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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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metrics: |
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- accuracy |
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model-index: |
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- name: Mistral-7B-v0.1_cola_relu |
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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-7B-v0.1_cola_relu |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3969 |
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- Accuracy: 0.8528 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 2 |
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- distributed_type: multi-GPU |
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- num_devices: 2 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 256 |
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- total_eval_batch_size: 128 |
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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: 750 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 3.3308 | 0.33 | 10 | 3.2312 | 0.6721 | |
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| 1.9948 | 0.66 | 20 | 1.9259 | 0.5628 | |
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| 1.755 | 0.98 | 30 | 1.6666 | 0.6529 | |
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| 1.2472 | 1.31 | 40 | 1.3599 | 0.6280 | |
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| 0.7 | 1.64 | 50 | 1.0398 | 0.6903 | |
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| 1.0118 | 1.97 | 60 | 0.8845 | 0.6798 | |
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| 0.7947 | 2.3 | 70 | 0.7958 | 0.7200 | |
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| 0.8203 | 2.62 | 80 | 0.7160 | 0.7191 | |
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| 0.8548 | 2.95 | 90 | 0.6607 | 0.7296 | |
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| 0.5277 | 3.28 | 100 | 0.6292 | 0.7430 | |
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| 0.7134 | 3.61 | 110 | 0.6562 | 0.7440 | |
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| 0.7233 | 3.93 | 120 | 0.6248 | 0.7488 | |
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| 0.5547 | 4.26 | 130 | 0.5399 | 0.7488 | |
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| 0.5171 | 4.59 | 140 | 0.5230 | 0.7536 | |
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| 0.492 | 4.92 | 150 | 0.5184 | 0.7632 | |
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| 0.5003 | 5.25 | 160 | 0.4999 | 0.7728 | |
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| 0.4884 | 5.57 | 170 | 0.4827 | 0.7814 | |
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| 0.514 | 5.9 | 180 | 0.5048 | 0.7910 | |
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| 0.3669 | 6.23 | 190 | 0.4783 | 0.7977 | |
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| 0.4786 | 6.56 | 200 | 0.4533 | 0.7948 | |
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| 0.4244 | 6.89 | 210 | 0.4379 | 0.8035 | |
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| 0.3235 | 7.21 | 220 | 0.4439 | 0.8073 | |
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| 0.4307 | 7.54 | 230 | 0.4258 | 0.8236 | |
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| 0.404 | 7.87 | 240 | 0.4184 | 0.8188 | |
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| 0.3772 | 8.2 | 250 | 0.4089 | 0.8207 | |
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| 0.3937 | 8.52 | 260 | 0.4595 | 0.8092 | |
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| 0.3896 | 8.85 | 270 | 0.4148 | 0.8265 | |
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| 0.3296 | 9.18 | 280 | 0.4130 | 0.8236 | |
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| 0.328 | 9.51 | 290 | 0.3944 | 0.8389 | |
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| 0.3383 | 9.84 | 300 | 0.3862 | 0.8322 | |
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| 0.3146 | 10.16 | 310 | 0.3847 | 0.8418 | |
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| 0.3069 | 10.49 | 320 | 0.4192 | 0.8245 | |
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| 0.2732 | 10.82 | 330 | 0.4190 | 0.8313 | |
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| 0.2819 | 11.15 | 340 | 0.4427 | 0.8188 | |
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| 0.3738 | 11.48 | 350 | 0.3807 | 0.8408 | |
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| 0.3004 | 11.8 | 360 | 0.3722 | 0.8437 | |
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| 0.2894 | 12.13 | 370 | 0.3922 | 0.8341 | |
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| 0.2747 | 12.46 | 380 | 0.3782 | 0.8370 | |
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| 0.2812 | 12.79 | 390 | 0.3667 | 0.8514 | |
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| 0.2369 | 13.11 | 400 | 0.3884 | 0.8408 | |
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| 0.2931 | 13.44 | 410 | 0.3807 | 0.8456 | |
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| 0.2702 | 13.77 | 420 | 0.3742 | 0.8399 | |
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| 0.2821 | 14.1 | 430 | 0.3737 | 0.8485 | |
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| 0.2358 | 14.43 | 440 | 0.3739 | 0.8456 | |
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| 0.2326 | 14.75 | 450 | 0.3699 | 0.8514 | |
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| 0.2475 | 15.08 | 460 | 0.3771 | 0.8466 | |
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| 0.2402 | 15.41 | 470 | 0.4064 | 0.8351 | |
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| 0.2435 | 15.74 | 480 | 0.3758 | 0.8456 | |
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| 0.1896 | 16.07 | 490 | 0.3779 | 0.8456 | |
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| 0.2228 | 16.39 | 500 | 0.3868 | 0.8456 | |
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| 0.2149 | 16.72 | 510 | 0.3800 | 0.8485 | |
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| 0.1781 | 17.05 | 520 | 0.3841 | 0.8514 | |
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| 0.1729 | 17.38 | 530 | 0.4000 | 0.8476 | |
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| 0.1897 | 17.7 | 540 | 0.3866 | 0.8456 | |
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| 0.1537 | 18.03 | 550 | 0.4317 | 0.8370 | |
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| 0.1478 | 18.36 | 560 | 0.4197 | 0.8466 | |
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| 0.1686 | 18.69 | 570 | 0.4325 | 0.8418 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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