---
library_name: transformers
license: mit
base_model: microsoft/Phi-4-multimodal-instruct
tags:
- generated_from_trainer
model-index:
- name: Phi-4-multimodal-instruct-scorecard
  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. -->

# Phi-4-multimodal-instruct-scorecard

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.
It achieves the following results on the evaluation set:
- Loss: 0.1685

## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 7

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1656        | 0.0799 | 20   | 0.1471          |
| 0.1358        | 0.1598 | 40   | 0.1494          |
| 0.1476        | 0.2398 | 60   | 0.1589          |
| 0.1513        | 0.3197 | 80   | 0.1542          |
| 0.1498        | 0.3996 | 100  | 0.1567          |
| 0.1422        | 0.4795 | 120  | 0.1645          |
| 0.1457        | 0.5594 | 140  | 0.1625          |
| 0.1524        | 0.6394 | 160  | 0.1577          |
| 0.1471        | 0.7193 | 180  | 0.1539          |
| 0.1493        | 0.7992 | 200  | 0.1458          |
| 0.1399        | 0.8791 | 220  | 0.1544          |
| 0.1422        | 0.9590 | 240  | 0.1653          |
| 0.1342        | 1.0360 | 260  | 0.1562          |
| 0.1379        | 1.1159 | 280  | 0.1546          |
| 0.1435        | 1.1958 | 300  | 0.1437          |
| 0.1358        | 1.2757 | 320  | 0.1485          |
| 0.1397        | 1.3556 | 340  | 0.1493          |
| 0.1358        | 1.4356 | 360  | 0.1553          |
| 0.1397        | 1.5155 | 380  | 0.1478          |
| 0.1358        | 1.5954 | 400  | 0.1431          |
| 0.1324        | 1.6753 | 420  | 0.1428          |
| 0.1335        | 1.7552 | 440  | 0.1384          |
| 0.1339        | 1.8352 | 460  | 0.1416          |
| 0.1301        | 1.9151 | 480  | 0.1551          |
| 0.1332        | 1.9950 | 500  | 0.1368          |
| 0.1214        | 2.0719 | 520  | 0.1420          |
| 0.1291        | 2.1518 | 540  | 0.1371          |
| 0.1281        | 2.2318 | 560  | 0.1397          |
| 0.1299        | 2.3117 | 580  | 0.1346          |
| 0.129         | 2.3916 | 600  | 0.1410          |
| 0.1282        | 2.4715 | 620  | 0.1399          |
| 0.1331        | 2.5514 | 640  | 0.1354          |
| 0.1289        | 2.6314 | 660  | 0.1374          |
| 0.1317        | 2.7113 | 680  | 0.1347          |
| 0.1283        | 2.7912 | 700  | 0.1373          |
| 0.1279        | 2.8711 | 720  | 0.1341          |
| 0.1277        | 2.9510 | 740  | 0.1355          |
| 0.1235        | 3.0280 | 760  | 0.1421          |
| 0.1255        | 3.1079 | 780  | 0.1338          |
| 0.1222        | 3.1878 | 800  | 0.1330          |
| 0.123         | 3.2677 | 820  | 0.1349          |
| 0.1252        | 3.3477 | 840  | 0.1324          |
| 0.1236        | 3.4276 | 860  | 0.1336          |
| 0.1233        | 3.5075 | 880  | 0.1328          |
| 0.1257        | 3.5874 | 900  | 0.1339          |
| 0.125         | 3.6673 | 920  | 0.1330          |
| 0.1233        | 3.7473 | 940  | 0.1351          |
| 0.1244        | 3.8272 | 960  | 0.1323          |
| 0.1235        | 3.9071 | 980  | 0.1318          |
| 0.1241        | 3.9870 | 1000 | 0.1306          |
| 0.1149        | 4.0639 | 1020 | 0.1322          |
| 0.1205        | 4.1439 | 1040 | 0.1366          |
| 0.119         | 4.2238 | 1060 | 0.1339          |
| 0.1182        | 4.3037 | 1080 | 0.1346          |
| 0.1195        | 4.3836 | 1100 | 0.1338          |
| 0.1196        | 4.4635 | 1120 | 0.1330          |
| 0.1186        | 4.5435 | 1140 | 0.1330          |
| 0.1174        | 4.6234 | 1160 | 0.1337          |
| 0.1167        | 4.7033 | 1180 | 0.1318          |
| 0.1228        | 4.7832 | 1200 | 0.1311          |
| 0.1183        | 4.8631 | 1220 | 0.1313          |
| 0.1202        | 4.9431 | 1240 | 0.1322          |
| 0.1106        | 5.0200 | 1260 | 0.1341          |
| 0.1125        | 5.0999 | 1280 | 0.1407          |
| 0.1137        | 5.1798 | 1300 | 0.1393          |
| 0.1107        | 5.2597 | 1320 | 0.1376          |
| 0.1087        | 5.3397 | 1340 | 0.1384          |
| 0.1092        | 5.4196 | 1360 | 0.1395          |
| 0.1086        | 5.4995 | 1380 | 0.1397          |
| 0.1088        | 5.5794 | 1400 | 0.1423          |
| 0.1088        | 5.6593 | 1420 | 0.1415          |
| 0.1069        | 5.7393 | 1440 | 0.1410          |
| 0.1091        | 5.8192 | 1460 | 0.1408          |
| 0.1096        | 5.8991 | 1480 | 0.1410          |
| 0.1066        | 5.9790 | 1500 | 0.1415          |
| 0.0936        | 6.0559 | 1520 | 0.1541          |
| 0.0881        | 6.1359 | 1540 | 0.1651          |
| 0.0895        | 6.2158 | 1560 | 0.1626          |
| 0.0891        | 6.2957 | 1580 | 0.1666          |
| 0.0869        | 6.3756 | 1600 | 0.1667          |
| 0.0892        | 6.4555 | 1620 | 0.1663          |
| 0.0863        | 6.5355 | 1640 | 0.1662          |
| 0.0865        | 6.6154 | 1660 | 0.1683          |
| 0.087         | 6.6953 | 1680 | 0.1686          |
| 0.084         | 6.7752 | 1700 | 0.1686          |
| 0.087         | 6.8551 | 1720 | 0.1684          |
| 0.0849        | 6.9351 | 1740 | 0.1685          |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1