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ayousanz/mistral-0.3b-backup
mistral-0.3b-backup is a text generation model from ayousanz. 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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From the Hugging Face model README
This model is a fine-tuned version of None on an unknown dataset. It achieves the following results on the evaluation set:
More information needed
More information needed
More information needed
The following hyperparameters were used during training:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.5141 | 0.09 | 1000 | 4.5160 |
| 3.7879 | 0.18 | 2000 | 3.8531 |
| 3.5484 | 0.27 | 3000 | 3.5881 |
| 3.3734 | 0.36 | 4000 | 3.4287 |
| 3.2722 | 0.45 | 5000 | 3.3144 |
| 3.2276 | 0.54 | 6000 | 3.2299 |
| 3.1809 | 0.63 | 7000 | 3.1597 |
| 3.0706 | 0.72 | 8000 | 3.1043 |
| 3.0185 | 0.81 | 9000 | 3.0578 |
| 2.9496 | 0.9 | 10000 | 3.0157 |
| 2.9374 | 0.99 | 11000 | 2.9815 |
| 2.8794 | 1.08 | 12000 | 2.9487 |
| 2.8407 | 1.17 | 13000 | 2.9229 |
| 2.8818 | 1.26 | 14000 | 2.8973 |
| 2.8167 | 1.35 | 15000 | 2.8730 |
| 2.7941 | 1.44 | 16000 | 2.8515 |
| 2.7878 | 1.53 | 17000 | 2.8311 |
| 2.7894 | 1.62 | 18000 | 2.8113 |
| 2.7158 | 1.71 | 19000 | 2.7935 |
| 2.7409 | 1.8 | 20000 | 2.7765 |
| 2.7349 | 1.89 | 21000 | 2.7613 |
| 2.6631 | 1.98 | 22000 | 2.7451 |
| 2.6766 | 2.07 | 23000 | 2.7353 |
| 2.6405 | 2.16 | 24000 | 2.7231 |
| 2.6707 | 2.25 | 25000 | 2.7121 |
| 2.6362 | 2.34 | 26000 | 2.7005 |
| 2.5997 | 2.43 | 27000 | 2.6904 |
| 2.6549 | 2.52 | 28000 | 2.6798 |
| 2.6056 | 2.61 | 29000 | 2.6688 |
| 2.5722 | 2.7 | 30000 | 2.6594 |
| 2.6179 | 2.79 | 31000 | 2.6509 |
| 2.6064 | 2.88 | 32000 | 2.6423 |
| 2.5836 | 2.97 | 33000 | 2.6340 |
| 2.5502 | 3.06 | 34000 | 2.6285 |
| 2.5428 | 3.15 | 35000 | 2.6218 |
| 2.5342 | 3.24 | 36000 | 2.6160 |
| 2.5152 | 3.33 | 37000 | 2.6090 |
| 2.5138 | 5.13 | 38000 | 2.5766 |
| 2.5032 | 5.27 | 39000 | 2.5683 |
| 2.4783 | 5.4 | 40000 | 2.5609 |
| 2.4519 | 5.54 | 41000 | 2.5545 |
| 2.4918 | 5.67 | 42000 | 2.5472 |
| 2.4591 | 5.81 | 43000 | 2.5411 |
| 2.4756 | 5.94 | 44000 | 2.5354 |
| 2.4434 | 6.08 | 45000 | 2.5345 |
| 2.4312 | 6.21 | 46000 | 2.5301 |
| 2.4576 | 6.35 | 47000 | 2.5242 |
| 2.4343 | 6.48 | 48000 | 2.5192 |
| 2.426 | 6.62 | 49000 | 2.5139 |
| 2.4136 | 6.75 | 50000 | 2.5084 |
| 2.4463 | 6.89 | 51000 | 2.5037 |
| 2.345 | 7.02 | 52000 | 2.5016 |
| 2.3736 | 7.16 | 53000 | 2.4990 |
| 2.4092 | 7.29 | 54000 | 2.4955 |
| 2.3689 | 7.43 | 55000 | 2.4917 |
| 2.3797 | 7.56 | 56000 | 2.4867 |