File size: 81,438 Bytes
b48dae5 9f42af5 b48dae5 4cd1983 b48dae5 4cd1983 b48dae5 9f42af5 b48dae5 aaabe6a b48dae5 9f42af5 aaabe6a 9f42af5 b48dae5 4cd1983 b48dae5 4cd1983 b48dae5 3095d1a b48dae5 9f42af5 30d1131 9f42af5 b48dae5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 |
---
license: apache-2.0
pipeline_tag: image-text-to-text
library_name: transformers
base_model:
- OpenGVLab/InternViT-300M-448px-V2_5
- openai/gpt-oss-20b
base_model_relation: merge
datasets:
- OpenGVLab/MMPR-v1.2
- OpenGVLab/MMPR-Tiny
language:
- multilingual
tags:
- internvl
- custom_code
---
# InternVL3_5-GPT-OSS-20B-A4B-Preview
[\[📂 GitHub\]](https://github.com/OpenGVLab/InternVL) [\[📜 InternVL 1.0\]](https://huggingface.co/papers/2312.14238) [\[📜 InternVL 1.5\]](https://huggingface.co/papers/2404.16821) [\[📜 InternVL 2.5\]](https://huggingface.co/papers/2412.05271) [\[📜 InternVL2.5-MPO\]](https://huggingface.co/papers/2411.10442) [\[📜 InternVL3\]](https://huggingface.co/papers/2504.10479) [\[📜 InternVL3.5\]](https://huggingface.co/papers/2508.18265)
[\[🆕 Blog\]](https://internvl.github.io/blog/) [\[🗨️ Chat Demo\]](https://chat.intern-ai.org.cn/) [\[🚀 Quick Start\]](#quick-start) [\[📖 Documents\]](https://internvl.readthedocs.io/en/latest/)
<div align="center">
<img width="500" alt="image" src="https://cdn-uploads.huggingface.co/production/uploads/64006c09330a45b03605bba3/zJsd2hqd3EevgXo6fNgC-.png">
</div>
## Introduction
We introduce *InternVL3.5*, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. A key innovation is the *Cascade Reinforcement Learning (Cascade RL)* framework, which enhances reasoning through a two-stage process: offline RL for stable convergence and online RL for refined alignment. This coarse-to-fine training strategy leads to substantial improvements on downstream reasoning tasks, e.g., MMMU and MathVista. To optimize efficiency, we propose a *Visual Resolution Router (ViR)* that dynamically adjusts the resolution of visual tokens without compromising performance. Coupled with ViR, our Decoupled *Vision-Language Deployment (DvD)* strategy separates the vision encoder and language model across different GPUs, effectively balancing computational load. These contributions collectively enable InternVL3.5 to achieve up to a +16.0\% gain in overall reasoning performance and a 4.05 \\(\times\\) inference speedup compared to its predecessor, i.e., InternVL3. In addition, InternVL3.5 supports novel capabilities such as GUI interaction and embodied agency. Notably, our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks—narrowing the performance gap with leading commercial models like GPT-5. All models and code are publicly released.
> Hatched bars represent closed-source commercial models. We report average scores on a set of multimodal general, reasoning, text, and agentic benchmarks: MMBench v1.1 (en), MMStar,BLINK, HallusionBench, AI2D, OCRBench, MMVet, MME-RealWorld (en), MVBench, VideoMME, MMMU, MathVista, MathVision, MathVerse, DynaMath, WeMath, LogicVista, MATH500, AIME24, AIME25, GPQA, MMLU-Pro, GAOKAO, IFEval, SGP-Bench, VSI-Bench, ERQA, SpaCE-10, and OmniSpatial.
See [quick start](#quick-start) for how to use our model.
## InternVL3.5 Family
In the following table, we provide an overview of the InternVL3.5 series.
To maintain consistency with earlier generations, we provide two model formats: [the GitHub format](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B), consistent with prior releases, and [the HF format](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-HF), aligned with the official Transformers standard.
> If you want to convert the checkpoint between these two formats, please refer to the scripts about [custom2hf](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/tools/internvl_custom2hf.py) and [hf2custom](https://github.com/OpenGVLab/InternVL/blob/main/internvl_chat/tools/internvl_hf2custom.py).
### Github Format
| Model | #Vision Param | #Language Param | #Total Param | HF Link | ModelScope Link |
| --------------------- | ------------- | --------------- | ------------ | ------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------- |
| InternVL3.5-1B | 0.3B | 0.8B | 1.1B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B) |
| InternVL3.5-2B | 0.3B | 2.0B | 2.3B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B) |
| InternVL3.5-4B | 0.3B | 4.4B | 4.7B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B) |
| InternVL3.5-8B | 0.3B | 8.2B | 8.5B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B) |
| InternVL3.5-14B | 0.3B | 14.8B | 15.1B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B) |
| InternVL3.5-38B | 5.5B | 32.8B | 38.4B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B) |
| InternVL3.5-20B-A4B | 0.3B | 20.9B | 21.2B-A4B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview) |
| InternVL3.5-30B-A3B | 0.3B | 30.5B | 30.8B-A3B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B) |
| InternVL3.5-241B-A28B | 5.5B | 235.1B | 240.7B-A28B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B) |
### HuggingFace Format
| Model | #Vision Param | #Language Param | #Total Param | HF Link | ModelScope Link |
| ------------------------ | ------------- | --------------- | ------------ | --------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- |
| InternVL3.5-1B-HF | 0.3B | 0.8B | 1.1B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B-HF) |
| InternVL3.5-2B-HF | 0.3B | 2.0B | 2.3B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B-HF) |
| InternVL3.5-4B-HF | 0.3B | 4.4B | 4.7B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B-HF) |
| InternVL3.5-8B-HF | 0.3B | 8.2B | 8.5B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B-HF) |
| InternVL3.5-14B-HF | 0.3B | 14.8B | 15.1B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B-HF) |
| InternVL3.5-38B-HF | 5.5B | 32.8B | 38.4B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B-HF) |
| InternVL3.5-20B-A4B-HF | 0.3B | 20.9B | 21.2B-A4B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF) |
| InternVL3.5-30B-A3B-HF | 0.3B | 30.5B | 30.8B-A3B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B-HF) |
| InternVL3.5-241B-A28B-HF | 5.5B | 235.1B | 240.7B-A28B | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-HF) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B-HF) |
> We conduct the evaluation with [VLMEvalkit](https://github.com/open-compass/VLMEvalKit). ***To enable the Thinking mode of our model, please set the system prompt to [R1_SYSTEM_PROMPT](https://github.com/open-compass/VLMEvalKit/blob/main/vlmeval/vlm/internvl/internvl_chat.py#L38).*** When enabling Thinking mode, we recommend setting `do_sample=True` and `temperature=0.6` to mitigate undesired repetition.
Our training pipeline comprises four stages: Multimodal Continual Pre-Training (**CPT**), Supervised Fine-Tuning (**SFT**), and Cascade Reinforcement Learning (**CascadeRL**). In CascadeRL, we first fine-tune the model using Mixed Preference Optimization (**MPO**) under an offline RL setting, followed by **GSPO** under an oneline RL setting.
For the Flash version of InternVL3.5, we additionally introduce a lightweight training stage, termed Visual Consistency Learning (**ViCO**), which reduces the token cost required to represent an image patch.
Here, we also open-source the model weights after different training stages for potential research usage.
***If you're unsure which version to use, please select the one without any suffix, as it has completed the full training pipeline.***
| Model | Training Pipeline | HF Link | ModelScope Link |
| -------------------------------- | --------------------- | --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- |
| InternVL3.5-1B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B-Pretrained) |
| InternVL3.5-1B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B-Instruct) |
| InternVL3.5-1B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B-MPO) |
| InternVL3.5-1B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-1B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-1B) |
| InternVL3.5-2B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B-Pretrained) |
| InternVL3.5-2B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B-Instruct) |
| InternVL3.5-2B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B-MPO) |
| InternVL3.5-2B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-2B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-2B) |
| InternVL3.5-4B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B-Pretrained) |
| InternVL3.5-4B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B-Instruct) |
| InternVL3.5-4B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B-MPO) |
| InternVL3.5-4B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-4B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-4B) |
| InternVL3.5-8B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B-Pretrained) |
| InternVL3.5-8B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B-Instruct) |
| InternVL3.5-8B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B-MPO) |
| InternVL3.5-8B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-8B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-8B) |
| InternVL3.5-14B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B-Pretrained) |
| InternVL3.5-14B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B-Instruct) |
| InternVL3.5-14B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B-MPO) |
| InternVL3.5-14B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-14B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-14B) |
| InternVL3.5-30B-A3B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B-Pretrained) |
| InternVL3.5-30B-A3B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B-Instruct) |
| InternVL3.5-30B-A3B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B-MPO) |
| InternVL3.5-30B-A3B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-30B-A3B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-30B-A3B) |
| InternVL3.5-38B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B-Pretrained) |
| InternVL3.5-38B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B-Instruct) |
| InternVL3.5-38B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B-MPO) |
| InternVL3.5-38B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-38B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-38B) |
| InternVL3.5-241B-A28B-Pretrained | CPT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Pretrained) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B-Pretrained) |
| InternVL3.5-241B-A28B-Instruct | CPT + SFT | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-Instruct) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B-Instruct) |
| InternVL3.5-241B-A28B-MPO | CPT + SFT + MPO | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B-MPO) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B-MPO) |
| InternVL3.5-241B-A28B | CPT + SFT + CascadeRL | [🤗 link](https://huggingface.co/OpenGVLab/InternVL3_5-241B-A28B) | [🤖 link](https://www.modelscope.cn/models/OpenGVLab/InternVL3_5-241B-A28B) |
The Flash version of our model will be released as soon as possible.
## Model Architecture
`InternVL3.5`:
This series of models follow the "ViT–MLP–LLM" paradigm adopted in previous versions of InternVL.
We initialize the language model using the Qwen3 series and GPT-OSS, and the vision encoder using InternViT-300M and InternViT-6B.
The Dynamic High Resolution strategy introduced in InternVL1.5 is also retained in our design.
`InternVL3.5-Flash`:
Compared to InternVL3.5, InternVL3.5-Flash further integrates the *Visual Resolution Router (ViR)*, thus yielding a series of efficient variants friendly suitable for resource-constrained scenarios.
Specifically, in InternVL3.5, each image patch is initially represented as 1024 visual tokens for the vision encoder, which are then compressed into 256 tokens via a pixel shuffle module before being passed to the Large Language Model (LLM).
In InternVL3.5-Flash, as shown in the Figure below, an additional pixel shuffle module with a higher compression rate is included, enabling the compression of visual tokens down to 64 tokens.
For each patch, the patch router determines the appropriate compression rate by assessing its semantic richness, and routes it to the corresponding pixel shuffle module accordingly.
Benefiting from this patch-aware compression mechanism, InternVL3.5-Flash is able to reduce the number of visual tokens by 50\% while maintaining nearly 100\% of the performance of InternVL3.5.
## Training and Deployment Strategy
### Pre-Training
During the pre-training stage, we update all model parameters jointly using the combination of large-scale text and multimodal corpora. Specifically, given an arbitrary training sample consisting of a multimodal token sequence \\(\mathbf{x}=\left(x_1, x_2, \ldots, x_L\right)\\), the next token prediction (NTP) loss is calculated on each text token as follows:
$$
\mathcal{L}_{i}=-\log p_\theta\left(x_i \mid x_1, \ldots, x_{i-1}\right),
$$
where \\(x_i\\) is the predicted token and prefix tokens in \\(\{x_1, x_2, \ldots, x_{i-1}\}\\) can be either text tokens or image tokens. Notably, for conversation samples, only response tokens are included for the calculation of the loss.
Additionally, to mitigate bias toward either longer or shorter responses during training, we adopt the square averaging to re-weight the NTP loss as follows:
$$
\mathcal{L}_{i}^{'} = \frac{w_i}{\sum_j w_j} \cdot \mathcal{L}_i, \quad w_i = \frac{1}{N^{0.5}},
$$
where \\(N\\) denotes the number of tokens in the training sample on which the loss needs to be calculated. The random JPEG compression is also included to enhance the model's real-world performance.
### Supervised Fine-Tuning
During the SFT phase, we adopt the same objective as in the pre-training stage and use the square-root averaging strategy to calculate the final loss. In this stage, the context window is set to 32K tokens to adapt long-context information.
Compared to InternVL3, the SFT stage of InternVL3.5 contains more high-quality and diverse training data derived from three sources:
(1) Instruction-following data from InternVL3, which are reused to preserve broad coverage of vision–language tasks.
(2) Multimodal reasoning data in the "Thinking" mode, which are included to instill long-thinking capabilities in the model. To construct such data, we first use InternVL3-78B to describe the image and then input the description into DeepSeek-R1 to sample rollouts with detailed reasoning processes. Rollouts with an incorrect final answer are filtered out. The questions in these datasets cover various expert domains, such as mathematics and scientific disciplines, thereby strengthening performance on different reasoning tasks.
(3) Capability-expansion datasets, which endow InternVL3.5 with new skills, including GUI-based interaction, embodied interaction, and scalable vect
### Cascade Reinforcement Learning
Cascade RL aims to combine the benefits of offline RL and online RL to progressively facilitate the post-training of MLLMs in an efficient manner.
Specifically, we first fine-tune the model using an offline RL algorithm as an efficient warm-up stage to reach a satisfied results, which can guarantee the high-quality rollouts for the latter stage.
Subsequently, we employ an online RL algorithm to further refine the output distribution based on rollouts generated by the model itself. Compared to the single offline or online RL stage, our cascaded RL achieves significant performance improvements at a fraction of the GPU time cost.
During the offline RL stage, we employ mixed preference optimization (MPO) to fine-tune the model. Specifically, the training objective of MPO is a combination of preference loss \\(\mathcal{L}_{p}\\), quality loss \\(\mathcal{L}_{q}\\), and generation loss \\(\mathcal{L}_{g}\\), which can be formulated as follows:
$$
\mathcal{L}_{\text{MPO}}=
w_{p} \mathcal{L}_{p}
+
w_{q} \mathcal{L}_{q}
+
w_{g} \mathcal{L}_{g}
,
$$
where \\(w_{*}\\) represents the weight assigned to each loss component.
The DPO loss, BCO loss, and LM loss serve as the preference loss, quality loss, and generation loss, respectively.
During the online RL stage, we employ GSPO, without reference model constraints, as our online RL algorithm, which we find more effective in training both dense and mixture-of-experts (MoE) models. Similar to GRPO, the advantage is defined as the normalized reward across responses sampled from the same query.
The training objective of GSPO is given by:
$$
\mathcal{L}_{\mathrm{GSPO}}(\theta)=\mathbb{E}_{x \sim \mathcal{D},\left\{y_i\right\}_{i=1}^G \sim \pi_{\theta \text { old }}(\cdot \mid x)}\left[\frac{1}{G} \sum_{i=1}^G \min \left(s_i(\theta) \widehat{A}_i, \operatorname{clip}\left(s_i(\theta), 1-\varepsilon, 1+\varepsilon\right) \widehat{A}_i\right)\right],
$$
where the importance sampling ratio is defined as the geometric mean of the per-token ratios.
> Please see [our paper](https://huggingface.co/papers/2508.18265) for more technical and experimental details.
### Visual Consistency Learning
We further include ViCO as an additional training stage to integrate the *visual resolution router (ViR)* into InternVL3.5, thereby reducing the inference cost of InternVL3.5. The obtained efficient version of InternVL3.5 are termed as *InternVL3.5-Flash*. In particular, ViCO comprises two stages:
`Consistency training`:
In this stage, the entire model is trained to minimize the divergence between response distributions conditioned on visual tokens with different compression rates.
In practice, we introduce an extra reference model, which is frozen and initialized with InternVL3.5.
Given a sample, each image patch is represented as either 256 or 64 tokens, and the training objective is defined as follows:
$$
\mathcal{L}_\text{ViCO} =
\mathbb{E}_{\xi \sim \mathcal{R}} \Bigg[
\frac{1}{N} \sum_{i=1}^{N} \mathrm{KL} \Big(
\pi_{\theta_{ref}}\left(y_i \mid y_{<i}, I\right) \;\Big\|\;
\pi_{\theta_{policy}}\left(y_i \mid y_{<i}, I_\xi\right)
\Big)
\Bigg],
$$
where \\(\mathrm{KL}\) denotes the KL divergence and \(\xi\) denotes the compression rate, which is uniformly sampled from \(\{\frac{1}{4},\frac{1}{16}\}\). The image \(I_\xi\) is represented as 256 tokens when \(\xi=\frac{1}{4}\) and 64 tokens when \(\xi=\frac{1}{16}\). Notably, the reference model always performs inference with \(\xi=\frac{1}{4}\).
`Router training`:
This stage aims to train the ViR to select an appropriate trade-off resolution for different inputs.
ViR is formulated as a binary classifier and trained using standard cross-entropy loss.
To construct the route targets, we first compute the KL divergence between the model outputs conditioned on uncompressed visual tokens (i.e., 256 tokens per patch) and those conditioned on compressed visual tokens (i.e., 64 tokens per patch).
During this stage, the main MLLM (ViT, MLP and LLM) is kept frozen, and only the ViR is trained.
Specifically, we first compute the loss ratio for each patch:
$$
r_i = \frac{\mathcal{L}_\text{ViCO}\big(y_i \mid I_{\frac{1}{16}}\big)}{\mathcal{L}_\text{ViCO}\big(y_i \mid I_{\frac{1}{4}}\big)},
$$
which quantifies the relative increase in loss caused by compressing the visual tokens. Based on this ratio, the binary ground-truth label for the patch router is defined as:
$$
y_i^\text{router} =
\begin{cases}
0, & r_i < \tau \; \text{(compression has negligible impact)} \\
1, & r_i \ge \tau \; \text{(compression has significant impact)},
\end{cases}
$$
where \(y_i^{\text{router}}=0\) and \(y_i^{\text{router}}=1\) indicate that the compression rate \(\xi\) is set to \(\tfrac{1}{16}\) and \(\tfrac{1}{4}\), respectively.
> Please see [our paper](https://huggingface.co/papers/2508.18265) for more technical and experimental details.
### Test-Time Scaling
Test-time scaling (TTS) has been empirically demonstrated as an effective approach to enhance the reasoning capabilities of LLMs and MLLMs, particularly for complex tasks necessitating multi-step inference.
In this work, we implement a comprehensive test-time scaling approach that simultaneously improves reasoning depth (i.e., deep thinking) and breadth (i.e., parallel thinking).
`Deep Thinking`: By activating the Thinking mode, we guide the model to deliberately engage in step-by-step reasoning (i.e., decomposing complex problems into logical steps and validating intermediate conclusions) prior to generating the final answer. This approach systematically improves the logical structure of solutions for complex problems, particularly those requiring multi-step inference, and enhances reasoning depth.
`Parallel Thinking`: Following InternVL3, for reasoning tasks, we adopt the Best-of-N (BoN) strategy by employing [VisualPRM-v1.1](https://huggingface.co/OpenGVLab/VisualPRM-8B-v1_1) as the critic model to select the optimal response from multiple reasoning candidates.
This approach improves reasoning breadth.
> Notably, unless otherwise specified, the experimental results reported in our paper are obtained without applying TTS. Thus far, we have only applied TTS to reasoning benchmarks, since we found that the model already exhibits strong perception and understanding capabilities, and initiating TTS yields no significant improvement.
### Decoupled Vision-Language Deployment
In multimodal inference, the vision encoder and language model have distinct computational characteristics. The vision encoder that transforms images into semantic features is highly parallelizable and does not rely on long-term history state. In contrast, the language model adopts the inference in an autoregressive manner, which requires previous states to compute the next one. This sequential property makes the language part more sensitive to memory bandwidth and latency.
When MLLMs are deployed online at scale, the vision and language models often block each other, thus incurring additional inference cost. This effect becomes more pronounced with larger vision models or higher-resolution images.
As shown in the Figure above, we propose decoupled vision-language deployment (DvD) to address this issue by separating vision and language processing, with a particular focus on optimizing the prefilling stage. The vision subsystem batches and processes images to produce compact feature embeddings, which are then transmitted to the language subsystem for fusion with the text context prior to decoding. This separation alleviates blocking and brings multimodal prefilling performance closer to that of pure language models.
In our system implementation, the ViT and MLP (and ViR for InternVL3.5-Flash) are deployed on the vision server, while the language server executes only the LLM. The communication is unidirectional, transmitting BF16 visual features over TCP, with RDMA optionally employed to achieve higher transmission speed. Vision processing, feature transmission, and language processing are organized into an asynchronous three-stage pipeline, enabling overlapped execution and minimizing pipeline stalls.
DvD increases GPU utilization and processing efficiency on the vision side, while enabling the language server to focus exclusively on the LLM’s prefilling and decoding without being blocked by vision computation. This design leads to improved throughput and responsiveness. Moreover, the architecture supports independent hardware cost optimization for the vision and language modules, and facilitates the seamless integration of new modules without requiring modifications to the language server deployment.
## Evaluation on Multimodal Capability
### Multimodal Reasoning and Mathematics
### OCR, Chart, and Document Understanding
### Multi-Image Understanding & Real-World Comprehension
### Comprehensive Multimodal Understanding & Multimodal Hallucination Evaluation
### Visual Grounding
### Multimodal Multilingual Understanding
### Video Understanding
### GUI Tasks
### Embodied Tasks
### SVG Tasks
## Evaluation on Language Capability
## Ablation Study
### Cascade Reinforcement Learning
### Decoupled Vision-Language Deployment
## Quick Start
We provide an example code to run `InternVL3.5-8B` using `transformers`. Please note that our models with up to 30B parameters can be deployed on a single A100 GPU, while the 38B model requires two A100 GPUs and the 235B model requires eight A100 GPUs.
> In most cases, both [LMDeploy](https://github.com/InternLM/lmdeploy) and [vLLM](https://github.com/vllm-project/vllm) can be used for model deployment. However, for InternVL3.5-20B-A4B, we recommend using vLLM since lmdeploy has not yet supported GPT-OSS.
> Please use transformers>=4.52.1 to ensure the model works normally. For the 20B version of our model, transformers>=4.55.0 is required.
### Model Loading
#### 16-bit (bf16 / fp16)
```python
import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3_5-8B"
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True).eval().cuda()
```
#### BNB 8-bit Quantization
```python
import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3_5-8B"
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
load_in_8bit=True,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True).eval()
```
#### Multiple GPUs
```python
import math
import torch
from transformers import AutoTokenizer, AutoModel
path = "OpenGVLab/InternVL3_5-8B"
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True,
device_map="auto").eval()
```
### Thinking Mode
To enable thinking mode, please set the system prompt to our Thinking System Prompt. When enabling Thinking mode, we recommend setting `do_sample=True` and `temperature=0.6` to mitigate undesired repetition.
```python
R1_SYSTEM_PROMPT = """
You are an AI assistant that rigorously follows this response protocol:
1. First, conduct a detailed analysis of the question. Consider different angles, potential solutions, and reason through the problem step-by-step. Enclose this entire thinking process within <think> and </think> tags.
2. After the thinking section, provide a clear, concise, and direct answer to the user's question. Separate the answer from the think section with a newline.
Ensure that the thinking process is thorough but remains focused on the query. The final answer should be standalone and not reference the thinking section.
""".strip()
model.system_message = R1_SYSTEMP_PROMPT
```
### Inference with Transformers
```python
import math
import numpy as np
import torch
import torchvision.transforms as T
from decord import VideoReader, cpu
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
from transformers import AutoModel, AutoTokenizer
IMAGENET_MEAN = (0.485, 0.456, 0.406)
IMAGENET_STD = (0.229, 0.224, 0.225)
def build_transform(input_size):
MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
transform = T.Compose([
T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
T.ToTensor(),
T.Normalize(mean=MEAN, std=STD)
])
return transform
def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
best_ratio_diff = float('inf')
best_ratio = (1, 1)
area = width * height
for ratio in target_ratios:
target_aspect_ratio = ratio[0] / ratio[1]
ratio_diff = abs(aspect_ratio - target_aspect_ratio)
if ratio_diff < best_ratio_diff:
best_ratio_diff = ratio_diff
best_ratio = ratio
elif ratio_diff == best_ratio_diff:
if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
best_ratio = ratio
return best_ratio
def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
orig_width, orig_height = image.size
aspect_ratio = orig_width / orig_height
# calculate the existing image aspect ratio
target_ratios = set(
(i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
i * j <= max_num and i * j >= min_num)
target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
# find the closest aspect ratio to the target
target_aspect_ratio = find_closest_aspect_ratio(
aspect_ratio, target_ratios, orig_width, orig_height, image_size)
# calculate the target width and height
target_width = image_size * target_aspect_ratio[0]
target_height = image_size * target_aspect_ratio[1]
blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
# resize the image
resized_img = image.resize((target_width, target_height))
processed_images = []
for i in range(blocks):
box = (
(i % (target_width // image_size)) * image_size,
(i // (target_width // image_size)) * image_size,
((i % (target_width // image_size)) + 1) * image_size,
((i // (target_width // image_size)) + 1) * image_size
)
# split the image
split_img = resized_img.crop(box)
processed_images.append(split_img)
assert len(processed_images) == blocks
if use_thumbnail and len(processed_images) != 1:
thumbnail_img = image.resize((image_size, image_size))
processed_images.append(thumbnail_img)
return processed_images
def load_image(image_file, input_size=448, max_num=12):
image = Image.open(image_file).convert('RGB')
transform = build_transform(input_size=input_size)
images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(image) for image in images]
pixel_values = torch.stack(pixel_values)
return pixel_values
path = 'OpenGVLab/InternVL3_5-8B'
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
load_in_8bit=False,
low_cpu_mem_usage=True,
use_flash_attn=True,
trust_remote_code=True,
device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False)
# set the max number of tiles in `max_num`
pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
generation_config = dict(max_new_tokens=1024, do_sample=True)
# pure-text conversation (纯文本对话)
question = 'Hello, who are you?'
response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Can you tell me a story?'
response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# single-image single-round conversation (单图单轮对话)
question = '<image>\nPlease describe the image shortly.'
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(f'User: {question}\nAssistant: {response}')
# single-image multi-round conversation (单图多轮对话)
question = '<image>\nPlease describe the image in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Please write a poem according to the image.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, combined images (多图多轮对话，拼接图像)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
question = '<image>\nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# multi-image multi-round conversation, separate images (多图多轮对话，独立图像)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
question = 'Image-1: <image>\nImage-2: <image>\nDescribe the two images in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'What are the similarities and differences between these two images.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list,
history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
# batch inference, single image per sample (单图批处理)
pixel_values1 = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda()
pixel_values2 = load_image('./examples/image2.jpg', max_num=12).to(torch.bfloat16).cuda()
num_patches_list = [pixel_values1.size(0), pixel_values2.size(0)]
pixel_values = torch.cat((pixel_values1, pixel_values2), dim=0)
questions = ['<image>\nDescribe the image in detail.'] * len(num_patches_list)
responses = model.batch_chat(tokenizer, pixel_values,
num_patches_list=num_patches_list,
questions=questions,
generation_config=generation_config)
for question, response in zip(questions, responses):
print(f'User: {question}\nAssistant: {response}')
# video multi-round conversation (视频多轮对话)
def get_index(bound, fps, max_frame, first_idx=0, num_segments=32):
if bound:
start, end = bound[0], bound[1]
else:
start, end = -100000, 100000
start_idx = max(first_idx, round(start * fps))
end_idx = min(round(end * fps), max_frame)
seg_size = float(end_idx - start_idx) / num_segments
frame_indices = np.array([
int(start_idx + (seg_size / 2) + np.round(seg_size * idx))
for idx in range(num_segments)
])
return frame_indices
def load_video(video_path, bound=None, input_size=448, max_num=1, num_segments=32):
vr = VideoReader(video_path, ctx=cpu(0), num_threads=1)
max_frame = len(vr) - 1
fps = float(vr.get_avg_fps())
pixel_values_list, num_patches_list = [], []
transform = build_transform(input_size=input_size)
frame_indices = get_index(bound, fps, max_frame, first_idx=0, num_segments=num_segments)
for frame_index in frame_indices:
img = Image.fromarray(vr[frame_index].asnumpy()).convert('RGB')
img = dynamic_preprocess(img, image_size=input_size, use_thumbnail=True, max_num=max_num)
pixel_values = [transform(tile) for tile in img]
pixel_values = torch.stack(pixel_values)
num_patches_list.append(pixel_values.shape[0])
pixel_values_list.append(pixel_values)
pixel_values = torch.cat(pixel_values_list)
return pixel_values, num_patches_list
video_path = './examples/red-panda.mp4'
pixel_values, num_patches_list = load_video(video_path, num_segments=8, max_num=1)
pixel_values = pixel_values.to(torch.bfloat16).cuda()
video_prefix = ''.join([f'Frame{i+1}: <image>\n' for i in range(len(num_patches_list))])
question = video_prefix + 'What is the red panda doing?'
# Frame1: <image>\nFrame2: <image>\n...\nFrame8: <image>\n{question}
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list, history=None, return_history=True)
print(f'User: {question}\nAssistant: {response}')
question = 'Describe this video in detail.'
response, history = model.chat(tokenizer, pixel_values, question, generation_config,
num_patches_list=num_patches_list, history=history, return_history=True)
print(f'User: {question}\nAssistant: {response}')
```
#### Streaming Output
Besides this method, you can also use the following code to get streamed output.
```python
from transformers import TextIteratorStreamer
from threading import Thread
# Initialize the streamer
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True, timeout=10)
# Define the generation configuration
generation_config = dict(max_new_tokens=1024, do_sample=False, streamer=streamer)
# Start the model chat in a separate thread
thread = Thread(target=model.chat, kwargs=dict(
tokenizer=tokenizer, pixel_values=pixel_values, question=question,
history=None, return_history=False, generation_config=generation_config,
))
thread.start()
# Initialize an empty string to store the generated text
generated_text = ''
# Loop through the streamer to get the new text as it is generated
for new_text in streamer:
if new_text == model.conv_template.sep:
break
generated_text += new_text
print(new_text, end='', flush=True) # Print each new chunk of generated text on the same line
```
## Finetune
Many repositories now support fine-tuning of the InternVL series models, including [InternVL](https://github.com/OpenGVLab/InternVL), [SWIFT](https://github.com/modelscope/ms-swift), [XTuner](https://github.com/InternLM/xtuner), and others. Please refer to their documentation for more details on fine-tuning.
## Deployment
### vLLM
vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs and MLLMs.
Please refer to the [documentation](https://docs.vllm.ai/en/latest/examples/offline_inference/vision_language.html?h=internvl#vision-language) for how to deploy internvl series.
```sh
pip install vllm>=0.10.1
```
NOTE: Up to version 0.10.1.1, vLLM exhibits compatibility issues with GPT-OSS when applied in MLLMs. If you encounter any errors, please try replacing the `vllm/model_executor/models/gpt_oss.py` file with the following content:
```python
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Iterable
from typing import Optional
import torch
import torch.distributed as dist
from torch import nn
from transformers import GptOssConfig
from vllm.attention import Attention, AttentionType
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import (get_ep_group, get_tensor_model_parallel_rank, get_pp_group,
get_tensor_model_parallel_world_size)
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (QKVParallelLinear,
RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.vocab_parallel_embedding import (
ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors
from vllm.utils import cdiv
from .utils import (extract_layer_index,
make_empty_intermediate_tensors_factory,
maybe_prefix)
class OAIAttention(nn.Module):
def __init__(
self,
config: GptOssConfig,
quant_config: Optional[QuantizationConfig] = None,
cache_config: Optional[CacheConfig] = None,
prefix: str = "",
):
super().__init__()
self.layer_idx = extract_layer_index(prefix)
self.head_dim = config.head_dim
self.num_attention_heads = config.num_attention_heads
self.num_key_value_heads = config.num_key_value_heads
self.hidden_size = config.hidden_size
self.rotary_emb = get_rope(
self.head_dim,
rotary_dim=self.head_dim,
max_position=config.max_position_embeddings,
base=config.rope_theta,
dtype=torch.float32,
rope_scaling={
"rope_type":
"yarn",
"factor":
config.rope_scaling["factor"],
"original_max_position_embeddings":
config.rope_scaling["original_max_position_embeddings"],
"beta_fast":
config.rope_scaling["beta_fast"],
"beta_slow":
config.rope_scaling["beta_slow"],
},
is_neox_style=True,
)
tp_size = get_tensor_model_parallel_world_size()
self.sinks = torch.nn.Parameter(
torch.empty(config.num_attention_heads // tp_size,
dtype=torch.bfloat16,
requires_grad=False))
self.norm = RMSNorm(config.hidden_size, eps=1e-5)
self.q_size = self.num_attention_heads * self.head_dim // tp_size
self.kv_size = self.num_key_value_heads * self.head_dim // tp_size
self.scaling = self.head_dim**-0.5
self.rope_theta = config.rope_theta
self.qkv = QKVParallelLinear(
hidden_size=self.hidden_size,
head_size=self.head_dim,
total_num_heads=self.num_attention_heads,
total_num_kv_heads=self.num_key_value_heads,
quant_config=quant_config,
prefix=f"{prefix}.qkv_proj",
)
self.o_proj = RowParallelLinear(
input_size=self.num_attention_heads * self.head_dim,
output_size=self.hidden_size,
quant_config=quant_config,
prefix=f"{prefix}.o_proj",
)
self.num_local_attention_heads = config.num_attention_heads // tp_size
self.num_local_key_value_heads = config.num_key_value_heads // tp_size
# Only apply sliding window to every other layer
sliding_window = (config.sliding_window if self.layer_idx %
2 == 0 else None)
self.attn = Attention(
self.num_local_attention_heads,
self.head_dim,
self.scaling,
num_kv_heads=self.num_local_key_value_heads,
cache_config=cache_config,
quant_config=quant_config,
per_layer_sliding_window=sliding_window,
attn_type=AttentionType.DECODER,
prefix=f"{prefix}.attn",
sinks=self.sinks,
)
def forward(self, hidden_states: torch.Tensor,
positions: torch.Tensor) -> torch.Tensor:
t = self.norm(hidden_states)
qkv, _ = self.qkv(t)
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
q, k = self.rotary_emb(positions, q, k)
v = v.contiguous()
attn_output = self.attn(q, k, v)
output, _ = self.o_proj(attn_output)
return output + hidden_states
class MLPBlock(torch.nn.Module):
def __init__(
self,
config: GptOssConfig,
layer_idx: int,
quant_config: QuantizationConfig,
prefix: str = "",
):
super().__init__()
self.layer_idx = layer_idx
self.num_experts = config.num_local_experts
self.experts_per_token = config.num_experts_per_tok
# self.world_size = dist.get_world_size() if dist.is_initialized() else 1
self.norm = RMSNorm(config.hidden_size, eps=1e-5)
self.router = torch.nn.Linear(config.hidden_size,
config.num_local_experts,
dtype=torch.bfloat16)
# assert config.intermediate_size % self.world_size == 0
self.experts = FusedMoE(num_experts=config.num_local_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
intermediate_size=config.intermediate_size,
reduce_results=True,
renormalize=True,
quant_config=quant_config,
prefix=f"{prefix}.experts",
apply_router_weight_on_input=False,
has_bias=True,
activation="swigluoai")
def forward(self, x: torch.Tensor) -> torch.Tensor:
t = self.norm(x)
g = self.router(t)
t = self.experts(hidden_states=t, router_logits=g)
return x + t
class TransformerBlock(torch.nn.Module):
def __init__(
self,
config: GptOssConfig,
quant_config: QuantizationConfig,
prefix: str = "",
):
super().__init__()
self.layer_idx = extract_layer_index(prefix)
self.attn = OAIAttention(config, prefix=f"{prefix}.attn")
self.mlp = MLPBlock(config,
self.layer_idx,
quant_config=quant_config,
prefix=f"{prefix}.mlp")
def forward(self, hidden_states: torch.Tensor,
positions: torch.Tensor) -> torch.Tensor:
attn_output = self.attn(hidden_states, positions)
output = self.mlp(attn_output)
return output
@support_torch_compile
class GptOssModel(nn.Module):
def __init__(
self,
*,
vllm_config: VllmConfig,
prefix: str = "",
):
super().__init__()
self.config = vllm_config.model_config.hf_config
self.quant_config = vllm_config.quant_config
self.config.hidden_size = self.config.hidden_size
self.embedding = VocabParallelEmbedding(
self.config.vocab_size,
self.config.hidden_size,
)
self.layers = torch.nn.ModuleList([
TransformerBlock(
self.config,
quant_config=self.quant_config,
prefix=maybe_prefix(prefix, f"block.{layer_idx}"),
) for layer_idx in range(self.config.num_hidden_layers)
])
self.norm = RMSNorm(self.config.hidden_size, eps=1e-5)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(
["hidden_states", "residual"], self.config.hidden_size))
def forward(self, input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None,) -> torch.Tensor:
if get_pp_group().is_first_rank:
if inputs_embeds is not None:
hidden_states = inputs_embeds
else:
# hidden_states = self.get_input_embeddings(input_ids)
hidden_states = self.embedding(input_ids)
residual = None
else:
assert intermediate_tensors is not None
hidden_states = intermediate_tensors["hidden_states"]
residual = intermediate_tensors["residual"]
# x = self.embedding(input_ids)
# for layer in self.layers:
# x = layer(x, positions)
# x = self.norm(x)
for layer in self.layers:
hidden_states = layer(hidden_states, positions)
hidden_states = self.norm(hidden_states)
return hidden_states
class GptOssForCausalLM(nn.Module):
def __init__(
self,
vllm_config: VllmConfig,
prefix: str = "",
):
super().__init__()
self.vllm_config = vllm_config
self.model_config = vllm_config.model_config.hf_config
self.model = GptOssModel(
vllm_config=vllm_config,
prefix=maybe_prefix(prefix, "model"),
)
self.lm_head = ParallelLMHead(
self.model_config.vocab_size,
self.model_config.hidden_size,
)
self.logits_processor = LogitsProcessor(self.model_config.vocab_size)
self.make_empty_intermediate_tensors = (
self.model.make_empty_intermediate_tensors)
def forward(self,
input_ids: torch.Tensor,
positions: torch.Tensor,
intermediate_tensors: Optional[IntermediateTensors] = None,
inputs_embeds: Optional[torch.Tensor] = None) -> torch.Tensor:
assert intermediate_tensors is None
assert inputs_embeds is None
return self.model(input_ids, positions)
def compute_logits(self, hidden_states: torch.Tensor,
sampling_metadata: SamplingMetadata) -> torch.Tensor:
logits = self.logits_processor(self.lm_head, hidden_states,
sampling_metadata)
return logits
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.model.embedding(input_ids)
def _load_weights_mxfp4(
self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
rename_mapping = {
"self_attn": "attn",
"input_layernorm.weight": "attn.norm.weight",
"post_attention_layernorm.weight": "mlp.norm.weight",
"embed_tokens": "embedding",
}
def maybe_rename(name: str) -> str:
for remap_name, new_name in rename_mapping.items():
if remap_name in name:
return name.replace(remap_name, new_name)
return name
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
mxfp4_block = 32
tp_rank = get_tensor_model_parallel_rank()
tp_size = get_tensor_model_parallel_world_size()
intermediate_size = self.model_config.intermediate_size
intermediate_size_block = intermediate_size // mxfp4_block
per_rank_intermediate_size_block = cdiv(intermediate_size_block,
tp_size)
per_rank_intermediate_size = (per_rank_intermediate_size_block *
mxfp4_block)
# Calculate common slicing bounds for current rank
tp_rank_start = tp_rank * per_rank_intermediate_size
tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size,
intermediate_size)
# Attention heads per rank
heads_per_rank = self.model_config.num_attention_heads // tp_size
head_start = tp_rank * heads_per_rank
use_ep = self.vllm_config.parallel_config.enable_expert_parallel
ep_size = get_ep_group().world_size
ep_rank = get_ep_group().rank
num_experts = self.model_config.num_local_experts
experts_per_rank = num_experts // ep_size
ep_rank_start = ep_rank * experts_per_rank
ep_rank_end = (ep_rank + 1) * experts_per_rank
for name, weight in weights:
# FIXME(woosuk): Remove this after testing.
weight = weight.cuda()
if "gate_up_proj_blocks" in name:
# Handle MLP gate and up projection weights
new_name = name.replace("gate_up_proj_blocks", "w13_weight")
# flat weight from (E, 2 * N, block_size, entry_per_block)
# to (E, 2 * N, -1), shouldn't trigger copy for contiguous
weight = weight.view(num_experts, 2 * intermediate_size,
-1).contiguous()
# Extract gate and up projection parts
# since the weight is shuffled, we can slice directly
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:,
2 * tp_rank_start:2 * tp_rank_end,
...]
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
narrow_weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "down_proj_blocks" in name:
# Handle MLP down projection weights
new_name = name.replace("down_proj_blocks", "w2_weight")
# same flatten here, but since 2 mx4 value are packed in 1
# uint8, divide by 2
weight = weight.view(num_experts, -1,
intermediate_size // 2).contiguous()
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[...,
tp_rank_start // 2:tp_rank_end // 2]
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
narrow_weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "gate_up_proj_scales" in name:
# Handle MLP gate and up projection weights scale
new_name = name.replace("gate_up_proj_scales",
"w13_weight_scale")
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:,
2 * tp_rank_start:2 * tp_rank_end,
...]
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
narrow_weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "down_proj_scales" in name:
# Handle MLP down projection weights
new_name = name.replace("down_proj_scales", "w2_weight_scale")
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[..., tp_rank_start //
mxfp4_block:tp_rank_end //
mxfp4_block]
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
narrow_weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "gate_up_proj_bias" in name:
# Handle MLP gate and up projection biases
new_name = name.replace("gate_up_proj_bias", "w13_bias")
# Extract gate and up projection bias parts
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:,
2 * tp_rank_start:2 * tp_rank_end]
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param,
narrow_weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "down_proj_bias" in name:
# Handle MLP down projection bias
new_name = name.replace("down_proj_bias", "w2_bias")
param = params_dict[new_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
if use_ep:
weight = weight[ep_rank_start:ep_rank_end, ...]
else:
# (only load on rank 0 to avoid duplication)
if tp_rank != 0:
weight.zero_()
weight_loader(param,
weight,
weight_name=new_name,
shard_id=None,
expert_id=None)
loaded_params.add(new_name)
elif "sinks" in name:
# Handle attention sinks (distributed across ranks)
name = name.replace("self_attn", "attn")
param = params_dict[name]
narrow_weight = weight.narrow(0, head_start, heads_per_rank)
param.data.copy_(narrow_weight)
loaded_params.add(name)
elif "q_proj" in name or "k_proj" in name or "v_proj" in name:
shard_id = ("q" if "q_proj" in name else
"k" if "k_proj" in name else "v")
name = name.replace("self_attn", "attn")
param_name = name.replace(f"{shard_id}_proj", "qkv")
param = params_dict[param_name]
weight_loader = param.weight_loader
weight_loader(param, weight, loaded_shard_id=shard_id)
loaded_params.add(param_name)
else:
# Handle all other weights with potential renaming
renamed_name = maybe_rename(name)
if renamed_name not in params_dict:
continue
param = params_dict[renamed_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, weight)
loaded_params.add(renamed_name)
return loaded_params
def _load_weights_other(
self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
rename_mapping = {
"self_attn": "attn",
"input_layernorm.weight": "attn.norm.weight",
"post_attention_layernorm.weight": "mlp.norm.weight",
"embed_tokens": "embedding",
}
def maybe_rename(name: str) -> str:
for remap_name, new_name in rename_mapping.items():
if remap_name in name:
return name.replace(remap_name, new_name)
return name
params_dict = dict(self.named_parameters())
loaded_params: set[str] = set()
tp_rank = get_tensor_model_parallel_rank()
tp_size = get_tensor_model_parallel_world_size()
intermediate_size = self.model_config.intermediate_size
per_rank_intermediate_size = cdiv(intermediate_size, tp_size)
# Calculate common slicing bounds for current rank
tp_rank_start = tp_rank * per_rank_intermediate_size
tp_rank_end = min((tp_rank + 1) * per_rank_intermediate_size,
intermediate_size)
# Attention heads per rank
heads_per_rank = self.model_config.num_attention_heads // tp_size
head_start = tp_rank * heads_per_rank
use_ep = self.vllm_config.parallel_config.enable_expert_parallel
ep_size = get_ep_group().world_size
ep_rank = get_ep_group().rank
num_experts = self.model_config.num_local_experts
experts_per_rank = num_experts // ep_size
ep_rank_start = ep_rank * experts_per_rank
ep_rank_end = (ep_rank + 1) * experts_per_rank
for name, weight in weights:
if ".experts.gate_up_proj" in name and "bias" not in name:
# Handle MLP gate and up projection weights
new_name = name.replace(".experts.gate_up_proj",
".experts.w13_weight")
# Extract gate and up projection parts
# since the weight is shuffled, we can slice directly
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:, :,
2 * tp_rank_start:2 * tp_rank_end]
narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
param = params_dict[new_name]
param.copy_(narrow_weight)
loaded_params.add(new_name)
elif ".experts.down_proj" in name and "bias" not in name:
# Handle MLP down projection weights
new_name = name.replace(".experts.down_proj",
".experts.w2_weight")
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:, tp_rank_start:tp_rank_end, :]
narrow_weight = narrow_weight.permute(0, 2, 1).contiguous()
param = params_dict[new_name]
param.copy_(narrow_weight)
loaded_params.add(new_name)
elif "gate_up_proj_bias" in name:
# Handle MLP gate and up projection biases
new_name = name.replace("gate_up_proj_bias", "w13_bias")
# Extract gate and up projection bias parts
if use_ep:
narrow_weight = weight[ep_rank_start:ep_rank_end, ...]
else:
narrow_weight = weight[:,
2 * tp_rank_start:2 * tp_rank_end]
param = params_dict[new_name]
param.copy_(narrow_weight)
loaded_params.add(new_name)
elif "down_proj_bias" in name:
# Handle MLP down projection bias
new_name = name.replace("down_proj_bias", "w2_bias")
if use_ep:
weight = weight[ep_rank_start:ep_rank_end, ...]
else:
# (only load on rank 0 to avoid duplication)
if tp_rank != 0:
weight.zero_()
param = params_dict[new_name]
param.copy_(weight)
loaded_params.add(new_name)
elif "sinks" in name:
# Handle attention sinks (distributed across ranks)
name = name.replace("self_attn", "attn")
param = params_dict[name]
narrow_weight = weight.narrow(0, head_start, heads_per_rank)
param.data.copy_(narrow_weight)
loaded_params.add(name)
elif "q_proj" in name or "k_proj" in name or "v_proj" in name:
shard_id = ("q" if "q_proj" in name else
"k" if "k_proj" in name else "v")
name = name.replace("self_attn", "attn")
param_name = name.replace(f"{shard_id}_proj", "qkv")
param = params_dict[param_name]
weight_loader = param.weight_loader
weight_loader(param, weight, loaded_shard_id=shard_id)
loaded_params.add(param_name)
else:
# Handle all other weights with potential renaming
renamed_name = maybe_rename(name)
if renamed_name not in params_dict:
continue
param = params_dict[renamed_name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, weight)
loaded_params.add(renamed_name)
return loaded_params
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
quant_method = (self.model_config.quantization_config['quant_method']
if hasattr(self.model_config, "quantization_config")
else None)
if quant_method == "mxfp4":
return self._load_weights_mxfp4(weights)
else:
return self._load_weights_other(weights)
```
### LMDeploy
***WARNING: Up to version 0.9.2, lmdeploy does not provide support for GPT-OSS. To deploy InternVL3_5-GPT-OSS-20B-Preview, we recommend using vLLM.***
LMDeploy is a toolkit for compressing, deploying, and serving LLMs & VLMs.
```sh
pip install lmdeploy>=0.9.1
```
LMDeploy abstracts the complex inference process of multi-modal Vision-Language Models (VLM) into an easy-to-use pipeline, similar to the Large Language Model (LLM) inference pipeline.
#### A 'Hello, world' Example
```python
from lmdeploy import pipeline, PytorchEngineConfig
from lmdeploy.vl import load_image
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg')
# Please set tp=2 for the 38B version and tp=8 for the 241B-A28B version.
model = 'OpenGVLab/InternVL3_5-8B'
pipe = pipeline(model, backend_config=PytorchEngineConfig(session_len=32768, tp=1))
response = pipe(('describe this image', image))
print(response.text)
```
#### Multi-images Inference
When dealing with multiple images, you can put them all in one list. Keep in mind that multiple images will lead to a higher number of input tokens, and as a result, the size of the context window typically needs to be increased.
```python
from lmdeploy import pipeline, PytorchEngineConfig
from lmdeploy.vl import load_image
from lmdeploy.vl.constants import IMAGE_TOKEN
# Please set tp=2 for the 38B version and tp=8 for the 241B-A28B version.
model = 'OpenGVLab/InternVL3_5-8B'
pipe = pipeline(model, backend_config=PytorchEngineConfig(session_len=32768, tp=1))
image_urls=[
'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg',
'https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg'
]
images = [load_image(img_url) for img_url in image_urls]
# Numbering images improves multi-image conversations
response = pipe((f'Image-1: {IMAGE_TOKEN}\nImage-2: {IMAGE_TOKEN}\ndescribe these two images', images))
print(response.text)
```
#### Batch Prompts Inference
Conducting inference with batch prompts is quite straightforward; just place them within a list structure:
```python
from lmdeploy import pipeline, PytorchEngineConfig
from lmdeploy.vl import load_image
# Please set tp=2 for the 38B version and tp=8 for the 241B-A28B version.
model = 'OpenGVLab/InternVL3_5-8B'
pipe = pipeline(model, backend_config=PytorchEngineConfig(session_len=32768, tp=1))
image_urls=[
"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg",
"https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/det.jpg"
]
prompts = [('describe this image', load_image(img_url)) for img_url in image_urls]
response = pipe(prompts)
print(response)
```
#### Multi-turn Conversation
There are two ways to do the multi-turn conversations with the pipeline. One is to construct messages according to the format of OpenAI and use above introduced method, the other is to use the `pipeline.chat` interface.
```python
from lmdeploy import pipeline, PytorchEngineConfig, GenerationConfig
from lmdeploy.vl import load_image
# Please set tp=2 for the 38B version and tp=8 for the 241B-A28B version.
model = 'OpenGVLab/InternVL3_5-8B'
pipe = pipeline(model, backend_config=PytorchEngineConfig(session_len=32768, tp=1))
image = load_image('https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/demo/resources/human-pose.jpg')
gen_config = GenerationConfig(top_k=50, top_p=0.95, temperature=0.6, max_new_tokens=8192)
sess = pipe.chat(('describe this image', image), gen_config=gen_config)
print(sess.response.text)
sess = pipe.chat('What is the woman doing?', session=sess, gen_config=gen_config)
print(sess.response.text)
```
#### Service
LMDeploy's `api_server` enables models to be easily packed into services with a single command. The provided RESTful APIs are compatible with OpenAI's interfaces. Below are an example of service startup:
```shell
lmdeploy serve api_server OpenGVLab/InternVL3_5-8B --server-port 23333 --tp 1 --backend pytorch
```
To use the OpenAI-style interface, you need to install OpenAI:
```shell
pip install openai
```
Then, use the code below to make the API call:
```python
from openai import OpenAI
client = OpenAI(api_key='YOUR_API_KEY', base_url='http://0.0.0.0:23333/v1')
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
model=model_name,
messages=[{
'role':
'user',
'content': [{
'type': 'text',
'text': 'describe this image',
}, {
'type': 'image_url',
'image_url': {
'url':
'https://modelscope.oss-cn-beijing.aliyuncs.com/resource/tiger.jpeg',
},
}],
}],
temperature=0.8,
top_p=0.8)
print(response)
```
## License
This project is released under the apache-2.0 License. This project uses the pre-trained Qwen3 as a component, which is licensed under the apache-2.0 License.
## Citation
If you find this project useful in your research, please consider citing:
```BibTeX
@article{wang2025internvl3_5,
title={InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency},
author={Wang, Weiyun and Gao, Zhangwei and Gu, Lixin and Pu, Hengjun and Cui, Long and Wei, Xingguang and Liu, Zhaoyang and Jing, Linglin and Ye, Shenglong and Shao, Jie and others},
journal={arXiv preprint arXiv:2508.18265},
year={2025}
}
```
|