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--- |
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license: apache-2.0 |
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base_model: mistralai/Mistral-7B-v0.1 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Mistral_Sparse_refined_web_relu_2024-03-11 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Mistral_Sparse_refined_web_relu_2024-03-11 |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3749 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 0 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- training_steps: 2600 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 8.7876 | 0.0 | 25 | 8.7120 | |
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| 8.1837 | 0.01 | 50 | 8.1741 | |
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| 7.7529 | 0.01 | 75 | 7.8037 | |
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| 7.5336 | 0.02 | 100 | 7.6036 | |
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| 7.1718 | 0.02 | 125 | 7.1058 | |
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| 4.7663 | 0.02 | 150 | 4.7163 | |
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| 3.7548 | 0.03 | 175 | 3.8001 | |
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| 3.2625 | 0.03 | 200 | 3.4192 | |
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| 3.0886 | 0.04 | 225 | 3.2355 | |
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| 2.9928 | 0.04 | 250 | 3.1153 | |
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| 2.8555 | 0.04 | 275 | 3.0392 | |
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| 2.8289 | 0.05 | 300 | 2.9870 | |
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| 2.7933 | 0.05 | 325 | 2.9505 | |
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| 2.6073 | 0.06 | 350 | 2.9160 | |
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| 2.7488 | 0.06 | 375 | 2.8941 | |
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| 2.7795 | 0.06 | 400 | 2.8722 | |
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| 2.7317 | 0.07 | 425 | 2.8537 | |
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| 2.6982 | 0.07 | 450 | 2.8407 | |
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| 2.5823 | 0.08 | 475 | 2.8258 | |
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| 2.6419 | 0.08 | 500 | 2.8164 | |
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| 2.7195 | 0.08 | 525 | 2.8082 | |
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| 2.6239 | 0.09 | 550 | 2.7980 | |
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| 2.7273 | 0.09 | 575 | 2.7869 | |
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| 2.5436 | 0.1 | 600 | 2.7809 | |
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| 2.6159 | 0.1 | 625 | 2.7761 | |
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| 2.6563 | 0.1 | 650 | 2.7666 | |
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| 2.6728 | 0.11 | 675 | 2.7573 | |
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| 2.6047 | 0.11 | 700 | 2.7509 | |
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| 2.6237 | 0.12 | 725 | 2.7493 | |
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| 2.5305 | 0.12 | 750 | 2.7458 | |
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| 2.5329 | 0.12 | 775 | 2.7392 | |
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| 2.6538 | 0.13 | 800 | 2.7359 | |
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| 2.6076 | 0.13 | 825 | 2.7310 | |
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| 2.5928 | 0.14 | 850 | 2.7279 | |
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| 2.455 | 0.14 | 875 | 2.7246 | |
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| 2.5579 | 0.14 | 900 | 2.7252 | |
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| 2.4948 | 0.15 | 925 | 2.7194 | |
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| 2.6219 | 0.15 | 950 | 2.7181 | |
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| 2.5387 | 0.16 | 975 | 2.7139 | |
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| 2.5734 | 0.16 | 1000 | 2.7134 | |
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| 2.6012 | 0.16 | 1025 | 2.7115 | |
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| 2.63 | 0.17 | 1050 | 2.7076 | |
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| 2.6361 | 0.17 | 1075 | 2.7045 | |
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| 2.5534 | 0.18 | 1100 | 2.7046 | |
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| 2.5756 | 0.18 | 1125 | 2.7031 | |
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| 2.5632 | 0.18 | 1150 | 2.6989 | |
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| 2.5971 | 0.19 | 1175 | 2.6960 | |
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| 2.4719 | 0.19 | 1200 | 2.6963 | |
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| 2.5377 | 0.2 | 1225 | 2.6944 | |
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| 2.552 | 0.2 | 1250 | 2.6907 | |
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| 2.5748 | 0.2 | 1275 | 2.6894 | |
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| 2.5799 | 0.21 | 1300 | 2.6877 | |
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| 2.5569 | 0.21 | 1325 | 2.6834 | |
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| 2.4413 | 0.22 | 1350 | 2.6822 | |
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| 2.5232 | 0.22 | 1375 | 2.6822 | |
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| 2.5346 | 0.22 | 1400 | 2.6806 | |
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| 2.479 | 0.23 | 1425 | 2.6791 | |
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| 2.4585 | 0.23 | 1450 | 2.6803 | |
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| 2.4104 | 0.24 | 1475 | 2.6776 | |
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| 2.4961 | 0.24 | 1500 | 2.6792 | |
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| 2.4219 | 0.24 | 1525 | 2.6770 | |
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| 2.4658 | 0.25 | 1550 | 2.6736 | |
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| 2.5875 | 0.25 | 1575 | 2.6755 | |
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| 2.5376 | 0.26 | 1600 | 2.6705 | |
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| 2.5466 | 0.26 | 1625 | 2.6726 | |
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| 2.5889 | 0.26 | 1650 | 2.6704 | |
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| 2.4973 | 0.27 | 1675 | 2.6667 | |
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| 2.5409 | 0.27 | 1700 | 2.6681 | |
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| 2.5386 | 0.28 | 1725 | 2.6658 | |
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| 2.5234 | 0.28 | 1750 | 2.6666 | |
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| 2.5066 | 0.28 | 1775 | 2.6619 | |
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| 2.4283 | 0.29 | 1800 | 2.6629 | |
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| 2.5253 | 0.29 | 1825 | 2.6623 | |
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| 2.5179 | 0.3 | 1850 | 2.6599 | |
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| 2.5023 | 0.3 | 1875 | 2.6608 | |
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| 2.5253 | 0.3 | 1900 | 2.6602 | |
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| 2.5788 | 0.31 | 1925 | 2.6602 | |
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| 2.5307 | 0.31 | 1950 | 2.6596 | |
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| 2.5108 | 0.32 | 1975 | 2.6593 | |
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| 2.462 | 0.32 | 2000 | 2.6597 | |
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| 2.5028 | 0.32 | 2025 | 2.6577 | |
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| 2.48 | 0.33 | 2050 | 2.6538 | |
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| 2.4742 | 0.33 | 2075 | 2.6534 | |
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| 2.554 | 0.34 | 2100 | 2.6544 | |
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| 2.5987 | 0.34 | 2125 | 2.6547 | |
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| 2.5224 | 0.34 | 2150 | 2.6550 | |
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| 2.4526 | 0.35 | 2175 | 2.6510 | |
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| 2.503 | 0.35 | 2200 | 2.6484 | |
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| 2.4648 | 0.36 | 2225 | 2.6487 | |
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| 2.4568 | 0.36 | 2250 | 2.6481 | |
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| 2.5701 | 0.36 | 2275 | 2.6465 | |
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| 2.5403 | 0.37 | 2300 | 2.6467 | |
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| 2.435 | 0.37 | 2325 | 2.6472 | |
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| 2.4823 | 0.38 | 2350 | 2.6479 | |
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| 2.536 | 0.38 | 2375 | 2.6468 | |
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| 2.5171 | 0.38 | 2400 | 2.6470 | |
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| 2.3852 | 0.39 | 2425 | 2.6475 | |
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| 2.3807 | 0.39 | 2450 | 2.6471 | |
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| 2.4753 | 0.4 | 2475 | 2.6456 | |
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| 2.5507 | 0.4 | 2500 | 2.6442 | |
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| 2.5331 | 0.4 | 2525 | 2.6441 | |
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| 2.3988 | 0.41 | 2550 | 2.6415 | |
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| 2.425 | 0.41 | 2575 | 2.6403 | |
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| 2.5062 | 0.42 | 2600 | 2.6429 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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