 | 399 | 0.0122 | |
|
|
| No log | 58.0 | 406 | 0.0112 | |
|
|
| No log | 59.0 | 413 | 0.0114 | |
|
|
| No log | 60.0 | 420 | 0.0112 | |
|
|
| No log | 61.0 | 427 | 0.0108 | |
|
|
| No log | 62.0 | 434 | 0.0105 | |
|
|
| No log | 63.0 | 441 | 0.0120 | |
|
|
| No log | 64.0 | 448 | 0.0110 | |
|
|
| No log | 65.0 | 455 | 0.0115 | |
|
|
| No log | 66.0 | 462 | 0.0102 | |
|
|
| No log | 67.0 | 469 | 0.0104 | |
|
|
| No log | 68.0 | 476 | 0.0113 | |
|
|
| No log | 69.0 | 483 | 0.0098 | |
|
|
| No log | 70.0 | 490 | 0.0101 | |
|
|
| No log | 71.0 | 497 | 0.0101 | |
|
|
| 0.4941 | 72.0 | 504 | 0.0094 | |
|
|
| 0.4941 | 73.0 | 511 | 0.0097 | |
|
|
| 0.4941 | 74.0 | 518 | 0.0095 | |
|
|
| 0.4941 | 75.0 | 525 | 0.0101 | |
|
|
| 0.4941 | 76.0 | 532 | 0.0095 | |
|
|
| 0.4941 | 77.0 | 539 | 0.0100 | |
|
|
| 0.4941 | 78.0 | 546 | 0.0092 | |
|
|
| 0.4941 | 79.0 | 553 | 0.0094 | |
|
|
| 0.4941 | 80.0 | 560 | 0.0095 | |
|
|
| 0.4941 | 81.0 | 567 | 0.0094 | |
|
|
| 0.4941 | 82.0 | 574 | 0.0093 | |
|
|
| 0.4941 | 83.0 | 581 | 0.0093 | |
|
|
| 0.4941 | 84.0 | 588 | 0.0094 | |
|
|
| 0.4941 | 85.0 | 595 | 0.0092 | |
|
|
| 0.4941 | 86.0 | 602 | 0.0089 | |
|
|
| 0.4941 | 87.0 | 609 | 0.0090 | |
|
|
| 0.4941 | 88.0 | 616 | 0.0090 | |
|
|
| 0.4941 | 89.0 | 623 | 0.0089 | |
|
|
| 0.4941 | 90.0 | 630 | 0.0088 | |
|
|
| 0.4941 | 91.0 | 637 | 0.0089 | |
|
|
| 0.4941 | 92.0 | 644 | 0.0088 | |
|
|
| 0.4941 | 93.0 | 651 | 0.0088 | |
|
|
| 0.4941 | 94.0 | 658 | 0.0088 | |
|
|
| 0.4941 | 95.0 | 665 | 0.0088 | |
|
|
| 0.4941 | 96.0 | 672 | 0.0088 | |
|
|
| 0.4941 | 97.0 | 679 | 0.0088 | |
|
|
| 0.4941 | 98.0 | 686 | 0.0088 | |
|
|
| 0.4941 | 99.0 | 693 | 0.0088 | |
|
|
| 0.4941 | 100.0 | 700 | 0.0088 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- PEFT 0.9.0 |
|
|
- Transformers 4.38.1 |
|
|
- Pytorch 2.1.2 |
|
|
- Datasets 2.1.0 |
|
|
- Tokenizers 0.15.2 |
