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GPTNeoX-160M-Minipile
SingularityHJY/GPTNeoX-160M-Minipile
GPTNeoX-160M-Minipile is a text generation model from SingularityHJY. Use it when you need the model to write or continue text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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.safetensors325 MB · 99%
At a glance
- Task
- Text Generation
- Library
- transformers
- Model type
- gpt_neox
- Access
- Public
- Created
- Nov 11, 2024
- Updated
- Nov 12, 2024
- SHA
- 995b891b
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Model card
From the Hugging Face model README
<!-- 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. -->
GPTNeoX-160M-Minipile
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.8779
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: 0.001
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.6923 | 0.0281 | 500 | 6.6275 |
| 5.7396 | 0.0561 | 1000 | 5.7512 |
| 5.3007 | 0.0842 | 1500 | 5.2374 |
| 4.6605 | 0.1123 | 2000 | 4.6288 |
| 4.2878 | 0.1403 | 2500 | 4.2100 |
| 4.0088 | 0.1684 | 3000 | 3.9680 |
| 3.8332 | 0.1965 | 3500 | 3.8331 |
| 3.7552 | 0.2246 | 4000 | 3.7197 |
| 3.6096 | 0.2526 | 4500 | 3.6527 |
| 3.6151 | 0.2807 | 5000 | 3.5885 |
| 3.4574 | 0.3088 | 5500 | 3.5467 |
| 3.4561 | 0.3368 | 6000 | 3.4924 |
| 3.3655 | 0.3649 | 6500 | 3.4318 |
| 3.4202 | 0.3930 | 7000 | 3.3960 |
| 3.3726 | 0.4210 | 7500 | 3.3732 |
| 3.3313 | 0.4491 | 8000 | 3.3337 |
| 3.3411 | 0.4772 | 8500 | 3.3040 |
| 3.3544 | 0.5052 | 9000 | 3.2786 |
| 3.2502 | 0.5333 | 9500 | 3.2705 |
| 3.2733 | 0.5614 | 10000 | 3.2517 |
| 3.2467 | 0.5895 | 10500 | 3.2253 |
| 3.2516 | 0.6175 | 11000 | 3.2078 |
| 3.1775 | 0.6456 | 11500 | 3.1942 |
| 3.207 | 0.6737 | 12000 | 3.1785 |
| 3.1331 | 0.7017 | 12500 | 3.1725 |
| 3.1398 | 0.7298 | 13000 | 3.1542 |
| 3.1569 | 0.7579 | 13500 | 3.1462 |
| 3.148 | 0.7859 | 14000 | 3.1280 |
| 3.1547 | 0.8140 | 14500 | 3.1201 |
| 3.1583 | 0.8421 | 15000 | 3.1070 |
| 3.103 | 0.8702 | 15500 | 3.0974 |
| 3.0527 | 0.8982 | 16000 | 3.0862 |
| 3.0612 | 0.9263 | 16500 | 3.0764 |
| 3.0707 | 0.9544 | 17000 | 3.0663 |
| 3.0632 | 0.9824 | 17500 | 3.0607 |
| 3.0317 | 1.0105 | 18000 | 3.0491 |
| 3.0108 | 1.0386 | 18500 | 3.0479 |
| 3.035 | 1.0666 | 19000 | 3.0415 |
| 3.0164 | 1.0947 | 19500 | 3.0324 |
| 2.9707 | 1.1228 | 20000 | 3.0250 |
| 3.0121 | 1.1508 | 20500 | 3.0211 |
| 2.9887 | 1.1789 | 21000 | 3.0135 |
| 2.9933 | 1.2070 | 21500 | 3.0050 |
| 2.9535 | 1.2351 | 22000 | 3.0005 |
| 2.9651 | 1.2631 | 22500 | 2.9931 |
| 2.9965 | 1.2912 | 23000 | 2.9875 |
| 2.9886 | 1.3193 | 23500 | 2.9819 |
| 2.92 | 1.3473 | 24000 | 2.9752 |
| 2.9263 | 1.3754 | 24500 | 2.9717 |
| 2.8707 | 1.4035 | 25000 | 2.9691 |
| 2.923 | 1.4315 | 25500 | 2.9627 |
| 2.9615 | 1.4596 | 26000 | 2.9555 |
| 2.9154 | 1.4877 | 26500 | 2.9518 |
| 2.9112 | 1.5157 | 27000 | 2.9481 |
| 2.9033 | 1.5438 | 27500 | 2.9433 |
| 2.9711 | 1.5719 | 28000 | 2.9379 |
| 2.8926 | 1.6000 | 28500 | 2.9344 |
| 2.9006 | 1.6280 | 29000 | 2.9301 |
| 2.9529 | 1.6561 | 29500 | 2.9263 |
| 2.8718 | 1.6842 | 30000 | 2.9223 |
| 2.8989 | 1.7122 | 30500 | 2.9188 |
| 2.9101 | 1.7403 | 31000 | 2.9149 |
| 2.9219 | 1.7684 | 31500 | 2.9120 |
| 2.9225 | 1.7964 | 32000 | 2.9082 |
| 2.8855 | 1.8245 | 32500 | 2.9058 |
| 2.8643 | 1.8526 | 33000 | 2.9026 |
| 2.8996 | 1.8806 | 33500 | 2.8999 |
| 2.9717 | 1.9087 | 34000 | 2.8974 |
| 2.8536 | 1.9368 | 34500 | 2.8960 |
| 2.8435 | 1.9649 | 35000 | 2.8928 |
| 2.861 | 1.9929 | 35500 | 2.8906 |
| 2.7977 | 2.0210 | 36000 | 2.8894 |
| 2.8228 | 2.0491 | 36500 | 2.8895 |
| 2.8064 | 2.0771 | 37000 | 2.8874 |
| 2.827 | 2.1052 | 37500 | 2.8863 |
| 2.8202 | 2.1333 | 38000 | 2.8852 |
| 2.8581 | 2.1613 | 38500 | 2.8840 |
| 2.8004 | 2.1894 | 39000 | 2.8828 |
| 2.7771 | 2.2175 | 39500 | 2.8820 |
| 2.8305 | 2.2456 | 40000 | 2.8814 |
| 2.8659 | 2.2736 | 40500 | 2.8806 |
| 2.8176 | 2.3017 | 41000 | 2.8804 |
| 2.8101 | 2.3298 | 41500 | 2.8797 |
| 2.7866 | 2.3578 | 42000 | 2.8792 |
| 2.8114 | 2.3859 | 42500 | 2.8789 |
| 2.8204 | 2.4140 | 43000 | 2.8786 |
| 2.861 | 2.4420 | 43500 | 2.8782 |
| 2.8333 | 2.4701 | 44000 | 2.8781 |
| 2.8111 | 2.4982 | 44500 | 2.8781 |
| 2.8145 | 2.5262 | 45000 | 2.8779 |
| 2.8349 | 2.5543 | 45500 | 2.8778 |
| 2.8058 | 2.5824 | 46000 | 2.8778 |
| 2.7971 | 2.6105 | 46500 | 2.8778 |
| 2.8267 | 2.6385 | 47000 | 2.8777 |
| 2.8172 | 2.6666 | 47500 | 2.8777 |
| 2.8355 | 2.6947 | 48000 | 2.8778 |
| 2.8189 | 2.7227 | 48500 | 2.8778 |
| 2.8155 | 2.7508 | 49000 | 2.8778 |
| 2.8542 | 2.7789 | 49500 | 2.8778 |
| 2.8476 | 2.8069 | 50000 | 2.8779 |
| 2.8288 | 2.8350 | 50500 | 2.8779 |
| 2.8399 | 2.8631 | 51000 | 2.8779 |
| 2.767 | 2.8911 | 51500 | 2.8779 |
| 2.8238 | 2.9192 | 52000 | 2.8779 |
| 2.7982 | 2.9473 | 52500 | 2.8779 |
| 2.7844 | 2.9754 | 53000 | 2.8779 |
Framework versions
- Transformers 4.45.0
- Pytorch 2.4.1
- Datasets 3.0.1
- Tokenizers 0.20.3
- Task
- Text Generation
- Library
- transformers
- Type
- gpt_neox
- Created
- Nov 11, 2024
- Updated
- Nov 12, 2024