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--- |
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license: gemma |
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library_name: peft |
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tags: |
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- generated_from_trainer |
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base_model: google/paligemma-3b-pt-224 |
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model-index: |
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- name: paligemma_vqav2 |
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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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# paligemma_vqav2 |
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This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on an Magneto/caption_for_mars_image_512_qa_format dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.0115 |
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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: 2e-05 |
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- train_batch_size: 2 |
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- eval_batch_size: 2 |
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- seed: 42 |
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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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- lr_scheduler_warmup_steps: 2 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 2.6221 | 0.0114 | 50 | 2.4036 | |
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| 2.244 | 0.0228 | 100 | 2.0980 | |
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| 2.0078 | 0.0343 | 150 | 1.8881 | |
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| 1.8561 | 0.0457 | 200 | 1.7707 | |
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| 1.6108 | 0.0571 | 250 | 1.6833 | |
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| 1.5712 | 0.0685 | 300 | 1.6297 | |
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| 1.6298 | 0.0800 | 350 | 1.5834 | |
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| 1.469 | 0.0914 | 400 | 1.5454 | |
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| 1.4758 | 0.1028 | 450 | 1.5210 | |
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| 1.5303 | 0.1142 | 500 | 1.4936 | |
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| 1.3559 | 0.1257 | 550 | 1.4793 | |
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| 1.4407 | 0.1371 | 600 | 1.4596 | |
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| 1.4655 | 0.1485 | 650 | 1.4360 | |
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| 1.4213 | 0.1599 | 700 | 1.4223 | |
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| 1.3744 | 0.1714 | 750 | 1.4022 | |
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| 1.4285 | 0.1828 | 800 | 1.3906 | |
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| 1.2105 | 0.1942 | 850 | 1.3790 | |
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| 1.3653 | 0.2056 | 900 | 1.3687 | |
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| 1.337 | 0.2170 | 950 | 1.3602 | |
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| 1.1845 | 0.2285 | 1000 | 1.3509 | |
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| 1.3404 | 0.2399 | 1050 | 1.3384 | |
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| 1.2957 | 0.2513 | 1100 | 1.3278 | |
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| 1.2107 | 0.2627 | 1150 | 1.3176 | |
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| 1.4208 | 0.2742 | 1200 | 1.3132 | |
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| 1.2522 | 0.2856 | 1250 | 1.3032 | |
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| 1.2735 | 0.2970 | 1300 | 1.2992 | |
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| 1.3567 | 0.3084 | 1350 | 1.2854 | |
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| 1.0994 | 0.3199 | 1400 | 1.2805 | |
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| 1.2496 | 0.3313 | 1450 | 1.2710 | |
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| 1.1944 | 0.3427 | 1500 | 1.2660 | |
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| 1.3303 | 0.3541 | 1550 | 1.2610 | |
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| 1.2942 | 0.3655 | 1600 | 1.2524 | |
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| 1.2187 | 0.3770 | 1650 | 1.2458 | |
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| 1.2071 | 0.3884 | 1700 | 1.2395 | |
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| 1.1734 | 0.3998 | 1750 | 1.2356 | |
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| 1.182 | 0.4112 | 1800 | 1.2301 | |
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| 1.2104 | 0.4227 | 1850 | 1.2302 | |
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| 1.1961 | 0.4341 | 1900 | 1.2258 | |
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| 1.1749 | 0.4455 | 1950 | 1.2244 | |
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| 1.1283 | 0.4569 | 2000 | 1.2189 | |
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| 1.095 | 0.4684 | 2050 | 1.2174 | |
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| 1.1376 | 0.4798 | 2100 | 1.2172 | |
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| 1.0772 | 0.4912 | 2150 | 1.2137 | |
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| 1.255 | 0.5026 | 2200 | 1.2111 | |
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| 1.1682 | 0.5141 | 2250 | 1.2076 | |
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| 1.1455 | 0.5255 | 2300 | 1.2052 | |
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| 1.151 | 0.5369 | 2350 | 1.2034 | |
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| 0.9805 | 0.5483 | 2400 | 1.2007 | |
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| 1.1706 | 0.5597 | 2450 | 1.1985 | |
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| 1.1961 | 0.5712 | 2500 | 1.1960 | |
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| 1.0449 | 0.5826 | 2550 | 1.1937 | |
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| 1.1375 | 0.5940 | 2600 | 1.1908 | |
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| 1.1205 | 0.6054 | 2650 | 1.1896 | |
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| 1.2097 | 0.6169 | 2700 | 1.1908 | |
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| 1.1976 | 0.6283 | 2750 | 1.1856 | |
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| 1.1327 | 0.6397 | 2800 | 1.0918 | |
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| 1.0446 | 0.6511 | 2850 | 1.0929 | |
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| 1.0804 | 0.6626 | 2900 | 1.0878 | |
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| 0.9446 | 0.6740 | 2950 | 1.0871 | |
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| 1.0722 | 0.6854 | 3000 | 1.0851 | |
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| 1.1224 | 0.6968 | 3050 | 1.0865 | |
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| 1.2711 | 0.7082 | 3100 | 1.0826 | |
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| 1.0378 | 0.7197 | 3150 | 1.0835 | |
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| 1.0873 | 0.7311 | 3200 | 1.0823 | |
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| 1.1336 | 0.7425 | 3250 | 1.0815 | |
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| 1.1407 | 0.7539 | 3300 | 1.0782 | |
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| 1.0805 | 0.7654 | 3350 | 1.0786 | |
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| 1.2204 | 0.7768 | 3400 | 1.0773 | |
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| 1.0855 | 0.7882 | 3450 | 1.1838 | |
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| 1.1151 | 0.7996 | 3500 | 1.1843 | |
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| 1.01 | 0.8111 | 3550 | 1.1815 | |
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| 1.1389 | 0.8225 | 3600 | 1.1828 | |
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| 1.0964 | 0.8339 | 3650 | 1.1802 | |
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| 0.9706 | 0.8453 | 3700 | 1.1803 | |
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| 1.0022 | 0.8568 | 3750 | 1.1764 | |
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| 1.0751 | 0.8682 | 3800 | 1.1764 | |
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| 0.9681 | 0.8796 | 3850 | 1.1764 | |
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| 1.101 | 0.8910 | 3900 | 1.1740 | |
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| 1.0931 | 0.9024 | 3950 | 1.1730 | |
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| 1.0791 | 0.9139 | 4000 | 1.1721 | |
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| 1.1654 | 0.9253 | 4050 | 1.1711 | |
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| 1.0536 | 0.9367 | 4100 | 1.1669 | |
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| 1.1077 | 0.9481 | 4150 | 1.1691 | |
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| 1.1421 | 0.9596 | 4200 | 1.1674 | |
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| 1.1065 | 0.9710 | 4250 | 1.1684 | |
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| 1.1226 | 0.9824 | 4300 | 1.1670 | |
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| 1.1432 | 0.9938 | 4350 | 1.1641 | |
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| 1.1632 | 1.0053 | 4400 | 1.1614 | |
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| 0.9927 | 1.0167 | 4450 | 1.1600 | |
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| 0.9685 | 1.0281 | 4500 | 1.1559 | |
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| 1.1403 | 1.0395 | 4550 | 1.1563 | |
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| 1.1059 | 1.0509 | 4600 | 1.1546 | |
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| 1.071 | 1.0624 | 4650 | 1.1544 | |
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| 1.0969 | 1.0738 | 4700 | 1.1537 | |
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| 1.0136 | 1.0852 | 4750 | 1.1521 | |
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| 1.0297 | 1.0966 | 4800 | 1.1519 | |
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| 1.1304 | 1.1081 | 4850 | 1.1508 | |
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| 1.2172 | 1.1195 | 4900 | 1.1517 | |
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| 1.0156 | 1.1309 | 4950 | 1.1511 | |
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| 1.0726 | 1.1423 | 5000 | 1.1483 | |
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| 1.0272 | 1.1538 | 5050 | 1.0159 | |
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| 1.1042 | 1.1652 | 5100 | 1.0153 | |
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| 1.0118 | 1.1766 | 5150 | 1.0127 | |
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| 1.1269 | 1.1880 | 5200 | 1.0148 | |
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| 1.0389 | 1.1995 | 5250 | 1.0152 | |
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| 1.1804 | 1.2109 | 5300 | 1.0154 | |
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| 1.1138 | 1.2223 | 5350 | 1.0153 | |
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| 1.0319 | 1.2337 | 5400 | 1.0144 | |
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| 1.0 | 1.2451 | 5450 | 1.0153 | |
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| 1.1573 | 1.2566 | 5500 | 1.0152 | |
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| 1.0604 | 1.2680 | 5550 | 1.0126 | |
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| 1.081 | 1.2794 | 5600 | 1.0118 | |
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| 0.988 | 1.2908 | 5650 | 1.0126 | |
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| 1.1302 | 1.3023 | 5700 | 1.0119 | |
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| 1.0626 | 1.3137 | 5750 | 1.0129 | |
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| 1.051 | 1.3251 | 5800 | 1.0100 | |
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| 1.0849 | 1.3365 | 5850 | 1.0094 | |
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| 1.0739 | 1.3480 | 5900 | 1.0090 | |
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| 1.0457 | 1.3594 | 5950 | 1.0074 | |
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| 1.0924 | 1.3708 | 6000 | 1.0090 | |
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| 0.9545 | 1.3822 | 6050 | 1.0084 | |
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| 1.0727 | 1.3936 | 6100 | 1.0076 | |
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| 1.1274 | 1.4051 | 6150 | 1.0075 | |
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| 1.0515 | 1.4165 | 6200 | 1.0066 | |
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| 0.9465 | 1.4279 | 6250 | 1.0057 | |
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| 1.029 | 1.4393 | 6300 | 1.0062 | |
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| 1.0454 | 1.4508 | 6350 | 1.0058 | |
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| 0.9563 | 1.4622 | 6400 | 1.0053 | |
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| 1.1052 | 1.4736 | 6450 | 1.0049 | |
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| 0.9351 | 1.4850 | 6500 | 1.0059 | |
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| 1.0649 | 1.4965 | 6550 | 1.0048 | |
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| 1.0206 | 1.5079 | 6600 | 1.0039 | |
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| 1.0616 | 1.5193 | 6650 | 1.0032 | |
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| 1.1544 | 1.5307 | 6700 | 1.0047 | |
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| 1.012 | 1.5422 | 6750 | 1.0199 | |
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| 1.0374 | 1.5536 | 6800 | 1.0177 | |
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| 1.1414 | 1.5650 | 6850 | 1.0174 | |
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| 0.8807 | 1.5764 | 6900 | 1.0177 | |
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| 1.0647 | 1.5878 | 6950 | 1.0156 | |
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| 1.023 | 1.5993 | 7000 | 1.0173 | |
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| 1.0109 | 1.6107 | 7050 | 1.0156 | |
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| 1.005 | 1.6221 | 7100 | 1.0163 | |
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| 1.0047 | 1.6335 | 7150 | 1.0163 | |
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| 1.0304 | 1.6450 | 7200 | 1.0158 | |
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| 0.9394 | 1.6564 | 7250 | 1.0158 | |
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| 1.0 | 1.6678 | 7300 | 1.0150 | |
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| 1.0296 | 1.6792 | 7350 | 1.0148 | |
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| 1.0314 | 1.6907 | 7400 | 1.0152 | |
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| 0.9902 | 1.7021 | 7450 | 1.0148 | |
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| 1.0266 | 1.7135 | 7500 | 1.0159 | |
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| 1.1017 | 1.7249 | 7550 | 1.0152 | |
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| 1.0706 | 1.7363 | 7600 | 1.0150 | |
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| 0.9999 | 1.7478 | 7650 | 1.0149 | |
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| 0.9819 | 1.7592 | 7700 | 1.0138 | |
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| 1.0049 | 1.7706 | 7750 | 1.0137 | |
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| 1.0488 | 1.7820 | 7800 | 1.0131 | |
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| 1.1126 | 1.7935 | 7850 | 1.0140 | |
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| 1.0583 | 1.8049 | 7900 | 1.0141 | |
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| 1.075 | 1.8163 | 7950 | 1.0126 | |
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| 1.1158 | 1.8277 | 8000 | 1.0117 | |
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| 1.0319 | 1.8392 | 8050 | 1.0128 | |
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| 1.0514 | 1.8506 | 8100 | 1.0128 | |
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| 1.1144 | 1.8620 | 8150 | 1.0119 | |
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| 0.983 | 1.8734 | 8200 | 1.0119 | |
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| 1.1242 | 1.8849 | 8250 | 1.0126 | |
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| 1.1011 | 1.8963 | 8300 | 1.0123 | |
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| 0.9533 | 1.9077 | 8350 | 1.0127 | |
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| 1.0661 | 1.9191 | 8400 | 1.0118 | |
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| 1.0133 | 1.9305 | 8450 | 1.0117 | |
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| 1.0856 | 1.9420 | 8500 | 1.0118 | |
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| 1.1292 | 1.9534 | 8550 | 1.0117 | |
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| 0.9881 | 1.9648 | 8600 | 1.0118 | |
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| 0.9716 | 1.9762 | 8650 | 1.0121 | |
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| 1.0925 | 1.9877 | 8700 | 1.0117 | |
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| 1.0235 | 1.9991 | 8750 | 1.0115 | |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.42.0.dev0 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.2 |
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- Tokenizers 0.19.1 |
