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End of training

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README.md ADDED
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+ ---
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+ base_model: google/gemma-2b
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+ library_name: peft
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+ license: gemma
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+ tags:
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+ - generated_from_trainer
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+ model-index:
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+ - name: Gemma-Medical-QA-LoRA
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+ results: []
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+ ---
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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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+
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+ # Gemma-Medical-QA-LoRA
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+
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+ This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 1.8668
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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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: 4
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+ - eval_batch_size: 4
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_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: linear
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+ - num_epochs: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:------:|:-----:|:---------------:|
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+ | 3.2642 | 0.0103 | 100 | 2.9924 |
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+ | 2.6906 | 0.0205 | 200 | 2.5289 |
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+ | 2.4587 | 0.0308 | 300 | 2.3835 |
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+ | 2.357 | 0.0411 | 400 | 2.3593 |
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+ | 2.3804 | 0.0514 | 500 | 2.3834 |
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+ | 2.3997 | 0.0616 | 600 | 2.4149 |
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+ | 2.4127 | 0.0719 | 700 | 2.4326 |
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+ | 2.4324 | 0.0822 | 800 | 2.4406 |
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+ | 2.4394 | 0.0924 | 900 | 2.4357 |
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+ | 2.4363 | 0.1027 | 1000 | 2.4273 |
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+ | 2.4104 | 0.1130 | 1100 | 2.4209 |
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+ | 2.4255 | 0.1232 | 1200 | 2.4175 |
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+ | 2.4019 | 0.1335 | 1300 | 2.4075 |
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+ | 2.4133 | 0.1438 | 1400 | 2.4035 |
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+ | 2.3963 | 0.1541 | 1500 | 2.3965 |
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+ | 2.3874 | 0.1643 | 1600 | 2.3951 |
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+ | 2.382 | 0.1746 | 1700 | 2.3907 |
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+ | 2.3827 | 0.1849 | 1800 | 2.3838 |
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+ | 2.384 | 0.1951 | 1900 | 2.3715 |
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+ | 2.3625 | 0.2054 | 2000 | 2.3617 |
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+ | 2.353 | 0.2157 | 2100 | 2.3717 |
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+ | 2.3554 | 0.2259 | 2200 | 2.3543 |
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+ | 2.3426 | 0.2362 | 2300 | 2.3422 |
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+ | 2.3522 | 0.2465 | 2400 | 2.3428 |
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+ | 2.3355 | 0.2568 | 2500 | 2.3437 |
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+ | 2.3451 | 0.2670 | 2600 | 2.3227 |
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+ | 2.335 | 0.2773 | 2700 | 2.3303 |
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+ | 2.3107 | 0.2876 | 2800 | 2.3312 |
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+ | 2.3336 | 0.2978 | 2900 | 2.3223 |
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+ | 2.3075 | 0.3081 | 3000 | 2.3116 |
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+ | 2.2991 | 0.3184 | 3100 | 2.3155 |
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+ | 2.3183 | 0.3286 | 3200 | 2.3094 |
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+ | 2.3085 | 0.3389 | 3300 | 2.3035 |
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+ | 2.3049 | 0.3492 | 3400 | 2.2998 |
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+ | 2.3003 | 0.3595 | 3500 | 2.2944 |
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+ | 2.3067 | 0.3697 | 3600 | 2.3003 |
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+ | 2.2962 | 0.3800 | 3700 | 2.3012 |
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+ | 2.2918 | 0.3903 | 3800 | 2.2859 |
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+ | 2.2761 | 0.4005 | 3900 | 2.2826 |
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+ | 2.2916 | 0.4108 | 4000 | 2.2703 |
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+ | 2.2744 | 0.4211 | 4100 | 2.2754 |
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+ | 2.2687 | 0.4313 | 4200 | 2.2646 |
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+ | 2.2673 | 0.4416 | 4300 | 2.2654 |
95
+ | 2.2472 | 0.4519 | 4400 | 2.2617 |
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+ | 2.2669 | 0.4622 | 4500 | 2.2536 |
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+ | 2.2516 | 0.4724 | 4600 | 2.2585 |
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+ | 2.2392 | 0.4827 | 4700 | 2.2514 |
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+ | 2.2551 | 0.4930 | 4800 | 2.2445 |
100
+ | 2.2539 | 0.5032 | 4900 | 2.2380 |
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+ | 2.2462 | 0.5135 | 5000 | 2.2374 |
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+ | 2.2168 | 0.5238 | 5100 | 2.2376 |
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+ | 2.2447 | 0.5340 | 5200 | 2.2272 |
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+ | 2.2022 | 0.5443 | 5300 | 2.2081 |
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+ | 2.2052 | 0.5546 | 5400 | 2.2144 |
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+ | 2.213 | 0.5649 | 5500 | 2.2079 |
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+ | 2.2078 | 0.5751 | 5600 | 2.2071 |
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+ | 2.199 | 0.5854 | 5700 | 2.2007 |
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+ | 2.2037 | 0.5957 | 5800 | 2.1961 |
110
+ | 2.2061 | 0.6059 | 5900 | 2.1962 |
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+ | 2.1766 | 0.6162 | 6000 | 2.1861 |
112
+ | 2.1852 | 0.6265 | 6100 | 2.1868 |
113
+ | 2.1859 | 0.6367 | 6200 | 2.1777 |
114
+ | 2.1891 | 0.6470 | 6300 | 2.1753 |
115
+ | 2.1611 | 0.6573 | 6400 | 2.1712 |
116
+ | 2.1444 | 0.6676 | 6500 | 2.1659 |
117
+ | 2.157 | 0.6778 | 6600 | 2.1505 |
118
+ | 2.1554 | 0.6881 | 6700 | 2.1399 |
119
+ | 2.1344 | 0.6984 | 6800 | 2.1402 |
120
+ | 2.1306 | 0.7086 | 6900 | 2.1252 |
121
+ | 2.0952 | 0.7189 | 7000 | 2.1077 |
122
+ | 2.0969 | 0.7292 | 7100 | 2.0913 |
123
+ | 2.1258 | 0.7394 | 7200 | 2.0886 |
124
+ | 2.0826 | 0.7497 | 7300 | 2.0780 |
125
+ | 2.0782 | 0.7600 | 7400 | 2.0791 |
126
+ | 2.0802 | 0.7703 | 7500 | 2.0685 |
127
+ | 2.0786 | 0.7805 | 7600 | 2.0647 |
128
+ | 2.0618 | 0.7908 | 7700 | 2.0588 |
129
+ | 2.0593 | 0.8011 | 7800 | 2.0619 |
130
+ | 2.0643 | 0.8113 | 7900 | 2.0614 |
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+ | 2.057 | 0.8216 | 8000 | 2.0661 |
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+ | 2.0588 | 0.8319 | 8100 | 2.0524 |
133
+ | 2.0686 | 0.8421 | 8200 | 2.0546 |
134
+ | 2.0333 | 0.8524 | 8300 | 2.0496 |
135
+ | 2.0621 | 0.8627 | 8400 | 2.0474 |
136
+ | 2.023 | 0.8730 | 8500 | 2.0390 |
137
+ | 2.0507 | 0.8832 | 8600 | 2.0414 |
138
+ | 2.0476 | 0.8935 | 8700 | 2.0421 |
139
+ | 2.033 | 0.9038 | 8800 | 2.0400 |
140
+ | 2.0617 | 0.9140 | 8900 | 2.0383 |
141
+ | 2.0287 | 0.9243 | 9000 | 2.0441 |
142
+ | 2.0156 | 0.9346 | 9100 | 2.0335 |
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+ | 2.0207 | 0.9448 | 9200 | 2.0337 |
144
+ | 2.036 | 0.9551 | 9300 | 2.0236 |
145
+ | 2.0411 | 0.9654 | 9400 | 2.0360 |
146
+ | 2.0277 | 0.9757 | 9500 | 2.0262 |
147
+ | 2.0449 | 0.9859 | 9600 | 2.0298 |
148
+ | 2.0323 | 0.9962 | 9700 | 2.0287 |
149
+ | 2.0124 | 1.0065 | 9800 | 2.0220 |
150
+ | 2.0211 | 1.0167 | 9900 | 2.0266 |
151
+ | 2.0137 | 1.0270 | 10000 | 2.0147 |
152
+ | 2.0169 | 1.0373 | 10100 | 2.0132 |
153
+ | 2.0075 | 1.0476 | 10200 | 2.0189 |
154
+ | 2.0022 | 1.0578 | 10300 | 2.0218 |
155
+ | 2.0172 | 1.0681 | 10400 | 2.0088 |
156
+ | 2.0152 | 1.0784 | 10500 | 2.0121 |
157
+ | 1.9993 | 1.0886 | 10600 | 2.0136 |
158
+ | 2.0109 | 1.0989 | 10700 | 2.0035 |
159
+ | 2.0155 | 1.1092 | 10800 | 2.0092 |
160
+ | 2.0101 | 1.1194 | 10900 | 2.0123 |
161
+ | 2.0028 | 1.1297 | 11000 | 2.0066 |
162
+ | 2.005 | 1.1400 | 11100 | 2.0060 |
163
+ | 2.0002 | 1.1503 | 11200 | 2.0026 |
164
+ | 1.994 | 1.1605 | 11300 | 2.0017 |
165
+ | 1.9938 | 1.1708 | 11400 | 2.0023 |
166
+ | 1.9896 | 1.1811 | 11500 | 2.0062 |
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+ | 1.9957 | 1.1913 | 11600 | 1.9995 |
168
+ | 1.987 | 1.2016 | 11700 | 1.9973 |
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+ | 2.0086 | 1.2119 | 11800 | 1.9899 |
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+ | 1.994 | 1.2221 | 11900 | 1.9962 |
171
+ | 1.9668 | 1.2324 | 12000 | 1.9951 |
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+ | 2.0094 | 1.2427 | 12100 | 1.9944 |
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+ | 1.9898 | 1.2530 | 12200 | 1.9907 |
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+ | 1.9958 | 1.2632 | 12300 | 1.9728 |
175
+ | 2.0 | 1.2735 | 12400 | 1.9864 |
176
+ | 1.9818 | 1.2838 | 12500 | 1.9907 |
177
+ | 2.0054 | 1.2940 | 12600 | 1.9877 |
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+ | 1.9837 | 1.3043 | 12700 | 1.9925 |
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+ | 1.9938 | 1.3146 | 12800 | 1.9869 |
180
+ | 2.0015 | 1.3248 | 12900 | 1.9958 |
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+ | 1.9753 | 1.3351 | 13000 | 1.9873 |
182
+ | 1.9632 | 1.3454 | 13100 | 1.9920 |
183
+ | 1.9932 | 1.3557 | 13200 | 1.9782 |
184
+ | 1.9875 | 1.3659 | 13300 | 1.9777 |
185
+ | 1.9693 | 1.3762 | 13400 | 1.9826 |
186
+ | 1.9898 | 1.3865 | 13500 | 1.9748 |
187
+ | 1.9678 | 1.3967 | 13600 | 1.9892 |
188
+ | 1.989 | 1.4070 | 13700 | 1.9770 |
189
+ | 1.9789 | 1.4173 | 13800 | 1.9684 |
190
+ | 1.9733 | 1.4275 | 13900 | 1.9726 |
191
+ | 1.9623 | 1.4378 | 14000 | 1.9726 |
192
+ | 1.95 | 1.4481 | 14100 | 1.9733 |
193
+ | 1.9679 | 1.4584 | 14200 | 1.9689 |
194
+ | 1.964 | 1.4686 | 14300 | 1.9788 |
195
+ | 2.0041 | 1.4789 | 14400 | 1.9707 |
196
+ | 1.9626 | 1.4892 | 14500 | 1.9641 |
197
+ | 1.979 | 1.4994 | 14600 | 1.9682 |
198
+ | 1.9558 | 1.5097 | 14700 | 1.9677 |
199
+ | 1.9701 | 1.5200 | 14800 | 1.9677 |
200
+ | 1.976 | 1.5302 | 14900 | 1.9659 |
201
+ | 1.9448 | 1.5405 | 15000 | 1.9627 |
202
+ | 1.9494 | 1.5508 | 15100 | 1.9698 |
203
+ | 1.9552 | 1.5611 | 15200 | 1.9746 |
204
+ | 1.9628 | 1.5713 | 15300 | 1.9635 |
205
+ | 1.9589 | 1.5816 | 15400 | 1.9668 |
206
+ | 1.9769 | 1.5919 | 15500 | 1.9625 |
207
+ | 1.9513 | 1.6021 | 15600 | 1.9599 |
208
+ | 1.9578 | 1.6124 | 15700 | 1.9638 |
209
+ | 1.9694 | 1.6227 | 15800 | 1.9612 |
210
+ | 1.9508 | 1.6329 | 15900 | 1.9642 |
211
+ | 1.9568 | 1.6432 | 16000 | 1.9583 |
212
+ | 1.9474 | 1.6535 | 16100 | 1.9610 |
213
+ | 1.9616 | 1.6638 | 16200 | 1.9591 |
214
+ | 1.963 | 1.6740 | 16300 | 1.9621 |
215
+ | 1.9612 | 1.6843 | 16400 | 1.9602 |
216
+ | 1.959 | 1.6946 | 16500 | 1.9417 |
217
+ | 1.945 | 1.7048 | 16600 | 1.9535 |
218
+ | 1.9519 | 1.7151 | 16700 | 1.9553 |
219
+ | 1.9361 | 1.7254 | 16800 | 1.9538 |
220
+ | 1.9411 | 1.7356 | 16900 | 1.9450 |
221
+ | 1.9614 | 1.7459 | 17000 | 1.9579 |
222
+ | 1.9634 | 1.7562 | 17100 | 1.9546 |
223
+ | 1.9559 | 1.7665 | 17200 | 1.9501 |
224
+ | 1.9483 | 1.7767 | 17300 | 1.9491 |
225
+ | 1.9603 | 1.7870 | 17400 | 1.9529 |
226
+ | 1.9559 | 1.7973 | 17500 | 1.9495 |
227
+ | 1.9464 | 1.8075 | 17600 | 1.9491 |
228
+ | 1.9598 | 1.8178 | 17700 | 1.9499 |
229
+ | 1.9349 | 1.8281 | 17800 | 1.9512 |
230
+ | 1.9515 | 1.8383 | 17900 | 1.9454 |
231
+ | 1.9481 | 1.8486 | 18000 | 1.9423 |
232
+ | 1.9479 | 1.8589 | 18100 | 1.9486 |
233
+ | 1.944 | 1.8692 | 18200 | 1.9579 |
234
+ | 1.9333 | 1.8794 | 18300 | 1.9406 |
235
+ | 1.9489 | 1.8897 | 18400 | 1.9439 |
236
+ | 1.9603 | 1.9000 | 18500 | 1.9437 |
237
+ | 1.9349 | 1.9102 | 18600 | 1.9366 |
238
+ | 1.9453 | 1.9205 | 18700 | 1.9440 |
239
+ | 1.9199 | 1.9308 | 18800 | 1.9397 |
240
+ | 1.9554 | 1.9410 | 18900 | 1.9443 |
241
+ | 1.9256 | 1.9513 | 19000 | 1.9345 |
242
+ | 1.9393 | 1.9616 | 19100 | 1.9372 |
243
+ | 1.9432 | 1.9719 | 19200 | 1.9392 |
244
+ | 1.935 | 1.9821 | 19300 | 1.9379 |
245
+ | 1.9252 | 1.9924 | 19400 | 1.9459 |
246
+ | 1.9378 | 2.0027 | 19500 | 1.9403 |
247
+ | 1.9326 | 2.0129 | 19600 | 1.9400 |
248
+ | 1.9272 | 2.0232 | 19700 | 1.9380 |
249
+ | 1.9368 | 2.0335 | 19800 | 1.9408 |
250
+ | 1.9318 | 2.0438 | 19900 | 1.9284 |
251
+ | 1.9165 | 2.0540 | 20000 | 1.9395 |
252
+ | 1.941 | 2.0643 | 20100 | 1.9335 |
253
+ | 1.9236 | 2.0746 | 20200 | 1.9384 |
254
+ | 1.9222 | 2.0848 | 20300 | 1.9318 |
255
+ | 1.9242 | 2.0951 | 20400 | 1.9293 |
256
+ | 1.9262 | 2.1054 | 20500 | 1.9298 |
257
+ | 1.9149 | 2.1156 | 20600 | 1.9291 |
258
+ | 1.9226 | 2.1259 | 20700 | 1.9338 |
259
+ | 1.9393 | 2.1362 | 20800 | 1.9306 |
260
+ | 1.9294 | 2.1465 | 20900 | 1.9358 |
261
+ | 1.9275 | 2.1567 | 21000 | 1.9345 |
262
+ | 1.918 | 2.1670 | 21100 | 1.9332 |
263
+ | 1.9164 | 2.1773 | 21200 | 1.9240 |
264
+ | 1.9248 | 2.1875 | 21300 | 1.9302 |
265
+ | 1.9128 | 2.1978 | 21400 | 1.9341 |
266
+ | 1.9159 | 2.2081 | 21500 | 1.9217 |
267
+ | 1.934 | 2.2183 | 21600 | 1.9261 |
268
+ | 1.9258 | 2.2286 | 21700 | 1.9244 |
269
+ | 1.9034 | 2.2389 | 21800 | 1.9384 |
270
+ | 1.9023 | 2.2492 | 21900 | 1.9233 |
271
+ | 1.94 | 2.2594 | 22000 | 1.9232 |
272
+ | 1.9319 | 2.2697 | 22100 | 1.9289 |
273
+ | 1.8986 | 2.2800 | 22200 | 1.9191 |
274
+ | 1.9087 | 2.2902 | 22300 | 1.9196 |
275
+ | 1.9125 | 2.3005 | 22400 | 1.9235 |
276
+ | 1.9081 | 2.3108 | 22500 | 1.9103 |
277
+ | 1.9324 | 2.3210 | 22600 | 1.9249 |
278
+ | 1.9132 | 2.3313 | 22700 | 1.9210 |
279
+ | 1.9134 | 2.3416 | 22800 | 1.9273 |
280
+ | 1.923 | 2.3519 | 22900 | 1.9184 |
281
+ | 1.9271 | 2.3621 | 23000 | 1.9240 |
282
+ | 1.9089 | 2.3724 | 23100 | 1.9195 |
283
+ | 1.9232 | 2.3827 | 23200 | 1.9158 |
284
+ | 1.9153 | 2.3929 | 23300 | 1.9159 |
285
+ | 1.9303 | 2.4032 | 23400 | 1.9214 |
286
+ | 1.9267 | 2.4135 | 23500 | 1.9195 |
287
+ | 1.9041 | 2.4237 | 23600 | 1.9159 |
288
+ | 1.9104 | 2.4340 | 23700 | 1.9119 |
289
+ | 1.9126 | 2.4443 | 23800 | 1.9147 |
290
+ | 1.9179 | 2.4546 | 23900 | 1.9173 |
291
+ | 1.8934 | 2.4648 | 24000 | 1.9156 |
292
+ | 1.9213 | 2.4751 | 24100 | 1.9168 |
293
+ | 1.9244 | 2.4854 | 24200 | 1.9193 |
294
+ | 1.9064 | 2.4956 | 24300 | 1.9014 |
295
+ | 1.8898 | 2.5059 | 24400 | 1.9215 |
296
+ | 1.9063 | 2.5162 | 24500 | 1.9200 |
297
+ | 1.9105 | 2.5264 | 24600 | 1.9210 |
298
+ | 1.916 | 2.5367 | 24700 | 1.9113 |
299
+ | 1.9099 | 2.5470 | 24800 | 1.9090 |
300
+ | 1.9051 | 2.5573 | 24900 | 1.9060 |
301
+ | 1.9177 | 2.5675 | 25000 | 1.9181 |
302
+ | 1.923 | 2.5778 | 25100 | 1.9144 |
303
+ | 1.8971 | 2.5881 | 25200 | 1.9079 |
304
+ | 1.9133 | 2.5983 | 25300 | 1.9068 |
305
+ | 1.9318 | 2.6086 | 25400 | 1.9089 |
306
+ | 1.9149 | 2.6189 | 25500 | 1.9109 |
307
+ | 1.9145 | 2.6291 | 25600 | 1.9076 |
308
+ | 1.911 | 2.6394 | 25700 | 1.9149 |
309
+ | 1.8884 | 2.6497 | 25800 | 1.9018 |
310
+ | 1.8946 | 2.6600 | 25900 | 1.9217 |
311
+ | 1.9106 | 2.6702 | 26000 | 1.9082 |
312
+ | 1.906 | 2.6805 | 26100 | 1.9063 |
313
+ | 1.9026 | 2.6908 | 26200 | 1.9070 |
314
+ | 1.9088 | 2.7010 | 26300 | 1.9059 |
315
+ | 1.8938 | 2.7113 | 26400 | 1.9019 |
316
+ | 1.8964 | 2.7216 | 26500 | 1.9131 |
317
+ | 1.8947 | 2.7318 | 26600 | 1.9096 |
318
+ | 1.8906 | 2.7421 | 26700 | 1.9053 |
319
+ | 1.911 | 2.7524 | 26800 | 1.8969 |
320
+ | 1.887 | 2.7627 | 26900 | 1.9111 |
321
+ | 1.8864 | 2.7729 | 27000 | 1.9064 |
322
+ | 1.9195 | 2.7832 | 27100 | 1.9044 |
323
+ | 1.9129 | 2.7935 | 27200 | 1.9019 |
324
+ | 1.8915 | 2.8037 | 27300 | 1.8970 |
325
+ | 1.9035 | 2.8140 | 27400 | 1.9049 |
326
+ | 1.8755 | 2.8243 | 27500 | 1.8986 |
327
+ | 1.8939 | 2.8345 | 27600 | 1.8993 |
328
+ | 1.895 | 2.8448 | 27700 | 1.8978 |
329
+ | 1.8744 | 2.8551 | 27800 | 1.9052 |
330
+ | 1.9178 | 2.8654 | 27900 | 1.9040 |
331
+ | 1.8916 | 2.8756 | 28000 | 1.8977 |
332
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333
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334
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335
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336
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337
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338
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339
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340
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341
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342
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344
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345
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346
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347
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348
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349
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351
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355
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356
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357
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362
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367
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369
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370
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371
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372
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373
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374
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377
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378
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380
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381
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382
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383
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384
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385
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386
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387
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388
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389
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390
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391
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392
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398
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399
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401
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402
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433
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468
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469
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480
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483
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484
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485
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488
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490
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491
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492
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493
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494
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495
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497
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498
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499
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500
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501
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502
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504
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505
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506
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508
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509
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510
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511
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512
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513
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514
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515
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516
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517
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518
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519
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520
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521
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522
+ | 1.8621 | 4.8372 | 47100 | 1.8682 |
523
+ | 1.8679 | 4.8475 | 47200 | 1.8691 |
524
+ | 1.8653 | 4.8578 | 47300 | 1.8669 |
525
+ | 1.8427 | 4.8680 | 47400 | 1.8645 |
526
+ | 1.8538 | 4.8783 | 47500 | 1.8658 |
527
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528
+ | 1.8693 | 4.8988 | 47700 | 1.8661 |
529
+ | 1.8379 | 4.9091 | 47800 | 1.8668 |
530
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531
+ | 1.8537 | 4.9296 | 48000 | 1.8664 |
532
+ | 1.8568 | 4.9399 | 48100 | 1.8664 |
533
+ | 1.867 | 4.9502 | 48200 | 1.8661 |
534
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535
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536
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537
+ | 1.8736 | 4.9913 | 48600 | 1.8668 |
538
+
539
+
540
+ ### Framework versions
541
+
542
+ - PEFT 0.12.0
543
+ - Transformers 4.43.3
544
+ - Pytorch 1.13.1+cu117
545
+ - Datasets 2.19.2
546
+ - Tokenizers 0.19.1
runs/Aug05_03-07-07_cmle-training-7460638318143501570/events.out.tfevents.1722827228.cmle-training-7460638318143501570 CHANGED
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