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--- |
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base_model: mistralai/Mistral-7B-v0.3 |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: mistral-7b-drug-prots |
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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-drug-prots |
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This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.5457 |
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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: 5e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 42 |
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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: 64 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 30 |
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- training_steps: 5300 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 1.7818 | 0.0094 | 50 | 1.6715 | |
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| 1.7216 | 0.0189 | 100 | 1.5833 | |
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| 1.6278 | 0.0283 | 150 | 1.5331 | |
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| 1.5849 | 0.0377 | 200 | 1.4866 | |
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| 1.6059 | 0.0472 | 250 | 1.4766 | |
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| 1.6047 | 0.0566 | 300 | 1.4635 | |
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| 1.5167 | 0.0660 | 350 | 1.4515 | |
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| 1.4995 | 0.0755 | 400 | 1.4386 | |
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| 1.5051 | 0.0849 | 450 | 1.4332 | |
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| 1.4858 | 0.0943 | 500 | 1.4210 | |
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| 1.5011 | 0.1038 | 550 | 1.4051 | |
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| 1.497 | 0.1132 | 600 | 1.4005 | |
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| 1.5202 | 0.1226 | 650 | 1.3932 | |
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| 1.5204 | 0.1321 | 700 | 1.3880 | |
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| 1.508 | 0.1415 | 750 | 1.3826 | |
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| 1.4552 | 0.1509 | 800 | 1.3753 | |
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| 1.4866 | 0.1604 | 850 | 1.3706 | |
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| 1.4661 | 0.1698 | 900 | 1.3694 | |
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| 1.4661 | 0.1792 | 950 | 1.3622 | |
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| 1.3875 | 0.1887 | 1000 | 1.3589 | |
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| 1.4471 | 0.1981 | 1050 | 1.3518 | |
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| 1.429 | 0.2075 | 1100 | 1.3390 | |
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| 1.4181 | 0.2170 | 1150 | 1.3365 | |
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| 1.39 | 0.2264 | 1200 | 1.3376 | |
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| 1.4067 | 0.2358 | 1250 | 1.3354 | |
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| 1.4017 | 0.2453 | 1300 | 1.3382 | |
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| 1.3842 | 0.2547 | 1350 | 1.3257 | |
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| 1.4398 | 0.2642 | 1400 | 1.3160 | |
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| 1.3642 | 0.2736 | 1450 | 1.3222 | |
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| 1.3647 | 0.2830 | 1500 | 1.3217 | |
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| 1.4066 | 0.2925 | 1550 | 1.3102 | |
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| 1.4094 | 0.3019 | 1600 | 1.3109 | |
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| 1.3473 | 0.3113 | 1650 | 1.3075 | |
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| 1.3645 | 0.3208 | 1700 | 1.3085 | |
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| 1.3318 | 0.3302 | 1750 | 1.2962 | |
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| 1.3562 | 0.3396 | 1800 | 1.2929 | |
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| 1.3539 | 0.3491 | 1850 | 1.2837 | |
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| 1.3587 | 0.3585 | 1900 | 1.2828 | |
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| 1.3827 | 0.3679 | 1950 | 1.2776 | |
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| 1.3335 | 0.3774 | 2000 | 1.2757 | |
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| 1.3663 | 0.3868 | 2050 | 1.2732 | |
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| 1.2937 | 0.3962 | 2100 | 1.2625 | |
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| 1.3318 | 0.4057 | 2150 | 1.2593 | |
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| 1.2886 | 0.4151 | 2200 | 1.2524 | |
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| 1.3033 | 0.4245 | 2250 | 1.2527 | |
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| 1.2531 | 0.4340 | 2300 | 1.2428 | |
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| 1.2568 | 0.4434 | 2350 | 1.2508 | |
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| 1.2573 | 0.4528 | 2400 | 1.2437 | |
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| 1.2364 | 0.4623 | 2450 | 1.2299 | |
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| 1.2111 | 0.4717 | 2500 | 1.2307 | |
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| 1.2016 | 0.4811 | 2550 | 1.2277 | |
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| 1.236 | 0.4906 | 2600 | 1.2182 | |
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| 1.1858 | 0.5 | 2650 | 1.2237 | |
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| 1.218 | 0.5094 | 2700 | 1.2161 | |
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| 1.1693 | 0.5189 | 2750 | 1.2247 | |
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| 1.1455 | 0.5283 | 2800 | 1.2277 | |
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| 1.1555 | 0.5377 | 2850 | 1.2305 | |
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| 1.162 | 0.5472 | 2900 | 1.2253 | |
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| 1.0834 | 0.5566 | 2950 | 1.2326 | |
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| 1.0964 | 0.5660 | 3000 | 1.2397 | |
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| 1.038 | 0.5755 | 3050 | 1.2370 | |
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| 1.0338 | 0.5849 | 3100 | 1.2477 | |
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| 1.0359 | 0.5943 | 3150 | 1.2390 | |
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| 0.9861 | 0.6038 | 3200 | 1.2547 | |
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| 1.008 | 0.6132 | 3250 | 1.2666 | |
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| 1.0275 | 0.6226 | 3300 | 1.2495 | |
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| 0.9443 | 0.6321 | 3350 | 1.2691 | |
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| 0.8923 | 0.6415 | 3400 | 1.2893 | |
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| 0.9118 | 0.6509 | 3450 | 1.2943 | |
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| 0.8411 | 0.6604 | 3500 | 1.2870 | |
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| 0.8356 | 0.6698 | 3550 | 1.2971 | |
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| 0.8326 | 0.6792 | 3600 | 1.3030 | |
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| 0.8053 | 0.6887 | 3650 | 1.3147 | |
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| 0.7921 | 0.6981 | 3700 | 1.3235 | |
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| 0.7563 | 0.7075 | 3750 | 1.3290 | |
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| 0.7223 | 0.7170 | 3800 | 1.3460 | |
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| 0.7157 | 0.7264 | 3850 | 1.3525 | |
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| 0.7539 | 0.7358 | 3900 | 1.3396 | |
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| 0.6838 | 0.7453 | 3950 | 1.3617 | |
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| 0.7088 | 0.7547 | 4000 | 1.3477 | |
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| 0.6409 | 0.7642 | 4050 | 1.3850 | |
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| 0.6083 | 0.7736 | 4100 | 1.3883 | |
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| 0.594 | 0.7830 | 4150 | 1.4017 | |
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| 0.5721 | 0.7925 | 4200 | 1.4264 | |
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| 0.5144 | 0.8019 | 4250 | 1.4292 | |
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| 0.494 | 0.8113 | 4300 | 1.4427 | |
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| 0.4591 | 0.8208 | 4350 | 1.4588 | |
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| 0.4711 | 0.8302 | 4400 | 1.4627 | |
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| 0.4668 | 0.8396 | 4450 | 1.4641 | |
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| 0.4409 | 0.8491 | 4500 | 1.4778 | |
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| 0.4487 | 0.8585 | 4550 | 1.4821 | |
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| 0.4816 | 0.8679 | 4600 | 1.4711 | |
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| 0.4293 | 0.8774 | 4650 | 1.5048 | |
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| 0.4126 | 0.8868 | 4700 | 1.5079 | |
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| 0.4284 | 0.8962 | 4750 | 1.5040 | |
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| 0.3911 | 0.9057 | 4800 | 1.5293 | |
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| 0.3883 | 0.9151 | 4850 | 1.5293 | |
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| 0.3862 | 0.9245 | 4900 | 1.5243 | |
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| 0.3937 | 0.9340 | 4950 | 1.5440 | |
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| 0.3836 | 0.9434 | 5000 | 1.5389 | |
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| 0.3827 | 0.9528 | 5050 | 1.5437 | |
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| 0.3698 | 0.9623 | 5100 | 1.5545 | |
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| 0.383 | 0.9717 | 5150 | 1.5394 | |
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| 0.401 | 0.9811 | 5200 | 1.5400 | |
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| 0.4024 | 0.9906 | 5250 | 1.5409 | |
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| 0.4305 | 1.0 | 5300 | 1.5457 | |
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### Framework versions |
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- Transformers 4.44.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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