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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/Phi-4-multimodal-instruct |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: Phi-4-multimodal-instruct-scorecard |
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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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# Phi-4-multimodal-instruct-scorecard |
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This model is a fine-tuned version of [microsoft/Phi-4-multimodal-instruct](https://huggingface.co/microsoft/Phi-4-multimodal-instruct) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1685 |
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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: 0.0002 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 7 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:------:|:----:|:---------------:| |
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| 0.1656 | 0.0799 | 20 | 0.1471 | |
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| 0.1358 | 0.1598 | 40 | 0.1494 | |
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| 0.1476 | 0.2398 | 60 | 0.1589 | |
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| 0.1513 | 0.3197 | 80 | 0.1542 | |
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| 0.1498 | 0.3996 | 100 | 0.1567 | |
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| 0.1422 | 0.4795 | 120 | 0.1645 | |
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| 0.1457 | 0.5594 | 140 | 0.1625 | |
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| 0.1524 | 0.6394 | 160 | 0.1577 | |
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| 0.1471 | 0.7193 | 180 | 0.1539 | |
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| 0.1493 | 0.7992 | 200 | 0.1458 | |
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| 0.1399 | 0.8791 | 220 | 0.1544 | |
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| 0.1422 | 0.9590 | 240 | 0.1653 | |
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| 0.1342 | 1.0360 | 260 | 0.1562 | |
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| 0.1379 | 1.1159 | 280 | 0.1546 | |
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| 0.1435 | 1.1958 | 300 | 0.1437 | |
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| 0.1358 | 1.2757 | 320 | 0.1485 | |
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| 0.1397 | 1.3556 | 340 | 0.1493 | |
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| 0.1358 | 1.4356 | 360 | 0.1553 | |
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| 0.1397 | 1.5155 | 380 | 0.1478 | |
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| 0.1358 | 1.5954 | 400 | 0.1431 | |
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| 0.1324 | 1.6753 | 420 | 0.1428 | |
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| 0.1335 | 1.7552 | 440 | 0.1384 | |
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| 0.1339 | 1.8352 | 460 | 0.1416 | |
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| 0.1301 | 1.9151 | 480 | 0.1551 | |
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| 0.1332 | 1.9950 | 500 | 0.1368 | |
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| 0.1214 | 2.0719 | 520 | 0.1420 | |
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| 0.1291 | 2.1518 | 540 | 0.1371 | |
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| 0.1281 | 2.2318 | 560 | 0.1397 | |
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| 0.1299 | 2.3117 | 580 | 0.1346 | |
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| 0.129 | 2.3916 | 600 | 0.1410 | |
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| 0.1282 | 2.4715 | 620 | 0.1399 | |
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| 0.1331 | 2.5514 | 640 | 0.1354 | |
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| 0.1289 | 2.6314 | 660 | 0.1374 | |
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| 0.1317 | 2.7113 | 680 | 0.1347 | |
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| 0.1283 | 2.7912 | 700 | 0.1373 | |
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| 0.1279 | 2.8711 | 720 | 0.1341 | |
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| 0.1277 | 2.9510 | 740 | 0.1355 | |
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| 0.1235 | 3.0280 | 760 | 0.1421 | |
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| 0.1255 | 3.1079 | 780 | 0.1338 | |
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| 0.1222 | 3.1878 | 800 | 0.1330 | |
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| 0.123 | 3.2677 | 820 | 0.1349 | |
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| 0.1252 | 3.3477 | 840 | 0.1324 | |
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| 0.1236 | 3.4276 | 860 | 0.1336 | |
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| 0.1233 | 3.5075 | 880 | 0.1328 | |
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| 0.1257 | 3.5874 | 900 | 0.1339 | |
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| 0.125 | 3.6673 | 920 | 0.1330 | |
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| 0.1233 | 3.7473 | 940 | 0.1351 | |
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| 0.1244 | 3.8272 | 960 | 0.1323 | |
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| 0.1235 | 3.9071 | 980 | 0.1318 | |
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| 0.1241 | 3.9870 | 1000 | 0.1306 | |
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| 0.1149 | 4.0639 | 1020 | 0.1322 | |
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| 0.1205 | 4.1439 | 1040 | 0.1366 | |
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| 0.119 | 4.2238 | 1060 | 0.1339 | |
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| 0.1182 | 4.3037 | 1080 | 0.1346 | |
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| 0.1195 | 4.3836 | 1100 | 0.1338 | |
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| 0.1196 | 4.4635 | 1120 | 0.1330 | |
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| 0.1186 | 4.5435 | 1140 | 0.1330 | |
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| 0.1174 | 4.6234 | 1160 | 0.1337 | |
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| 0.1167 | 4.7033 | 1180 | 0.1318 | |
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| 0.1228 | 4.7832 | 1200 | 0.1311 | |
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| 0.1183 | 4.8631 | 1220 | 0.1313 | |
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| 0.1202 | 4.9431 | 1240 | 0.1322 | |
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| 0.1106 | 5.0200 | 1260 | 0.1341 | |
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| 0.1125 | 5.0999 | 1280 | 0.1407 | |
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| 0.1137 | 5.1798 | 1300 | 0.1393 | |
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| 0.1107 | 5.2597 | 1320 | 0.1376 | |
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| 0.1087 | 5.3397 | 1340 | 0.1384 | |
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| 0.1092 | 5.4196 | 1360 | 0.1395 | |
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| 0.1086 | 5.4995 | 1380 | 0.1397 | |
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| 0.1088 | 5.5794 | 1400 | 0.1423 | |
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| 0.1088 | 5.6593 | 1420 | 0.1415 | |
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| 0.1069 | 5.7393 | 1440 | 0.1410 | |
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| 0.1091 | 5.8192 | 1460 | 0.1408 | |
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| 0.1096 | 5.8991 | 1480 | 0.1410 | |
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| 0.1066 | 5.9790 | 1500 | 0.1415 | |
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| 0.0936 | 6.0559 | 1520 | 0.1541 | |
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| 0.0881 | 6.1359 | 1540 | 0.1651 | |
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| 0.0895 | 6.2158 | 1560 | 0.1626 | |
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| 0.0891 | 6.2957 | 1580 | 0.1666 | |
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| 0.0869 | 6.3756 | 1600 | 0.1667 | |
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| 0.0892 | 6.4555 | 1620 | 0.1663 | |
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| 0.0863 | 6.5355 | 1640 | 0.1662 | |
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| 0.0865 | 6.6154 | 1660 | 0.1683 | |
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| 0.087 | 6.6953 | 1680 | 0.1686 | |
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| 0.084 | 6.7752 | 1700 | 0.1686 | |
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| 0.087 | 6.8551 | 1720 | 0.1684 | |
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| 0.0849 | 6.9351 | 1740 | 0.1685 | |
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### Framework versions |
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- Transformers 4.48.2 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.1 |
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- Tokenizers 0.21.1 |
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