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---
license: mit
base_model: TheBloke/zephyr-7B-beta-GPTQ
tags:
- trl
- sft
- generated_from_trainer
metrics:
- rouge
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9415
- Rouge1: 0.7212
- Rouge2: 0.5438
- Rougel: 0.6914
- Rougelsum: 0.7070
## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|
| 2.025 | 1.11 | 10 | 1.7161 | 0.5886 | 0.2570 | 0.5121 | 0.5475 |
| 1.1915 | 2.22 | 20 | 1.2651 | 0.6729 | 0.4745 | 0.6406 | 0.6543 |
| 0.9665 | 3.33 | 30 | 1.0929 | 0.6952 | 0.4754 | 0.6673 | 0.6810 |
| 0.7734 | 4.44 | 40 | 0.9885 | 0.7230 | 0.5207 | 0.6976 | 0.7041 |
| 0.6012 | 5.56 | 50 | 0.9900 | 0.7342 | 0.5549 | 0.7101 | 0.7208 |
| 0.5071 | 6.67 | 60 | 0.9744 | 0.7380 | 0.5513 | 0.7143 | 0.7256 |
| 0.3746 | 7.78 | 70 | 1.0405 | 0.7381 | 0.5550 | 0.7137 | 0.7290 |
| 0.275 | 8.89 | 80 | 1.1184 | 0.7430 | 0.5602 | 0.7193 | 0.7289 |
| 0.2043 | 10.0 | 90 | 1.1665 | 0.7352 | 0.5511 | 0.7062 | 0.7193 |
| 0.1503 | 11.11 | 100 | 1.2355 | 0.7368 | 0.5586 | 0.7077 | 0.7234 |
| 0.1163 | 12.22 | 110 | 1.3027 | 0.7298 | 0.5502 | 0.7023 | 0.7155 |
| 0.099 | 13.33 | 120 | 1.3860 | 0.7274 | 0.5558 | 0.7034 | 0.7144 |
| 0.0824 | 14.44 | 130 | 1.4979 | 0.7298 | 0.5562 | 0.7033 | 0.7172 |
| 0.084 | 15.56 | 140 | 1.4817 | 0.7284 | 0.5452 | 0.6991 | 0.7157 |
| 0.0745 | 16.67 | 150 | 1.4627 | 0.7187 | 0.5376 | 0.6940 | 0.7062 |
| 0.0666 | 17.78 | 160 | 1.4370 | 0.7306 | 0.5498 | 0.7004 | 0.7193 |
| 0.0669 | 18.89 | 170 | 1.5003 | 0.7357 | 0.5602 | 0.7073 | 0.7231 |
| 0.0632 | 20.0 | 180 | 1.4717 | 0.7247 | 0.5493 | 0.7035 | 0.7163 |
| 0.0609 | 21.11 | 190 | 1.4582 | 0.7271 | 0.5469 | 0.7012 | 0.7136 |
| 0.0599 | 22.22 | 200 | 1.5727 | 0.7365 | 0.5576 | 0.7073 | 0.7233 |
| 0.0587 | 23.33 | 210 | 1.5053 | 0.7419 | 0.5605 | 0.7083 | 0.7265 |
| 0.0532 | 24.44 | 220 | 1.5750 | 0.7372 | 0.5631 | 0.7109 | 0.7235 |
| 0.0529 | 25.56 | 230 | 1.5663 | 0.7356 | 0.5515 | 0.7082 | 0.7234 |
| 0.0519 | 26.67 | 240 | 1.5608 | 0.7403 | 0.5601 | 0.7106 | 0.7258 |
| 0.0502 | 27.78 | 250 | 1.5099 | 0.7314 | 0.5467 | 0.6999 | 0.7183 |
| 0.0562 | 28.89 | 260 | 1.5654 | 0.7317 | 0.5592 | 0.7051 | 0.7194 |
| 0.0486 | 30.0 | 270 | 1.5988 | 0.7309 | 0.5556 | 0.7010 | 0.7171 |
| 0.0451 | 31.11 | 280 | 1.5663 | 0.7301 | 0.5577 | 0.7003 | 0.7177 |
| 0.0425 | 32.22 | 290 | 1.6243 | 0.7281 | 0.5563 | 0.7022 | 0.7160 |
| 0.0436 | 33.33 | 300 | 1.6507 | 0.7253 | 0.5509 | 0.6955 | 0.7134 |
| 0.0419 | 34.44 | 310 | 1.5603 | 0.7334 | 0.5520 | 0.7019 | 0.7195 |
| 0.0428 | 35.56 | 320 | 1.6508 | 0.7282 | 0.5469 | 0.6954 | 0.7153 |
| 0.0409 | 36.67 | 330 | 1.7279 | 0.7220 | 0.5396 | 0.6894 | 0.7118 |
| 0.0406 | 37.78 | 340 | 1.6654 | 0.7324 | 0.5540 | 0.7055 | 0.7217 |
| 0.0402 | 38.89 | 350 | 1.7581 | 0.7210 | 0.5397 | 0.6923 | 0.7106 |
| 0.0405 | 40.0 | 360 | 1.6995 | 0.7250 | 0.5472 | 0.6959 | 0.7153 |
| 0.0393 | 41.11 | 370 | 1.7305 | 0.7234 | 0.5399 | 0.6944 | 0.7138 |
| 0.0387 | 42.22 | 380 | 1.7684 | 0.7177 | 0.5363 | 0.6884 | 0.7082 |
| 0.0396 | 43.33 | 390 | 1.7825 | 0.7208 | 0.5390 | 0.6878 | 0.7095 |
| 0.0391 | 44.44 | 400 | 1.7773 | 0.7222 | 0.5392 | 0.6929 | 0.7124 |
| 0.0386 | 45.56 | 410 | 1.8209 | 0.7200 | 0.5415 | 0.6904 | 0.7086 |
| 0.0383 | 46.67 | 420 | 1.7873 | 0.7210 | 0.5403 | 0.6901 | 0.7093 |
| 0.0387 | 47.78 | 430 | 1.7906 | 0.7186 | 0.5396 | 0.6901 | 0.7095 |
| 0.0385 | 48.89 | 440 | 1.8082 | 0.7224 | 0.5448 | 0.6954 | 0.7137 |
| 0.0392 | 50.0 | 450 | 1.7851 | 0.7309 | 0.5472 | 0.6988 | 0.7188 |
| 0.0386 | 51.11 | 460 | 1.8098 | 0.7201 | 0.5414 | 0.6937 | 0.7100 |
| 0.038 | 52.22 | 470 | 1.8145 | 0.7214 | 0.5413 | 0.6931 | 0.7114 |
| 0.0374 | 53.33 | 480 | 1.7956 | 0.7229 | 0.5408 | 0.6919 | 0.7120 |
| 0.038 | 54.44 | 490 | 1.8609 | 0.7231 | 0.5386 | 0.6876 | 0.7093 |
| 0.0375 | 55.56 | 500 | 1.8295 | 0.7253 | 0.5400 | 0.6924 | 0.7127 |
| 0.0384 | 56.67 | 510 | 1.8193 | 0.7238 | 0.5419 | 0.6958 | 0.7138 |
| 0.0374 | 57.78 | 520 | 1.8510 | 0.7202 | 0.5386 | 0.6890 | 0.7083 |
| 0.0382 | 58.89 | 530 | 1.8385 | 0.7227 | 0.5403 | 0.6888 | 0.7098 |
| 0.0374 | 60.0 | 540 | 1.8390 | 0.7203 | 0.5424 | 0.6895 | 0.7089 |
| 0.0374 | 61.11 | 550 | 1.8651 | 0.7202 | 0.5398 | 0.6902 | 0.7084 |
| 0.0378 | 62.22 | 560 | 1.8618 | 0.7236 | 0.5402 | 0.6882 | 0.7097 |
| 0.0374 | 63.33 | 570 | 1.8483 | 0.7203 | 0.5369 | 0.6905 | 0.7097 |
| 0.0363 | 64.44 | 580 | 1.8637 | 0.7190 | 0.5389 | 0.6897 | 0.7089 |
| 0.0378 | 65.56 | 590 | 1.8953 | 0.7236 | 0.5369 | 0.6882 | 0.7099 |
| 0.0377 | 66.67 | 600 | 1.8834 | 0.7210 | 0.5396 | 0.6909 | 0.7104 |
| 0.037 | 67.78 | 610 | 1.8741 | 0.7210 | 0.5436 | 0.6937 | 0.7117 |
| 0.0367 | 68.89 | 620 | 1.8890 | 0.7214 | 0.5419 | 0.6917 | 0.7097 |
| 0.0384 | 70.0 | 630 | 1.8942 | 0.7238 | 0.5432 | 0.6921 | 0.7115 |
| 0.0368 | 71.11 | 640 | 1.8945 | 0.7250 | 0.5414 | 0.6907 | 0.7116 |
| 0.0369 | 72.22 | 650 | 1.9093 | 0.7235 | 0.5402 | 0.6896 | 0.7094 |
| 0.0374 | 73.33 | 660 | 1.9073 | 0.7221 | 0.5432 | 0.6942 | 0.7093 |
| 0.0368 | 74.44 | 670 | 1.8925 | 0.7202 | 0.5434 | 0.6936 | 0.7097 |
| 0.0374 | 75.56 | 680 | 1.8965 | 0.7187 | 0.5434 | 0.6936 | 0.7084 |
| 0.0369 | 76.67 | 690 | 1.9101 | 0.7200 | 0.5422 | 0.6931 | 0.7078 |
| 0.0369 | 77.78 | 700 | 1.9184 | 0.7186 | 0.5407 | 0.6915 | 0.7074 |
| 0.0368 | 78.89 | 710 | 1.9334 | 0.7218 | 0.5411 | 0.6896 | 0.7078 |
| 0.0366 | 80.0 | 720 | 1.9221 | 0.7227 | 0.5411 | 0.6907 | 0.7090 |
| 0.0364 | 81.11 | 730 | 1.9238 | 0.7227 | 0.5427 | 0.6922 | 0.7090 |
| 0.0369 | 82.22 | 740 | 1.9318 | 0.7198 | 0.5432 | 0.6931 | 0.7068 |
| 0.0364 | 83.33 | 750 | 1.9346 | 0.7210 | 0.5432 | 0.6931 | 0.7083 |
| 0.0377 | 84.44 | 760 | 1.9375 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0358 | 85.56 | 770 | 1.9375 | 0.7217 | 0.5427 | 0.6922 | 0.7076 |
| 0.0363 | 86.67 | 780 | 1.9339 | 0.7206 | 0.5427 | 0.6914 | 0.7065 |
| 0.0376 | 87.78 | 790 | 1.9345 | 0.7206 | 0.5427 | 0.6914 | 0.7065 |
| 0.0363 | 88.89 | 800 | 1.9342 | 0.7198 | 0.5432 | 0.6931 | 0.7068 |
| 0.0361 | 90.0 | 810 | 1.9367 | 0.7186 | 0.5422 | 0.6931 | 0.7063 |
| 0.0363 | 91.11 | 820 | 1.9384 | 0.7198 | 0.5432 | 0.6931 | 0.7068 |
| 0.0366 | 92.22 | 830 | 1.9390 | 0.7186 | 0.5422 | 0.6931 | 0.7063 |
| 0.0369 | 93.33 | 840 | 1.9403 | 0.7206 | 0.5438 | 0.6914 | 0.7070 |
| 0.0358 | 94.44 | 850 | 1.9407 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0354 | 95.56 | 860 | 1.9409 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0369 | 96.67 | 870 | 1.9414 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0361 | 97.78 | 880 | 1.9417 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0365 | 98.89 | 890 | 1.9420 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
| 0.0364 | 100.0 | 900 | 1.9415 | 0.7212 | 0.5438 | 0.6914 | 0.7070 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
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