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mistral-7b-drug-prots

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mistral

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Kamyar-zeinalipour commited on Dec 26, 2024

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