File size: 124,941 Bytes
9c197a2 |
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 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 |
{
"cells": [
{
"cell_type": "markdown",
"id": "1cc557a5-5a55-4bae-8555-a2b655abfa4a",
"metadata": {},
"source": [
"SPDX-License-Identifier: Apache-2.0\n",
"Copyright (c) 2023, Rahul Unnikrishnan Nair <[email protected]>\n"
]
},
{
"cell_type": "markdown",
"id": "2123be0f-586b-47c9-af17-9d667f28eb3d",
"metadata": {},
"source": [
"---\n",
"\n",
"**Text to SQL Generation: Fine-Tuning LLMs with QLoRA on Intel**\n",
"\n",
"👋 Hello and welcome! In this Jupyter Notebook, we will walkthrough the process of fine-tuning a large language model (LLM) to improve its capabilities in generating SQL queries from natural language input. The notebook is suitable for AI engineers and practitioners looking to tune LLMs for specialized tasks such as Text-to-SQL conversions.\n",
"\n",
"**What you will learn with this Notebook**\n",
"\n",
"- 🛠️ Fine-tune a Language Model with either a pre-existing dataset or a custom dataset tailored to your needs on Intel Hw.\n",
"- 💡 Gain insights into the fine-tuning process, including how to manipulate various training parameters to optimize your model's performance.\n",
"- 📊 Test different configurations and observe the results in real-time.\n",
"\n",
"**Hardware Compatibility**\n",
"\n",
"- 🖥️ Designed for 4th Generation Intel® Xeon® Scalable Processors (CPU) and Intel® Data Center GPU Max Series 1100 (XPU)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "67c0e67f-8473-44d6-8065-edfc63a6459d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"List of Intel GPUs available on the system:\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"| Device ID | Device Information |\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"| 0 | Device Name: Intel(R) Data Center GPU Max 1100 |\n",
"| | Vendor Name: Intel(R) Corporation |\n",
"| | SOC UUID: 00000000-0000-0029-0000-002f0bda8086 |\n",
"| | PCI BDF Address: 0000:29:00.0 |\n",
"| | DRM Device: /dev/dri/card0 |\n",
"| | Function Type: physical |\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"| 1 | Device Name: Intel(R) Data Center GPU Max 1100 |\n",
"| | Vendor Name: Intel(R) Corporation |\n",
"| | SOC UUID: 00000000-0000-003a-0000-002f0bda8086 |\n",
"| | PCI BDF Address: 0000:3a:00.0 |\n",
"| | DRM Device: /dev/dri/card2 |\n",
"| | Function Type: physical |\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"| 2 | Device Name: Intel(R) Data Center GPU Max 1100 |\n",
"| | Vendor Name: Intel(R) Corporation |\n",
"| | SOC UUID: 00000000-0000-009a-0000-002f0bda8086 |\n",
"| | PCI BDF Address: 0000:9a:00.0 |\n",
"| | DRM Device: /dev/dri/card3 |\n",
"| | Function Type: physical |\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"| 3 | Device Name: Intel(R) Data Center GPU Max 1100 |\n",
"| | Vendor Name: Intel(R) Corporation |\n",
"| | SOC UUID: 00000000-0000-00ca-0000-002f0bda8086 |\n",
"| | PCI BDF Address: 0000:ca:00.0 |\n",
"| | DRM Device: /dev/dri/card4 |\n",
"| | Function Type: physical |\n",
"+-----------+--------------------------------------------------------------------------------------+\n",
"Intel Xeon CPU used by this notebook:\n",
"Model name: Intel(R) Xeon(R) Platinum 8480+\n"
]
}
],
"source": [
"!echo \"List of Intel GPUs available on the system:\"\n",
"!xpu-smi discovery 2> /dev/null\n",
"!echo \"Intel Xeon CPU used by this notebook:\"\n",
"!lscpu | grep \"Model name\"\n"
]
},
{
"cell_type": "markdown",
"id": "76293a12-551e-421a-8eb3-06387931d307",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"---\n",
"\n",
"**Fine-Tuning with QLoRA: Balancing Memory Efficiency and Adaptability**\n",
"\n",
"We leverage the QLoRA methodology for fine-tuning, enabling the loading and refinement of LLMs within the constraints of available GPU memory. Quantized Low Rank Adaptation or QLoRA achieves this by applying a clever combination of weight quantization and adapter-based finetuning.\n",
"\n",
"**How Does QLoRA Work?**\n",
"\n",
"- QLoRA reduces memory footprint via weight quantization. It compresses the pre-trained model weights significantly.\n",
"- During fine-tuning, it focuses on optimizing adapter parameters—low-rank matrices added to the network, tailored for the specific task.\n",
"- This selective training is computationally efficient, targeting a smaller set of trainable parameters.\n",
"\n",
"\n",
"**What is the Big Picture?**\n",
"\n",
"- Think reparameterization: We inject LoRA weights, training only these, not the entire layer, for fine-tuning.\n",
"- This technique is key for task-specific model adaptation.\n",
"- Imagine a hub-and-spoke model for deployment: The hub is the foundational model, and the spokes are task-specific LoRA adapters.\n",
"\n",
"Below, on the left, is an overview of the reparameterization implemented with LoRA (with Quantization). This involves a set of low-rank matrices—think of these as an essential subset of larger weight matrices—trained specifically for the task. On the right, there's a high-level view of a hub-and-spoke model for LLM deployment, where the hub represents the foundational model, and the spokes are the LoRA adapters.\n",
"\n",
"<div align=\"center\">\n",
" <img src=\"https://github.com/rahulunair/sql_llm/assets/786476/c30d7fb4-2051-428c-9c55-fc4130cb11bc\" alt=\"lora_adapters_reparameterization\" width=\"75%\">\n",
"</div>"
]
},
{
"cell_type": "markdown",
"id": "6362a562-f3ce-4dce-9678-ce317e554a04",
"metadata": {},
"source": [
"## Initialization\n",
"\n",
"Let's first install and import all the necessary packages required for the fine-tuning process.\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "9236e9d1-e75e-4089-9a4d-27421521cfd5",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Installation in progress, please wait...\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mLooking in links: https://developer.intel.com/ipex-whl-stable-xpu\n",
"Requirement already satisfied: bigdl-llm==2.5.0b20240318 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2.5.0b20240318)\n",
"Requirement already satisfied: py-cpuinfo in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (9.0.0)\n",
"Requirement already satisfied: protobuf in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (4.25.3)\n",
"Requirement already satisfied: mpmath==1.3.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (1.3.0)\n",
"Requirement already satisfied: numpy==1.26.4 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (1.26.4)\n",
"Requirement already satisfied: transformers==4.31.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (4.31.0)\n",
"Requirement already satisfied: sentencepiece in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (0.2.0)\n",
"Requirement already satisfied: tokenizers==0.13.3 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (0.13.3)\n",
"Collecting accelerate==0.21.0 (from bigdl-llm[xpu]==2.5.0b20240318)\n",
" Downloading accelerate-0.21.0-py3-none-any.whl.metadata (17 kB)\n",
"Requirement already satisfied: tabulate in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (0.9.0)\n",
"Requirement already satisfied: torch==2.1.0a0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2.1.0a0+cxx11.abi)\n",
"Requirement already satisfied: torchvision==0.16.0a0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (0.16.0a0+cxx11.abi)\n",
"Requirement already satisfied: intel-extension-for-pytorch==2.1.10+xpu in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2.1.10+xpu)\n",
"Requirement already satisfied: bigdl-core-xe-21==2.5.0b20240318 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2.5.0b20240318)\n",
"Requirement already satisfied: intel-openmp in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2024.0.3)\n",
"Requirement already satisfied: bigdl-core-xe-esimd-21==2.5.0b20240318 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bigdl-llm[xpu]==2.5.0b20240318) (2.5.0b20240318)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from accelerate==0.21.0->bigdl-llm[xpu]==2.5.0b20240318) (23.2)\n",
"Requirement already satisfied: psutil in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.21.0->bigdl-llm[xpu]==2.5.0b20240318) (5.9.8)\n",
"Requirement already satisfied: pyyaml in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.21.0->bigdl-llm[xpu]==2.5.0b20240318) (6.0.1)\n",
"Requirement already satisfied: pydantic in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from intel-extension-for-pytorch==2.1.10+xpu->bigdl-llm[xpu]==2.5.0b20240318) (2.6.4)\n",
"Requirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (3.13.1)\n",
"Requirement already satisfied: typing-extensions in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (4.10.0)\n",
"Requirement already satisfied: sympy in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (1.12)\n",
"Requirement already satisfied: networkx in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (3.1.3)\n",
"Requirement already satisfied: fsspec in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (2023.10.0)\n",
"Requirement already satisfied: requests in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (2.31.0)\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (10.0.1)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.31.0->bigdl-llm[xpu]==2.5.0b20240318) (0.17.3)\n",
"Requirement already satisfied: regex!=2019.12.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.31.0->bigdl-llm[xpu]==2.5.0b20240318) (2023.12.25)\n",
"Requirement already satisfied: safetensors>=0.3.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.31.0->bigdl-llm[xpu]==2.5.0b20240318) (0.4.2)\n",
"Requirement already satisfied: tqdm>=4.27 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from transformers==4.31.0->bigdl-llm[xpu]==2.5.0b20240318) (4.66.2)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from jinja2->torch==2.1.0a0->bigdl-llm[xpu]==2.5.0b20240318) (2.1.5)\n",
"Requirement already satisfied: annotated-types>=0.4.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from pydantic->intel-extension-for-pytorch==2.1.10+xpu->bigdl-llm[xpu]==2.5.0b20240318) (0.6.0)\n",
"Requirement already satisfied: pydantic-core==2.16.3 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from pydantic->intel-extension-for-pytorch==2.1.10+xpu->bigdl-llm[xpu]==2.5.0b20240318) (2.16.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->torchvision==0.16.0a0->bigdl-llm[xpu]==2.5.0b20240318) (2024.2.2)\n",
"Downloading accelerate-0.21.0-py3-none-any.whl (244 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25h\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mInstalling collected packages: accelerate\n",
" Attempting uninstall: accelerate\n",
" Found existing installation: accelerate 0.23.0\n",
" Uninstalling accelerate-0.23.0:\n",
" Successfully uninstalled accelerate-0.23.0\n",
"\u001b[33m WARNING: The scripts accelerate, accelerate-config and accelerate-launch are installed in '/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0mSuccessfully installed accelerate-0.21.0\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: peft==0.5.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (0.5.0)\n",
"Requirement already satisfied: numpy>=1.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from peft==0.5.0) (23.2)\n",
"Requirement already satisfied: psutil in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (5.9.8)\n",
"Requirement already satisfied: pyyaml in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (6.0.1)\n",
"Requirement already satisfied: torch>=1.13.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (2.1.0a0+cxx11.abi)\n",
"Requirement already satisfied: transformers in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (4.31.0)\n",
"Requirement already satisfied: tqdm in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from peft==0.5.0) (4.66.2)\n",
"Requirement already satisfied: accelerate in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (0.21.0)\n",
"Requirement already satisfied: safetensors in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from peft==0.5.0) (0.4.2)\n",
"Requirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (3.13.1)\n",
"Requirement already satisfied: typing-extensions in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (4.10.0)\n",
"Requirement already satisfied: sympy in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (1.12)\n",
"Requirement already satisfied: networkx in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (3.1.3)\n",
"Requirement already satisfied: fsspec in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.13.0->peft==0.5.0) (2023.10.0)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers->peft==0.5.0) (0.17.3)\n",
"Requirement already satisfied: regex!=2019.12.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers->peft==0.5.0) (2023.12.25)\n",
"Requirement already satisfied: requests in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers->peft==0.5.0) (2.31.0)\n",
"Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers->peft==0.5.0) (0.13.3)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from jinja2->torch>=1.13.0->peft==0.5.0) (2.1.5)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers->peft==0.5.0) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers->peft==0.5.0) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers->peft==0.5.0) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers->peft==0.5.0) (2024.2.2)\n",
"Requirement already satisfied: mpmath>=0.19 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from sympy->torch>=1.13.0->peft==0.5.0) (1.3.0)\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mCollecting accelerate==0.23.0\n",
" Downloading accelerate-0.23.0-py3-none-any.whl.metadata (18 kB)\n",
"Requirement already satisfied: numpy>=1.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.23.0) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from accelerate==0.23.0) (23.2)\n",
"Requirement already satisfied: psutil in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.23.0) (5.9.8)\n",
"Requirement already satisfied: pyyaml in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.23.0) (6.0.1)\n",
"Requirement already satisfied: torch>=1.10.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.23.0) (2.1.0a0+cxx11.abi)\n",
"Requirement already satisfied: huggingface-hub in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from accelerate==0.23.0) (0.17.3)\n",
"Requirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (3.13.1)\n",
"Requirement already satisfied: typing-extensions in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (4.10.0)\n",
"Requirement already satisfied: sympy in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (1.12)\n",
"Requirement already satisfied: networkx in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (3.1.3)\n",
"Requirement already satisfied: fsspec in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch>=1.10.0->accelerate==0.23.0) (2023.10.0)\n",
"Requirement already satisfied: requests in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from huggingface-hub->accelerate==0.23.0) (2.31.0)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from huggingface-hub->accelerate==0.23.0) (4.66.2)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from jinja2->torch>=1.10.0->accelerate==0.23.0) (2.1.5)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->huggingface-hub->accelerate==0.23.0) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->huggingface-hub->accelerate==0.23.0) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->huggingface-hub->accelerate==0.23.0) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->huggingface-hub->accelerate==0.23.0) (2024.2.2)\n",
"Requirement already satisfied: mpmath>=0.19 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from sympy->torch>=1.10.0->accelerate==0.23.0) (1.3.0)\n",
"Downloading accelerate-0.23.0-py3-none-any.whl (258 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m258.1/258.1 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m0:01\u001b[0m\n",
"\u001b[?25h\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mInstalling collected packages: accelerate\n",
" Attempting uninstall: accelerate\n",
" Found existing installation: accelerate 0.21.0\n",
" Uninstalling accelerate-0.21.0:\n",
" Successfully uninstalled accelerate-0.21.0\n",
"Successfully installed accelerate-0.23.0\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mCollecting transformers==4.34.0\n",
" Downloading transformers-4.34.0-py3-none-any.whl.metadata (121 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.5/121.5 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (3.13.1)\n",
"Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (0.17.3)\n",
"Requirement already satisfied: numpy>=1.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (1.26.4)\n",
"Requirement already satisfied: packaging>=20.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from transformers==4.34.0) (23.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (6.0.1)\n",
"Requirement already satisfied: regex!=2019.12.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (2023.12.25)\n",
"Requirement already satisfied: requests in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (2.31.0)\n",
"Collecting tokenizers<0.15,>=0.14 (from transformers==4.34.0)\n",
" Downloading tokenizers-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\n",
"Requirement already satisfied: safetensors>=0.3.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from transformers==4.34.0) (0.4.2)\n",
"Requirement already satisfied: tqdm>=4.27 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from transformers==4.34.0) (4.66.2)\n",
"Requirement already satisfied: fsspec in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->transformers==4.34.0) (2023.10.0)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from huggingface-hub<1.0,>=0.16.4->transformers==4.34.0) (4.10.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers==4.34.0) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers==4.34.0) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers==4.34.0) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests->transformers==4.34.0) (2024.2.2)\n",
"Downloading transformers-4.34.0-py3-none-any.whl (7.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.7/7.7 MB\u001b[0m \u001b[31m68.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hDownloading tokenizers-0.14.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.8 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.8/3.8 MB\u001b[0m \u001b[31m97.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
"\u001b[?25h\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mInstalling collected packages: tokenizers, transformers\n",
" Attempting uninstall: tokenizers\n",
" Found existing installation: tokenizers 0.13.3\n",
" Uninstalling tokenizers-0.13.3:\n",
" Successfully uninstalled tokenizers-0.13.3\n",
" Attempting uninstall: transformers\n",
" Found existing installation: transformers 4.31.0\n",
" Uninstalling transformers-4.31.0:\n",
" Successfully uninstalled transformers-4.31.0\n",
"Successfully installed tokenizers-0.14.1 transformers-4.34.0\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: datasets==2.14.6 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (2.14.6)\n",
"Requirement already satisfied: numpy>=1.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (1.26.4)\n",
"Requirement already satisfied: pyarrow>=8.0.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (15.0.0)\n",
"Requirement already satisfied: dill<0.3.8,>=0.3.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (0.3.7)\n",
"Requirement already satisfied: pandas in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (2.2.1)\n",
"Requirement already satisfied: requests>=2.19.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (2.31.0)\n",
"Requirement already satisfied: tqdm>=4.62.1 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (4.66.2)\n",
"Requirement already satisfied: xxhash in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (3.4.1)\n",
"Requirement already satisfied: multiprocess in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (0.70.15)\n",
"Requirement already satisfied: fsspec<=2023.10.0,>=2023.1.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from fsspec[http]<=2023.10.0,>=2023.1.0->datasets==2.14.6) (2023.10.0)\n",
"Requirement already satisfied: aiohttp in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (3.9.3)\n",
"Requirement already satisfied: huggingface-hub<1.0.0,>=0.14.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (0.17.3)\n",
"Requirement already satisfied: packaging in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from datasets==2.14.6) (23.2)\n",
"Requirement already satisfied: pyyaml>=5.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from datasets==2.14.6) (6.0.1)\n",
"Requirement already satisfied: aiosignal>=1.1.2 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (1.3.1)\n",
"Requirement already satisfied: attrs>=17.3.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (23.2.0)\n",
"Requirement already satisfied: frozenlist>=1.1.1 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (1.4.1)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (6.0.5)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (1.9.4)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from aiohttp->datasets==2.14.6) (4.0.3)\n",
"Requirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets==2.14.6) (3.13.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from huggingface-hub<1.0.0,>=0.14.0->datasets==2.14.6) (4.10.0)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.6) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.6) (3.6)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.6) (2.2.1)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from requests>=2.19.0->datasets==2.14.6) (2024.2.2)\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from pandas->datasets==2.14.6) (2.8.2)\n",
"Requirement already satisfied: pytz>=2020.1 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from pandas->datasets==2.14.6) (2023.3.post1)\n",
"Requirement already satisfied: tzdata>=2022.7 in /opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/site-packages (from pandas->datasets==2.14.6) (2023.3)\n",
"Requirement already satisfied: six>=1.5 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from python-dateutil>=2.8.2->pandas->datasets==2.14.6) (1.16.0)\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mDefaulting to user installation because normal site-packages is not writeable\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mRequirement already satisfied: bitsandbytes==0.43.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (0.43.0)\n",
"Requirement already satisfied: scipy==1.12.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (1.12.0)\n",
"Requirement already satisfied: torch in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bitsandbytes==0.43.0) (2.1.0a0+cxx11.abi)\n",
"Requirement already satisfied: numpy in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from bitsandbytes==0.43.0) (1.26.4)\n",
"Requirement already satisfied: filelock in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (3.13.1)\n",
"Requirement already satisfied: typing-extensions in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (4.10.0)\n",
"Requirement already satisfied: sympy in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (1.12)\n",
"Requirement already satisfied: networkx in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (3.1.3)\n",
"Requirement already satisfied: fsspec in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from torch->bitsandbytes==0.43.0) (2023.10.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from jinja2->torch->bitsandbytes==0.43.0) (2.1.5)\n",
"Requirement already satisfied: mpmath>=0.19 in /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages (from sympy->torch->bitsandbytes==0.43.0) (1.3.0)\n",
"\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0m\u001b[33mWARNING: Ignoring invalid distribution -ransformers (/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages)\u001b[0m\u001b[33m\n",
"\u001b[0mInstallation completed.\n"
]
}
],
"source": [
"import sys\n",
"import site\n",
"from pathlib import Path\n",
"\n",
"!echo \"Installation in progress, please wait...\"\n",
"!{sys.executable} -m pip cache purge > /dev/null\n",
"!{sys.executable} -m pip install --pre --upgrade \"bigdl-llm[xpu]==2.5.0b20240318\" -f https://developer.intel.com/ipex-whl-stable-xpu\n",
"!{sys.executable} -m pip install \"peft==0.5.0\" #> /dev/null\n",
"!{sys.executable} -m pip install \"accelerate==0.23.0\" --no-warn-script-location #> /dev/null\n",
"!{sys.executable} -m pip install \"transformers==4.34.0\" --no-warn-script-location #> /dev/null \n",
"!{sys.executable} -m pip install \"datasets==2.14.6\" --no-warn-script-location #> /dev/null 2>&1 \n",
"!{sys.executable} -m pip install \"bitsandbytes==0.43.0\" \"scipy==1.12.0\" #> /dev/null 2>&1\n",
"!echo \"Installation completed.\"\n",
"\n",
"def get_python_version():\n",
" return \"python\" + \".\".join(map(str, sys.version_info[:2]))\n",
"\n",
"def set_local_bin_path():\n",
" local_bin = str(Path.home() / \".local\" / \"bin\") \n",
" local_site_packages = str(\n",
" Path.home() / \".local\" / \"lib\" / get_python_version() / \"site-packages\"\n",
" )\n",
" sys.path.append(local_bin)\n",
" sys.path.insert(0, site.getusersitepackages())\n",
" sys.path.insert(0, sys.path.pop(sys.path.index(local_site_packages)))\n",
"\n",
"set_local_bin_path()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "31eb9cf2-abcf-48f8-918b-37a18f85ac7c",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-03-24 01:29:30,660 - root - INFO - intel_extension_for_pytorch auto imported\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/bitsandbytes/libbitsandbytes_cpu.so: undefined symbol: cadam32bit_grad_fp32\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-03-24 01:29:32.212342: I tensorflow/core/util/port.cc:111] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-03-24 01:29:32.219815: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-03-24 01:29:32.245983: E tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:9342] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"2024-03-24 01:29:32.246003: E tensorflow/compiler/xla/stream_executor/cuda/cuda_fft.cc:609] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"2024-03-24 01:29:32.246022: E tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:1518] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-03-24 01:29:32.251166: I tensorflow/tsl/cuda/cudart_stub.cc:28] Could not find cuda drivers on your machine, GPU will not be used.\n",
"2024-03-24 01:29:32.251625: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 AVX_VNNI AMX_TILE AMX_INT8 AMX_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
"2024-03-24 01:29:34.718987: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
"2024-03-24 01:29:36.469335: I itex/core/wrapper/itex_cpu_wrapper.cc:60] Intel Extension for Tensorflow* AVX512 CPU backend is loaded.\n",
"2024-03-24 01:29:36.877562: I itex/core/wrapper/itex_gpu_wrapper.cc:35] Intel Extension for Tensorflow* GPU backend is loaded.\n",
"2024-03-24 01:29:36.959003: I itex/core/devices/gpu/itex_gpu_runtime.cc:129] Selected platform: Intel(R) Level-Zero\n",
"2024-03-24 01:29:36.959284: I itex/core/devices/gpu/itex_gpu_runtime.cc:154] number of sub-devices is zero, expose root device.\n"
]
}
],
"source": [
"import logging\n",
"import os\n",
"import warnings\n",
"import predictionguard as pg\n",
"\n",
"warnings.filterwarnings(\n",
" \"ignore\", category=UserWarning, module=\"intel_extension_for_pytorch\"\n",
")\n",
"warnings.filterwarnings(\n",
" \"ignore\", category=UserWarning, module=\"torchvision.io.image\", lineno=13\n",
")\n",
"warnings.filterwarnings(\n",
" \"ignore\",\n",
" message=\"The installed version of bitsandbytes was compiled without GPU support.*\",\n",
" category=UserWarning,\n",
" module='bitsandbytes.cextension'\n",
")\n",
"warnings.filterwarnings(\"ignore\")\n",
"warnings.filterwarnings(\n",
" \"ignore\",\n",
" category=FutureWarning,\n",
" message=\"This implementation of AdamW is deprecated\",\n",
")\n",
"os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n",
"os.environ[\"NUMEXPR_MAX_THREADS\"] = \"28\"\n",
"os.environ[\"ENABLE_SDP_FUSION\"] = \"true\"\n",
"os.environ[\"SYCL_PI_LEVEL_ZERO_USE_IMMEDIATE_COMMANDLISTS\"]=\"1\"\n",
"\n",
"os.environ[\"PREDICTIONGUARD_TOKEN\"] = \"q1VuOjnffJ3NO2oFN8Q9m8vghYc84ld13jaqdF7E\"\n",
"\n",
"logging.getLogger(\"transformers\").setLevel(logging.ERROR)\n",
"logging.getLogger(\"bigdl\").setLevel(logging.ERROR)\n",
"\n",
"\n",
"import torch\n",
"import intel_extension_for_pytorch as ipex\n",
"from datasets import load_dataset\n",
"from datasets import Dataset\n",
"from bigdl.llm.transformers import AutoModelForCausalLM\n",
"from bigdl.llm.transformers.qlora import (\n",
" get_peft_model,\n",
" prepare_model_for_kbit_training as prepare_model,\n",
")\n",
"from peft import LoraConfig\n",
"from bigdl.llm.transformers.qlora import PeftModel\n",
"import transformers\n",
"from transformers import (\n",
" BitsAndBytesConfig,\n",
" DataCollatorForSeq2Seq,\n",
" LlamaTokenizer,\n",
" AutoTokenizer,\n",
" Trainer,\n",
" TrainingArguments,\n",
")\n",
"\n",
"transformers.logging.set_verbosity_error()"
]
},
{
"cell_type": "markdown",
"id": "02b08285",
"metadata": {},
"source": [
"---\n",
"\n",
"**Note on Model Storage Management**\n",
"\n",
"A set of LLM foundation models are supported out-of-the-box as stated below `BASE_MDOELS` dictionary. However, if you're interested in experimenting with additional models, consider the following guidelines:\n",
"\n",
"- **Storage Quota:** Be mindful of your free storage quota and space requirements for additional models.\n",
"- **PEFT Library Support:** For models supported by `peft`, refer to the [PEFT repository](https://github.com/huggingface/peft/blob/main/src/peft/utils/other.py#L434) for predefined LoRA target modules.\n",
"- **Custom Models:** For non-`peft` models, manually configure LoRA target modules in `LoraConfig`. Example for llama models: `[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"]`.\n",
"- **Disk Space Management:** Check disk space with the provided Python function. Delete cache to free space, but this requires re-downloading models later.\n",
"- **Reset Model Cache Path:** Update `MODEL_CACHE_PATH = \"~/\"` in the **Model Configuration** cell.\n",
"\n",
"---\n",
"\n",
"**Python Function to Check Disk Space**\n",
"\n",
"```python\n",
"# Function to check available disk space in the Hugging Face cache directory\n",
"import os\n",
"import shutil\n",
"\n",
"def check_disk_space(path=\"~/.cache/huggingface/\"):\n",
" abs_path = os.path.expanduser(path)\n",
" total, used, free = shutil.disk_usage(abs_path)\n",
" print(f\"Total: {total // (2**30)} GiB\")\n",
" print(f\"Used: {used // (2**30)} GiB\")\n",
" print(f\"Free: {free // (2**30)} GiB\")\n",
"\n",
"# Example usage\n",
"check_disk_space()\n",
"```\n"
]
},
{
"cell_type": "markdown",
"id": "131864cb-ce5d-405b-8886-5d0f1f487c30",
"metadata": {},
"source": [
"---\n",
"\n",
"**Tailoring Your Model Configuration**\n",
"\n",
"Dive into the customization core of LLM fine-tuning, equipped with a diverse range of base models to suit unique goals.\n",
"\n",
"- **Model Choices in `BASE_MODELS`**: \n",
" - From the `open_llama_3b_v2` to the broader `Llama-2-13b-hf`.\n",
" - Specialized options like `CodeLlama-7b-hf`.\n",
" - Experiment to find the best fit for your objectives.\n",
"\n",
"- **Dataset**:\n",
" - Using `b-mc2/sql-create-context` from Huggingface datasets, a set of 78,577 examples (natural language queries, SQL statements).\n",
" - Ideal for text-to-SQL models. Dataset details [here](https://huggingface.co/datasets/b-mc2/sql-create-context).\n",
"\n",
"- **Your Model Options**: Within the `BASE_MODELS`, you’ll find options ranging from the nimble\n",
" `open_llama_7b_v2` to the more expansive `Llama-2-13b-hf`, and specialized variants like `CodeLlama-7b-hf`.\n",
" Feel free to switch between these models to discover which one aligns best with your objectives.\n",
"\n",
"- **LoRA Parameters - Your Knobs to Turn**:\n",
" - `r` (Rank): This is a key factor in how finely your model can adapt. A higher rank can grasp more\n",
" complex nuances, while a lower rank ensures a leaner memory footprint.\n",
" - `lora_alpha` (Scaling Factor): Adjusts LoRA adapters' impact.\n",
" the integrity of the pre-trained weights.\n",
" - `target_modules`: You decide which parts of the transformer model to enhance with LoRA adapters,\n",
" directly impacting how your model interprets and generates language.\n",
" - `lora_dropout`: Controls overfitting; experiment for optimal generalization.\n",
" - `bias`: Modify to observe learning dynamic changes.\n",
"\n",
"This notebook is set to start with `CodeLlama-7b-hf` as the default model, as our task is to generate code. To use models like Llama 2, you will have to accept the usage policy as stipulated [here](https://ai.meta.com/llama/use-policy/)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "88077cc2-8fcf-4128-ac44-9fb2bb327398",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"================================================================================\n",
"Using Device: xpu\n",
"Final model will be saved to: ./final_model\n",
"LoRA adapters will be saved to: ./lora_adapters\n",
"Finetuning Model: NousResearch/CodeLlama-7b-hf\n",
"Using dataset from: b-mc2/sql-create-context\n",
"Model cache: /home/common/data/Big_Data/GenAI/llm_models\n",
"================================================================================\n"
]
}
],
"source": [
"BASE_MODELS = {\n",
" \"0\": \"NousResearch/Nous-Hermes-Llama-2-7b\", # https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b\n",
" \"1\": \"NousResearch/Llama-2-7b-chat-hf\", # https://huggingface.co/NousResearch/Llama-2-7b-chat-hf\n",
" \"2\": \"NousResearch/Llama-2-13b-hf\", # https://huggingface.co/NousResearch/Llama-2-13b-hf\n",
" \"3\": \"NousResearch/CodeLlama-7b-hf\", # https://huggingface.co/NousResearch/CodeLlama-7b-hf\n",
" \"4\": \"Phind/Phind-CodeLlama-34B-v2\", # https://huggingface.co/Phind/Phind-CodeLlama-34B-v2\n",
" \"5\": \"openlm-research/open_llama_3b_v2\", # https://huggingface.co/openlm-research/open_llama_3b_v2\n",
" \"6\": \"openlm-research/open_llama_13b\", # https://huggingface.co/openlm-research/open_llama_13b\n",
" \"7\": \"HuggingFaceH4/zephyr-7b-beta\", # https://huggingface.co/HuggingFaceH4/zephyr-7b-beta\n",
"}\n",
"BASE_MODEL = BASE_MODELS[\"3\"]\n",
"DATA_PATH = \"b-mc2/sql-create-context\"\n",
"MODEL_PATH = \"./final_model\"\n",
"ADAPTER_PATH = \"./lora_adapters\"\n",
"DEVICE = torch.device(\"xpu\" if torch.xpu.is_available() else \"cpu\")\n",
"LORA_CONFIG = LoraConfig(\n",
" r=16, # rank\n",
" lora_alpha=32, # scaling factor\n",
" target_modules=[\"q_proj\", \"k_proj\", \"v_proj\"], \n",
" lora_dropout=0.05,\n",
" bias=\"none\",\n",
" task_type=\"CAUSAL_LM\",\n",
")\n",
"MODEL_CACHE_PATH = \"/home/common/data/Big_Data/GenAI/llm_models\"\n",
"\n",
"print(\"=\" * 80)\n",
"print(f\"Using Device: {DEVICE}\")\n",
"print(f\"Final model will be saved to: {MODEL_PATH}\")\n",
"print(f\"LoRA adapters will be saved to: {ADAPTER_PATH}\")\n",
"print(f\"Finetuning Model: {BASE_MODEL}\")\n",
"print(f\"Using dataset from: {DATA_PATH}\")\n",
"print(f\"Model cache: {MODEL_CACHE_PATH}\")\n",
"print(\"=\" * 80)"
]
},
{
"cell_type": "markdown",
"id": "d797b621-fd06-4ae9-a883-d7d15f16d6c4",
"metadata": {},
"source": [
"---\n",
"\n",
"**Prompt Engineering for Text-to-SQL Conversion**\n",
"\n",
"In the realm of fine-tuning language models for specialized tasks, the design of the prompt is pivotal. The function `generate_prompt_sql` encapsulates the input question, the relevant database context, and the expected output in a structured and concise manner.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "d8ecd7df-b7ce-48b0-ba9f-04b7ec0c1cf4",
"metadata": {},
"outputs": [],
"source": [
"def generate_prompt_sql(input_question, context, output=\"\"):\n",
" \"\"\"\n",
" Generates a prompt for fine-tuning the LLM model for text-to-SQL tasks.\n",
"\n",
" Parameters:\n",
" input_question (str): The input text or question to be converted to SQL.\n",
" context (str): The schema or context in which the SQL query operates.\n",
" output (str, optional): The expected SQL query as the output.\n",
"\n",
" Returns:\n",
" str: A formatted string serving as the prompt for the fine-tuning task.\n",
" \"\"\"\n",
" return f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables.\"\"\" \n",
"\n",
"### Input:\n",
"input_question = str(\"How many employees live in california\")\n",
"\n",
"### Context:\n",
"context = str(\"the number of approximate employees worldwide, country, and state they are in a single company\")\n",
"\n",
"### Response:\n",
"response = f\"Knowing {context}, can you answer {input_question}?\"\n",
"\n",
"output = \"Select from table employees with california home\" #approximately"
]
},
{
"cell_type": "markdown",
"id": "9dd863cc",
"metadata": {},
"source": [
"---\n",
"\n",
"**Model Loading and Configuration**\n",
"\n",
"Initializing the `FineTuner`, we load the base model using `base_model_id`. Key to this setup is the ` bnb_4bit_quant_type=\"nf4\"` option, using bitsandbytes library, checkout [BigDL library](https://bigdl.readthedocs.io/en/latest/) for more information on this. This approach significantly cuts down on memory. Additionally, we configure the LoRA adapters for mixed-precision training with `torch.float16`.\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "aeab82dc",
"metadata": {},
"outputs": [],
"source": [
"def setup_model_and_tokenizer(base_model_id: str):\n",
" \"\"\"Downloads / Loads the pre-trained model and tokenizer based on the given base model ID for training, \n",
" with fallbacks for permission errors to use default cache.\"\"\"\n",
" local_model_id = base_model_id.replace(\"/\", \"--\")\n",
" local_model_path = os.path.join(MODEL_CACHE_PATH, local_model_id)\n",
"\n",
" bnb_config = BitsAndBytesConfig(\n",
" load_in_8bit=True,\n",
" bnb_4bit_use_double_quant=False,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" accelerator='onnxruntime',\n",
" )\n",
" try:\n",
" print(f\"Attempting to load model and tokenizer from: {local_model_path}\")\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" local_model_path,\n",
" quantization_config=bnb_config,\n",
" )\n",
" tokenizer_class = LlamaTokenizer if \"llama\" in base_model_id.lower() else AutoTokenizer\n",
" tokenizer = tokenizer_class.from_pretrained(local_model_path)\n",
" except (OSError, PermissionError) as e:\n",
" print(f\"Failed to load from {local_model_path} due to {e}. Attempting to download...\")\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" base_model_id, \n",
" quantization_config=bnb_config,\n",
" )\n",
" tokenizer_class = LlamaTokenizer if \"llama\" in base_model_id.lower() else AutoTokenizer\n",
" tokenizer = tokenizer_class.from_pretrained(base_model_id)\n",
"\n",
" tokenizer.pad_token_id = 0\n",
" tokenizer.padding_side = \"left\"\n",
" return model, tokenizer\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "3777656b-74f4-4ebc-b741-a5d50bc6e79a",
"metadata": {},
"source": [
"---\n",
"\n",
"**FineTuner**\n",
"\n",
"The `FineTuner` class encapsulates the entire process of fine-tuning llms for tasks such as text-to-SQL conversion.\n",
"\n",
"\n",
"**Tokenization Strategy**\n",
"\n",
"The tokenization process is tailored to the type of model being fine-tuned. For instance, if we are working with a Llama model, we utilize a `LlamaTokenizer` to ensure compatibility with the model's expected input format. For other models, a generic `AutoTokenizer` is used. We configure the tokenizer to pad from the left side (`padding_side=\"left\"`) and set the pad token ID to 0.\n",
"\n",
"**Data Tokenization and Preparation**\n",
"\n",
"The `tokenize_data` method is where the fine-tuner ingests raw text data and converts it into a format suitable for training the model. This method handles the addition of end-of-sequence tokens, truncation to a specified `cutoff_len`, and conditioning on the input for training.\n",
"\n",
"**Dataset Handling**\n",
"\n",
"`prepare_data` manages the splitting of data into training and validation sets, applying the `tokenize_data` transformation to each entry. This ensures that our datasets are ready for input into the model, with all necessary tokenization applied.\n",
"\n",
"**Training Process**\n",
"\n",
"Finally, the `train_model` method orchestrates the training process, setting up the `Trainer` with the correct datasets, training arguments, and data collator. The fine-tuning process is encapsulated within the `finetune` method, which strings together all the previous steps into a coherent pipeline, from model setup to training execution.\n",
"\n",
"**Using QLoRA for Efficient Fine-Tuning**\n",
"1. Load a pretrained model (e.g., LLaMA2) in low precision with ` bnb_4bit_quant_type=\"nf4\"` for 4-bit quantized weights.\n",
"2. Prepare the quantized model with `prepare_model(model)`, handling weight quantization.\n",
"3. Add LoRA adapters via `get_peft_model(model, config)` for setting adapter parameters.\n",
"4. Fine-tune with `Trainer`, focusing gradients on adapters while keeping base model weights fixed.\n",
"\n",
"**Code Implementation**\n",
"- Model loading with BigDL's `AutoModelForCausalLM`, initializing in 4-bit using `load_in_low_bit=\"nf4\"`.\n",
"- `prepare_model()` quantizes the model weights.\n",
"- `get_peft_model()` adds LoRA adapters.\n",
"- Trainer handles fine-tuning, optimizing only adapter weights.\n",
"\n",
"\n",
"So in summary, we leverage QLoRA in BigDL to load the base LLM in low precision, inject adapters with `peft`, and efficiently finetune by optimizing just the adapters end-to-end while keeping the base model fixed. This unlocks huge memory savings, allowing us to adapt giant models."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4d8f4cb8-0da5-4572-bd07-bdae1db897a3",
"metadata": {},
"outputs": [],
"source": [
"class FineTuner:\n",
" \"\"\"A class to handle the fine-tuning of LLM models.\"\"\"\n",
"\n",
" def __init__(self, base_model_id: str, model_path: str, device: torch.device):\n",
" \"\"\"\n",
" Initialize the FineTuner with base model, model path, and device.\n",
"\n",
" Parameters:\n",
" base_model_id (str): Id of pre-trained model to use for fine-tuning.\n",
" model_path (str): Path to save the fine-tuned model.\n",
" device (torch.device): Device to run the model on.\n",
" \"\"\"\n",
" self.base_model_id = base_model_id\n",
" self.model_path = model_path\n",
" self.device = device\n",
" self.model, self.tokenizer = setup_model_and_tokenizer(base_model_id)\n",
"\n",
"\n",
" def tokenize_data(\n",
" self, data_points, add_eos_token=True, train_on_inputs=False, cutoff_len=512\n",
" ) -> dict:\n",
" \"\"\"\n",
" Tokenizes dataset of SQL related data points consisting of questions, context, and answers.\n",
"\n",
" Parameters:\n",
" data_points (dict): A batch from the dataset containing 'question', 'context', and 'answer'.\n",
" add_eos_token (bool): Whether to add an EOS token at the end of each tokenized sequence.\n",
" cutoff_len (int): The maximum length for each tokenized sequence.\n",
"\n",
" Returns:\n",
" dict: A dictionary containing tokenized 'input_ids', 'attention_mask', and 'labels'.\n",
" \"\"\"\n",
" try:\n",
" question = data_points[\"question\"]\n",
" context = data_points[\"context\"]\n",
" answer = data_points[\"answer\"]\n",
" if train_on_inputs:\n",
" user_prompt = generate_prompt_sql(question, context)\n",
" tokenized_user_prompt = self.tokenizer(\n",
" user_prompt,\n",
" truncation=True,\n",
" max_length=cutoff_len,\n",
" padding=False,\n",
" return_tensors=None,\n",
" )\n",
" user_prompt_len = len(tokenized_user_prompt[\"input_ids\"])\n",
" if add_eos_token:\n",
" user_prompt_len -= 1\n",
"\n",
" combined_text = generate_prompt_sql(question, context, answer)\n",
" tokenized = self.tokenizer(\n",
" combined_text,\n",
" truncation=True,\n",
" max_length=cutoff_len,\n",
" padding=False,\n",
" return_tensors=None,\n",
" )\n",
" if (\n",
" tokenized[\"input_ids\"][-1] != self.tokenizer.eos_token_id\n",
" and add_eos_token\n",
" and len(tokenized[\"input_ids\"]) < cutoff_len\n",
" ):\n",
" tokenized[\"input_ids\"].append(self.tokenizer.eos_token_id)\n",
" tokenized[\"attention_mask\"].append(1)\n",
" tokenized[\"labels\"] = tokenized[\"input_ids\"].copy()\n",
" if train_on_inputs:\n",
" tokenized[\"labels\"] = [-100] * user_prompt_len + tokenized[\"labels\"][\n",
" user_prompt_len:\n",
" ]\n",
" return tokenized\n",
" except Exception as e:\n",
" logging.error(\n",
" f\"Error in batch tokenization: {e}\"\n",
" )\n",
" raise e\n",
"\n",
" def prepare_data(self, data, val_set_size=100) -> Dataset:\n",
" \"\"\"Prepare training and validation datasets.\"\"\"\n",
" try:\n",
" train_val_split = data[\"train\"].train_test_split(\n",
" test_size=val_set_size, shuffle=True, seed=42\n",
" )\n",
" train_data = train_val_split[\"train\"].shuffle().map(self.tokenize_data)\n",
" val_data = train_val_split[\"test\"].shuffle().map(self.tokenize_data)\n",
" return train_data, val_data\n",
" except Exception as e:\n",
" logging.error(\n",
" f\"Error in preparing data: {e}\"\n",
" )\n",
" raise e\n",
"\n",
" def train_model(self, train_data, val_data, training_args):\n",
" \"\"\"\n",
" Fine-tune the model with the given training and validation data.\n",
"\n",
" Parameters:\n",
" train_data (Dataset): Training data.\n",
" val_data (Optional[Dataset]): Validation data.\n",
" training_args (TrainingArguments): Training configuration.\n",
" \"\"\"\n",
" try:\n",
" self.model = self.model.to(self.device)\n",
" self.model.gradient_checkpointing_enable()\n",
" self.model = prepare_model(self.model)\n",
" self.model = get_peft_model(self.model, LORA_CONFIG)\n",
" trainer = Trainer(\n",
" model=self.model,\n",
" train_dataset=train_data,\n",
" eval_dataset=val_data,\n",
" args=training_args,\n",
" data_collator=DataCollatorForSeq2Seq(\n",
" self.tokenizer,\n",
" pad_to_multiple_of=8,\n",
" return_tensors=\"pt\",\n",
" padding=True,\n",
" ),\n",
" )\n",
" self.model.config.use_cache = False\n",
" results = trainer.train()\n",
" #print(results)\n",
" self.model.save_pretrained(self.model_path)\n",
" except Exception as e:\n",
" logging.error(f\"Error in model training: {e}\")\n",
"\n",
" def finetune(self, data_path, training_args):\n",
" \"\"\"\n",
" Execute the fine-tuning pipeline.\n",
"\n",
" Parameters:\n",
" data_path (str): Path to the data for fine-tuning.\n",
" training_args (TrainingArguments): Training configuration.\n",
" \"\"\"\n",
" try:\n",
" data = load_dataset(data_path)\n",
" train_data, val_data = self.prepare_data(data)\n",
" self.train_model(train_data, val_data, training_args)\n",
" except KeyboardInterrupt:\n",
" print(\"Interrupt received, saving model...\")\n",
" self.model.save_pretrained(f\"{self.model_path}_interrupted\")\n",
" print(f\"Model saved to {self.model_path}_interrupted\")\n",
" except Exception as e:\n",
" logging.error(f\"Error in fintuning: {e}\")"
]
},
{
"cell_type": "markdown",
"id": "34af4187-d362-49b8-bdbc-e8d2a7ae0ac0",
"metadata": {},
"source": [
"---\n",
"**Fine-Tuning the Model**\n",
"\n",
"The `lets_finetune` function orchestrates the fine-tuning process, offering a customizable interface for training. It enables specification of device, model, batch size, warm-up steps, learning rate, and maximum training steps.\n",
"\n",
"\n",
"**Some of the key Training Parameters:**\n",
"- `per_device_batch_size`: Number of batches on each XPU.\n",
"- `gradient_accumulation_steps`: Enables larger effective batch sizes.\n",
"- `warmup_steps`: Stabilizes training dynamics at the start.\n",
"- `save_steps`: Determines checkpoint frequency.\n",
"- `max_steps`: Limits training iterations, start with a high number like 1000 or 2000 (default here is `200`).\n",
"- `learning_rate`: Balances convergence speed and training stability.\n",
"- `max_grad_norm`: Clips gradients to avoid excessively large values.\n",
"\n",
"**Monitoring and Interruption**\n",
"- Monitor training/validation loss to identify optimal stopping point.\n",
"- Interrupt training in Jupyter via `Kernel -> Interrupt Kernel` if performance is satisfactory before `max_steps`.\n",
"- Latest checkpoint is saved in `./final_model_interrupted`; last saved adapter checkpoint in `./lora_adapters`.\n",
"\n",
"This setup allows for efficient and flexible model fine-tuning, adaptable to varying project needs and computational constraints.\n",
"\n",
"---\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "67a39122-eafc-483c-ad81-9c55de15e936",
"metadata": {},
"outputs": [],
"source": [
"ENABLE_WANDB = True\n",
"\n",
"def lets_finetune(\n",
" device=DEVICE,\n",
" model=BASE_MODEL,\n",
" per_device_batch_size=4,\n",
" warmup_steps=20,\n",
" learning_rate=2e-5,\n",
" max_steps=200,\n",
" gradient_accum_steps=4,\n",
"):\n",
" try:\n",
" # Training parameters\n",
" save_steps = 20\n",
" eval_steps = 20\n",
" max_grad_norm = 0.3\n",
" save_total_limit = 3\n",
" logging_steps = 20\n",
"\n",
" print(\"\\n\" + \"\\033[1;34m\" + \"=\" * 60 + \"\\033[0m\")\n",
" print(\"\\033[1;34mTraining Parameters:\\033[0m\")\n",
" param_format = \"\\033[1;34m{:<25} {}\\033[0m\"\n",
" print(param_format.format(\"Foundation model:\", BASE_MODEL))\n",
" print(param_format.format(\"Model save path:\", MODEL_PATH))\n",
" print(param_format.format(\"Device used:\", DEVICE))\n",
" if DEVICE.type.startswith(\"xpu\"):\n",
" print(param_format.format(\"Intel GPU:\", torch.xpu.get_device_name()))\n",
" print(param_format.format(\"Batch size per device:\", per_device_batch_size))\n",
" print(param_format.format(\"Gradient accum. steps:\", gradient_accum_steps))\n",
" print(param_format.format(\"Warmup steps:\", warmup_steps))\n",
" print(param_format.format(\"Save steps:\", save_steps))\n",
" print(param_format.format(\"Evaluation steps:\", eval_steps))\n",
" print(param_format.format(\"Max steps:\", max_steps))\n",
" print(param_format.format(\"Learning rate:\", learning_rate))\n",
" print(param_format.format(\"Max gradient norm:\", max_grad_norm))\n",
" print(param_format.format(\"Save total limit:\", save_total_limit))\n",
" print(param_format.format(\"Logging steps:\", logging_steps))\n",
" print(\"\\033[1;34m\" + \"=\" * 60 + \"\\033[0m\\n\")\n",
"\n",
" # Initialize the finetuner with the model and device information\n",
" finetuner = FineTuner(\n",
" base_model_id=model, model_path=MODEL_PATH, device=device\n",
" )\n",
"\n",
" training_args = TrainingArguments(\n",
" per_device_train_batch_size=per_device_batch_size,\n",
" gradient_accumulation_steps=gradient_accum_steps,\n",
" warmup_steps=warmup_steps,\n",
" save_steps=save_steps,\n",
" save_strategy=\"steps\",\n",
" eval_steps=eval_steps,\n",
" evaluation_strategy=\"steps\",\n",
" max_steps=max_steps,\n",
" learning_rate=learning_rate,\n",
" #max_grad_norm=max_grad_norm,\n",
" bf16=True,\n",
" use_ipex=True,\n",
" #lr_scheduler_type=\"cosine\",\n",
" load_best_model_at_end=True,\n",
" ddp_find_unused_parameters=False,\n",
" group_by_length=True,\n",
" save_total_limit=save_total_limit,\n",
" logging_steps=logging_steps,\n",
" optim=\"adamw_hf\",\n",
" output_dir=\"./lora_adapters\",\n",
" logging_dir=\"./logs\",\n",
" report_to=\"wandb\" if ENABLE_WANDB else [],\n",
" )\n",
" # Start fine-tuning\n",
" finetuner.finetune(DATA_PATH, training_args)\n",
" except Exception as e:\n",
" logging.error(f\"Error occurred: {e}\")"
]
},
{
"cell_type": "markdown",
"id": "5ddc5dee-498b-498a-9ad8-d03f569e9e9a",
"metadata": {},
"source": [
"We can optionally use Weights & Biases to track our training metrics, uncomment the below cell to enable `wandb`. You will need to pass in your API key when prompted. You can ofcourse skip this step if you'd like to.\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "f1a31697-8bf5-4ec9-9e13-bb7095218fcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"installing wandb...\n",
"installation complete...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-03-24 01:30:49,246 - wandb.jupyter - ERROR - Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n",
"2024-03-24 01:30:54,065 - wandb.sdk.lib.retry - INFO - Retry attempt failed:\n",
"Traceback (most recent call last):\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 793, in urlopen\n",
" response = self._make_request(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 491, in _make_request\n",
" raise new_e\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 467, in _make_request\n",
" self._validate_conn(conn)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 1099, in _validate_conn\n",
" conn.connect()\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connection.py\", line 653, in connect\n",
" sock_and_verified = _ssl_wrap_socket_and_match_hostname(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connection.py\", line 806, in _ssl_wrap_socket_and_match_hostname\n",
" ssl_sock = ssl_wrap_socket(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/ssl_.py\", line 465, in ssl_wrap_socket\n",
" ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/ssl_.py\", line 509, in _ssl_wrap_socket_impl\n",
" return ssl_context.wrap_socket(sock, server_hostname=server_hostname)\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 501, in wrap_socket\n",
" return self.sslsocket_class._create(\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 1074, in _create\n",
" self.do_handshake()\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 1343, in do_handshake\n",
" self._sslobj.do_handshake()\n",
"ConnectionResetError: [Errno 104] Connection reset by peer\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/requests/adapters.py\", line 486, in send\n",
" resp = conn.urlopen(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 847, in urlopen\n",
" retries = retries.increment(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/retry.py\", line 470, in increment\n",
" raise reraise(type(error), error, _stacktrace)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/util.py\", line 38, in reraise\n",
" raise value.with_traceback(tb)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 793, in urlopen\n",
" response = self._make_request(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 491, in _make_request\n",
" raise new_e\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 467, in _make_request\n",
" self._validate_conn(conn)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 1099, in _validate_conn\n",
" conn.connect()\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connection.py\", line 653, in connect\n",
" sock_and_verified = _ssl_wrap_socket_and_match_hostname(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/connection.py\", line 806, in _ssl_wrap_socket_and_match_hostname\n",
" ssl_sock = ssl_wrap_socket(\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/ssl_.py\", line 465, in ssl_wrap_socket\n",
" ssl_sock = _ssl_wrap_socket_impl(sock, context, tls_in_tls, server_hostname)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/urllib3/util/ssl_.py\", line 509, in _ssl_wrap_socket_impl\n",
" return ssl_context.wrap_socket(sock, server_hostname=server_hostname)\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 501, in wrap_socket\n",
" return self.sslsocket_class._create(\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 1074, in _create\n",
" self.do_handshake()\n",
" File \"/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/ssl.py\", line 1343, in do_handshake\n",
" self._sslobj.do_handshake()\n",
"urllib3.exceptions.ProtocolError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))\n",
"\n",
"During handling of the above exception, another exception occurred:\n",
"\n",
"Traceback (most recent call last):\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/wandb/sdk/lib/retry.py\", line 131, in __call__\n",
" result = self._call_fn(*args, **kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/wandb/sdk/internal/internal_api.py\", line 366, in execute\n",
" return self.client.execute(*args, **kwargs) # type: ignore\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/wandb/vendor/gql-0.2.0/wandb_gql/client.py\", line 52, in execute\n",
" result = self._get_result(document, *args, **kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/wandb/vendor/gql-0.2.0/wandb_gql/client.py\", line 60, in _get_result\n",
" return self.transport.execute(document, *args, **kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/wandb/sdk/lib/gql_request.py\", line 58, in execute\n",
" request = self.session.post(self.url, **post_args)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/requests/sessions.py\", line 637, in post\n",
" return self.request(\"POST\", url, data=data, json=json, **kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/requests/sessions.py\", line 589, in request\n",
" resp = self.send(prep, **send_kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/requests/sessions.py\", line 703, in send\n",
" r = adapter.send(request, **kwargs)\n",
" File \"/home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/requests/adapters.py\", line 501, in send\n",
" raise ConnectionError(err, request=request)\n",
"requests.exceptions.ConnectionError: ('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer'))\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: Network error (ConnectionError), entering retry loop.\n",
"\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
]
}
],
"source": [
"if ENABLE_WANDB:\n",
" print(\"installing wandb...\")\n",
" !{sys.executable} -m pip install -U --force \"wandb==0.15.12\" > /dev/null 2>&1\n",
" print(\"installation complete...\")\n",
"\n",
" import wandb\n",
" os.environ[\"WANDB_PROJECT\"] = f\"text-to-sql-finetune-model-name_{BASE_MODEL.replace('/', '_')}\"\n",
" os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\"\n",
" wandb.login()"
]
},
{
"cell_type": "markdown",
"id": "c497cbaf-994b-474b-92f1-fb33fca6b81f",
"metadata": {},
"source": [
"\n",
"---\n",
"\n",
"**Let's Finetune!**\n",
"\n",
"Now it's time to actually fine-tune the model. The `lets_finetune` function below takes care of this. It initializes a FineTuner object with the configurations you've set or left as default."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "1fa45c6d-fac1-4331-8dd5-3ebf9cceed6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[1;34m============================================================\u001b[0m\n",
"\u001b[1;34mTraining Parameters:\u001b[0m\n",
"\u001b[1;34mFoundation model: NousResearch/CodeLlama-7b-hf\u001b[0m\n",
"\u001b[1;34mModel save path: ./final_model\u001b[0m\n",
"\u001b[1;34mDevice used: xpu\u001b[0m\n",
"\u001b[1;34mIntel GPU: Intel(R) Data Center GPU Max 1100\u001b[0m\n",
"\u001b[1;34mBatch size per device: 4\u001b[0m\n",
"\u001b[1;34mGradient accum. steps: 4\u001b[0m\n",
"\u001b[1;34mWarmup steps: 20\u001b[0m\n",
"\u001b[1;34mSave steps: 20\u001b[0m\n",
"\u001b[1;34mEvaluation steps: 20\u001b[0m\n",
"\u001b[1;34mMax steps: 200\u001b[0m\n",
"\u001b[1;34mLearning rate: 2e-05\u001b[0m\n",
"\u001b[1;34mMax gradient norm: 0.3\u001b[0m\n",
"\u001b[1;34mSave total limit: 3\u001b[0m\n",
"\u001b[1;34mLogging steps: 20\u001b[0m\n",
"\u001b[1;34m============================================================\u001b[0m\n",
"\n",
"Attempting to load model and tokenizer from: /home/common/data/Big_Data/GenAI/llm_models/NousResearch--CodeLlama-7b-hf\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "118e0ed053b744399bd7b4509b2269c4",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading checkpoint shards: 0%| | 0/3 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7205d215ca1d4a24a0060e1b728fb7c2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/78477 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b1ffa52982cf4e14bb4e6e415ac8452a",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Map: 0%| | 0/100 [00:00<?, ? examples/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "99e3ee573e8142ffb08379f52023da19",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.011112394375312659, max=1.0…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"wandb: Network error (ConnectTimeout), entering retry loop.\n",
"2024-03-24 01:34:18,614 - root - ERROR - Error in model training: Run initialization has timed out after 90.0 sec. \n",
"Please refer to the documentation for additional information: https://docs.wandb.ai/guides/track/tracking-faq#initstarterror-error-communicating-with-wandb-process-\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Problem at: /home/uafcb7f73a7c1b7b8895a40af90eab07/.local/lib/python3.9/site-packages/transformers/integrations/integration_utils.py 740 setup\n"
]
}
],
"source": [
"lets_finetune()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "1b20eb38-aa96-4b44-8d72-03f0a89dfee6",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "91dee373103542d7b4224662b7f46dd9",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5d94af4c93b5473294d0a7868591cea1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Downloading config.json: 0.00B [00:00, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "OSError",
"evalue": "It looks like the config file at '/home/uafcb7f73a7c1b7b8895a40af90eab07/.cache/huggingface/hub/models--Fredswqa1--DysfunctEcosenseLLM/snapshots/c8a9df5a77a07883e4ee3e59c9ca2683c4189c6b/config.json' is not a valid JSON file.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mJSONDecodeError\u001b[0m Traceback (most recent call last)",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/configuration_utils.py:705\u001b[0m, in \u001b[0;36mPretrainedConfig._get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 704\u001b[0m \u001b[38;5;66;03m# Load config dict\u001b[39;00m\n\u001b[0;32m--> 705\u001b[0m config_dict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dict_from_json_file\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresolved_config_file\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 706\u001b[0m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m commit_hash\n",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/configuration_utils.py:801\u001b[0m, in \u001b[0;36mPretrainedConfig._dict_from_json_file\u001b[0;34m(cls, json_file)\u001b[0m\n\u001b[1;32m 800\u001b[0m text \u001b[38;5;241m=\u001b[39m reader\u001b[38;5;241m.\u001b[39mread()\n\u001b[0;32m--> 801\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mjson\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloads\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m 344\u001b[0m parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m 345\u001b[0m parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[0;32m--> 346\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
"File \u001b[0;32m/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/json/decoder.py:337\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m 333\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the Python representation of ``s`` (a ``str`` instance\u001b[39;00m\n\u001b[1;32m 334\u001b[0m \u001b[38;5;124;03mcontaining a JSON document).\u001b[39;00m\n\u001b[1;32m 335\u001b[0m \n\u001b[1;32m 336\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 337\u001b[0m obj, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraw_decode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_w\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mend\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n",
"File \u001b[0;32m/opt/intel/oneapi/intelpython/envs/pytorch-gpu/lib/python3.9/json/decoder.py:355\u001b[0m, in \u001b[0;36mJSONDecoder.raw_decode\u001b[0;34m(self, s, idx)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n\u001b[0;32m--> 355\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpecting value\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, err\u001b[38;5;241m.\u001b[39mvalue) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj, end\n",
"\u001b[0;31mJSONDecodeError\u001b[0m: Expecting value: line 1 column 1 (char 0)",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[11], line 14\u001b[0m\n\u001b[1;32m 11\u001b[0m checkpoint_path \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFredswqa1/DysfunctEcosenseLLM\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;66;03m# Replace with your checkpoint folder\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;66;03m# Load the model\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mAutoModelForSequenceClassification\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcheckpoint_path\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# Load the tokenizer\u001b[39;00m\n\u001b[1;32m 17\u001b[0m tokenizer \u001b[38;5;241m=\u001b[39m AutoTokenizer\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLlamaTokenizer\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;66;03m#add name of your model's tokenizer on Hugging Face OR custom tokenizer\u001b[39;00m\n",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/models/auto/auto_factory.py:525\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 523\u001b[0m _ \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 525\u001b[0m config, kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mAutoConfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_pretrained\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 526\u001b[0m \u001b[43m \u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 527\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_unused_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m 528\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrust_remote_code\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 529\u001b[0m \u001b[43m \u001b[49m\u001b[43mcode_revision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcode_revision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 530\u001b[0m \u001b[43m \u001b[49m\u001b[43m_commit_hash\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_hash\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 531\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mhub_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 532\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 533\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 535\u001b[0m \u001b[38;5;66;03m# if torch_dtype=auto was passed here, ensure to pass it on\u001b[39;00m\n\u001b[1;32m 536\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs_orig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/models/auto/configuration_auto.py:1034\u001b[0m, in \u001b[0;36mAutoConfig.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1031\u001b[0m trust_remote_code \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrust_remote_code\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m 1032\u001b[0m code_revision \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcode_revision\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m-> 1034\u001b[0m config_dict, unused_kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mPretrainedConfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_config_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1035\u001b[0m has_remote_code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAutoConfig\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto_map\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 1036\u001b[0m has_local_code \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01min\u001b[39;00m CONFIG_MAPPING\n",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/configuration_utils.py:620\u001b[0m, in \u001b[0;36mPretrainedConfig.get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 618\u001b[0m original_kwargs \u001b[38;5;241m=\u001b[39m copy\u001b[38;5;241m.\u001b[39mdeepcopy(kwargs)\n\u001b[1;32m 619\u001b[0m \u001b[38;5;66;03m# Get config dict associated with the base config file\u001b[39;00m\n\u001b[0;32m--> 620\u001b[0m config_dict, kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_config_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpretrained_model_name_or_path\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 621\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict:\n\u001b[1;32m 622\u001b[0m original_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
"File \u001b[0;32m~/.local/lib/python3.9/site-packages/transformers/configuration_utils.py:708\u001b[0m, in \u001b[0;36mPretrainedConfig._get_config_dict\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 706\u001b[0m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_commit_hash\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m commit_hash\n\u001b[1;32m 707\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (json\u001b[38;5;241m.\u001b[39mJSONDecodeError, \u001b[38;5;167;01mUnicodeDecodeError\u001b[39;00m):\n\u001b[0;32m--> 708\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mEnvironmentError\u001b[39;00m(\n\u001b[1;32m 709\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIt looks like the config file at \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresolved_config_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not a valid JSON file.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 710\u001b[0m )\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_local:\n\u001b[1;32m 713\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mloading configuration file \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresolved_config_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
"\u001b[0;31mOSError\u001b[0m: It looks like the config file at '/home/uafcb7f73a7c1b7b8895a40af90eab07/.cache/huggingface/hub/models--Fredswqa1--DysfunctEcosenseLLM/snapshots/c8a9df5a77a07883e4ee3e59c9ca2683c4189c6b/config.json' is not a valid JSON file."
]
}
],
"source": [
"# Logging in to Hugging Face\n",
"from huggingface_hub import notebook_login, Repository\n",
"\n",
"# Login to Hugging Face\n",
"notebook_login()\n",
"\n",
"# Model and Tokenize Loading\n",
"from transformers import AutoModelForSequenceClassification, AutoTokenizer\n",
"\n",
"# Define the path to the checkpoint\n",
"checkpoint_path = \"Fredswqa1/DysfunctEcosenseLLM\" # Replace with your checkpoint folder\n",
"\n",
"# Load the model\n",
"model = AutoModelForSequenceClassification.from_pretrained(checkpoint_path)\n",
"\n",
"# Load the tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(\"LlamaTokenizer\") #add name of your model's tokenizer on Hugging Face OR custom tokenizer\n",
"\n",
"# Save the model and tokenizer\n",
"model_name_on_hub = \"EcoSense-LLMChat\"\n",
"model.save_pretrained(model_name_on_hub)\n",
"tokenizer.save_pretrained(model_name_on_hub)\n",
"\n",
"# Push to the hub\n",
"model.push_to_hub(model_name_on_hub)\n",
"tokenizer.push_to_hub(model_name_on_hub)\n",
"\n",
"# Congratulations! Your fine-tuned model is now uploaded to the Hugging Face Model Hub. \n",
"# You can view and share your model using its URL: https://huggingface.co/<your-username>/<your-model-name>"
]
},
{
"cell_type": "markdown",
"id": "39613af7-3408-4412-8ded-aa6116759f06",
"metadata": {},
"source": [
"\n",
"**Testing our Fine-Tuned LLM**\n",
"\n",
"Congratulations on successfully fine-tuning your Language Model for Text-to-SQL tasks! It's now time to put the model to the test.\n",
"\n",
"___"
]
},
{
"cell_type": "markdown",
"id": "00cb638e-1464-4596-8809-186834b3f277",
"metadata": {},
"source": [
"**TextToSQLGenerator: Generating SQL Queries from Text Prompts**\n",
"\n",
"**Important Note**: Remember to re-import necessary packages and re-define `BASE_MODELS` by rerunning relevant cells if the Jupyter kernel is restarted.\n",
"\n",
"**Overview of `TextToSQLGenerator`**\n",
"- Designed for generating SQL queries from natural language prompts.\n",
"- Allows model selection at initialization.\n",
"\n",
"**Initialization and Configuration:**\n",
"- Set `use_adapter` to `True` for using the fine-tuned LoRA model; defaults to the base model otherwise.\n",
"- Automatic tokenizer selection based on the model ID, with special handling for 'llama' models.\n",
"- Optimized loading for CPU / XPUs (`low_cpu_mem_usage`, `load_in_4bit`).\n",
"- For LoRA models, loads fine-tuned checkpoints for inference.\n",
"\n",
"**Generating SQL Queries:**\n",
"\n",
"The `generate` method is where the actual translation occurs. Given a text prompt, the method encodes the prompt using the tokenizer, ensuring that it fits within the model's maximum length constraints. It then performs inference to generate the SQL query.\n",
"\n",
"The method parameters like `temperature` and `repetition_penalty` which we can tweak to control the creativity and quality of the generated queries!"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "10a0e504-46b7-4c6e-8d80-b28798607ed6",
"metadata": {},
"outputs": [],
"source": [
"os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
"INFERENCE_DEVICE = torch.device(\"xpu\") # change this to `xpu` to use Intel GPU for inference \n",
"\n",
"def generate_prompt_sql(input_question, context, output=\"\"):\n",
" \"\"\"\n",
" Generates a prompt for fine-tuning the LLM model for text-to-SQL tasks.\n",
"\n",
" Parameters:\n",
" input_question (str): The input text or question to be converted to SQL.\n",
" context (str): The schema or context in which the SQL query operates.\n",
" output (str, optional): The expected SQL query as the output.\n",
"\n",
" Returns:\n",
" str: A formatted string serving as the prompt for the fine-tuning task.\n",
" \"\"\"\n",
" return f\"\"\"You are a powerful text-to-SQL model. Your job is to answer questions about a database. You are given a question and context regarding one or more tables. \n",
"\n",
"You must output the SQL query that answers the question.\n",
"\n",
"### Input:\n",
"{input_question}\n",
"\n",
"### Context:\n",
"{context}\n",
"\n",
"### Response:\n",
"{output}\"\"\"\n",
"\n",
"\n",
"def setup_model_and_tokenizer(base_model_id: str):\n",
" \"\"\"Downloads / Loads the pre-trained model and tokenizer in nf4 based on the given base model ID for training, \n",
" with fallbacks for permission errors to use default cache.\"\"\"\n",
" local_model_id = base_model_id.replace(\"/\", \"--\")\n",
" local_model_path = os.path.join(MODEL_CACHE_PATH, local_model_id)\n",
"\n",
" bnb_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=False,\n",
" bnb_4bit_quant_type=\"nf4\",\n",
" bnb_4bit_compute_dtype=torch.bfloat16\n",
" )\n",
" try:\n",
" print(f\"Attempting to load model and tokenizer from: {local_model_path}\")\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" local_model_path,\n",
" quantization_config=bnb_config\n",
" )\n",
" tokenizer_class = LlamaTokenizer if \"llama\" in base_model_id.lower() else AutoTokenizer\n",
" tokenizer = tokenizer_class.from_pretrained(local_model_path)\n",
" except (OSError, PermissionError) as e:\n",
" print(f\"Failed to load from {local_model_path} due to {e}. Attempting to download...\")\n",
" model = AutoModelForCausalLM.from_pretrained(\n",
" base_model_id, \n",
" quantization_config=bnb_config\n",
" )\n",
" tokenizer_class = LlamaTokenizer if \"llama\" in base_model_id.lower() else AutoTokenizer\n",
" tokenizer = tokenizer_class.from_pretrained(base_model_id)\n",
"\n",
" tokenizer.pad_token_id = 0\n",
" tokenizer.padding_side = \"left\"\n",
" return model.to(INFERENCE_DEVICE), tokenizer\n",
"\n",
"class TextToSQLGenerator:\n",
" \"\"\"Handles SQL query generation for a given text prompt.\"\"\"\n",
"\n",
" def __init__(\n",
" self, base_model_id=BASE_MODEL, use_adapter=False, lora_checkpoint=None, loaded_base_model=None\n",
" ):\n",
" \"\"\"\n",
" Initialize the InferenceModel class.\n",
" Parameters:\n",
" use_adapter (bool, optional): Whether to use LoRA model. Defaults to False.\n",
" \"\"\"\n",
" try:\n",
" if loaded_base_model:\n",
" self.model = loaded_base_model.model\n",
" self.tokenizer = loaded_base_model.tokenizer\n",
" else:\n",
" self.model, self.tokenizer = setup_model_and_tokenizer(base_model_id)\n",
" if use_adapter:\n",
" self.model = PeftModel.from_pretrained(self.model, lora_checkpoint)\n",
" except Exception as e:\n",
" logging.error(f\"Exception occurred during model initialization: {e}\")\n",
" raise\n",
"\n",
" self.model.to(INFERENCE_DEVICE)\n",
" self.max_length = 512\n",
"\n",
"\n",
" def generate(self, prompt, **kwargs):\n",
" \"\"\"Generates an SQL query based on the given prompt.\n",
" Parameters:\n",
" prompt (str): The SQL prompt.\n",
" Returns:\n",
" str: The generated SQL query.\n",
" \"\"\"\n",
" try:\n",
" encoded_prompt = self.tokenizer(\n",
" prompt,\n",
" truncation=True,\n",
" max_length=self.max_length,\n",
" padding=False,\n",
" return_tensors=\"pt\",\n",
" ).input_ids.to(INFERENCE_DEVICE)\n",
" with torch.no_grad():\n",
" with torch.xpu.amp.autocast():\n",
" outputs = self.model.generate(\n",
" input_ids=encoded_prompt,\n",
" do_sample=True,\n",
" max_length=self.max_length,\n",
" temperature=0.3,\n",
" repetition_penalty=1.2,\n",
" )\n",
" generated = self.tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
" return generated\n",
" except Exception as e:\n",
" logging.error(f\"Exception occurred during query generation: {e}\")\n",
" raise"
]
},
{
"cell_type": "markdown",
"id": "dfb1c247-f6e2-4ccd-bdc6-26f41ea63c55",
"metadata": {},
"source": [
"---\n",
"**Generate SQL from Natural Language!** 🚀 \n",
"\n",
"**With `TextToSQLGenerator`:**\n",
"- Compare base model 🆚 LoRA model.\n",
"- Instantiate with different `use_adapter` settings for side-by-side comparison.\n",
"\n",
"**Things to try out:**\n",
"\n",
"1. **Select a Natural Language Question** 🗣️: Use a prompt or sample data (see samples dict below) for SQL translation.\n",
"2. **Base Model SQL Generation** 🏗️: Generate SQL from the prompt using the base model.\n",
"3. **Fine-Tuned Model SQL Generation** ✨: Generate SQL with the fine-tuned model; note improvements.\n",
"4. **Compare Outputs** 🔍: Evaluate both SQL queries for accuracy to compare both models.\n",
"5. **Iterate and Refine** 🔁: Adjust training parameters or dataset and finetune again if required.\n",
"6. **Integrate with 🗂️ LlamaIndex 🦙**: Use frameworks like [LlamaIndex](https://github.com/run-llama/llama_index) to integrated your finetuned model for querying a database using natural language.\n"
]
},
{
"cell_type": "markdown",
"id": "94c63bc1-9e86-4760-ac9b-c915969ee5fb",
"metadata": {},
"source": [
"**Now let's see how our model performance, Let's generate some SQL queries:**"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dd2c6f25-5c45-4061-9b62-91233149b7a2",
"metadata": {},
"outputs": [],
"source": [
"# lets load base model for a baseline comparison\n",
"base_model = TextToSQLGenerator(\n",
" use_adapter=False,\n",
" lora_checkpoint=\"\",\n",
") # setting use_adapter=False to use the base model\n",
"finetuned_model = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8417146-0c28-4996-81a4-c4b0857d81e7",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"\n",
"from IPython.display import display, HTML\n",
"\n",
"\n",
"# let's use some fake sample data\n",
"samples = \"\"\"\n",
"[\n",
" {\n",
" \"question\": \"What is the capacity of the stadium where the team 'Mountain Eagles' plays?\",\n",
" \"context\": \"CREATE TABLE stadium_info (team_name VARCHAR, stadium_name VARCHAR, capacity INT)\"\n",
" },\n",
" {\n",
" \"question\": \"How many goals did player John Smith score last season?\",\n",
" \"context\": \"CREATE TABLE player_stats (player_name VARCHAR, goals_scored INT, season VARCHAR)\"\n",
" },\n",
" {\n",
" \"question\": \"What are the operating hours for the Central Library on weekends?\",\n",
" \"context\": \"CREATE TABLE library_hours (library_name VARCHAR, day_of_week VARCHAR, open_time TIME, close_time TIME)\"\n",
" }\n",
"]\n",
"\"\"\"\n",
"\n",
"def _extract_sections(output):\n",
" input_section = output.split(\"### Input:\")[1].split(\"### Context:\")[0]\n",
" context_section = output.split(\"### Context:\")[1].split(\"### Response:\")[0]\n",
" response_section = output.split(\"### Response:\")[1]\n",
" return input_section, context_section, response_section\n",
"\n",
"def run_inference(sample_data, model, finetuned=False):\n",
" if INFERENCE_DEVICE.type.startswith(\"xpu\"):\n",
" torch.xpu.empty_cache()\n",
" \n",
" color = \"#4CAF52\" if finetuned else \"#2196F4\"\n",
" model_type = \"finetuned\" if finetuned else \"base\"\n",
" display(HTML(f\"<div style='color:{color};'>Processing queries on {INFERENCE_DEVICE} please wait...</div>\"))\n",
" \n",
" for index, row in enumerate(sample_data):\n",
" try:\n",
" prompt = generate_prompt_sql(row[\"question\"], context=row[\"context\"])\n",
" output = model.generate(prompt) \n",
" input_section, context_section, response_section = _extract_sections(output)\n",
" \n",
" tabbed_output = f\"\"\"\n",
" <details>\n",
" <summary style='color: {color};'><b>{model_type} model - Sample {index+1}</b> (Click to expand)</summary>\n",
" <div style='padding-left: 20px;'>\n",
" <p><b>Expected input 📝:</b><br>{input_section}</p>\n",
" <p><b>Expected context 📚:</b><br>{context_section}</p>\n",
" <p><b>Generated response 💡:</b><br>{response_section}</p>\n",
" </div>\n",
" </details>\n",
" <hr style='border-top: 1px solid #bbb;'>\"\"\" # Subtle separator\n",
" display(HTML(tabbed_output))\n",
" except Exception as e:\n",
" logging.error(f\"Exception occurred during sample processing: {e}\")\n",
"\n",
"# checkpoints are saved to `./lora_adapters`.\n",
"# Update the USING_CHECKPOINT to the one you want to use.\n",
"USING_CHECKPOINT=200\n",
"# if the kernel is interrupted the latest adapter (LORA_CHECKPOINT) is `./final_model_interrupted/`\n",
"# or else, the final model LORA_CHECKPOINT is `./final_model`\n",
"LORA_CHECKPOINT = f\"./lora_adapters/checkpoint-{USING_CHECKPOINT}/\"\n",
"\n",
"if os.path.exists(LORA_CHECKPOINT):\n",
" sample_data = json.loads(samples)\n",
" run_inference(sample_data, model=base_model)\n",
" if not finetuned_model:\n",
" finetuned_model = TextToSQLGenerator(\n",
" use_adapter=True,\n",
" lora_checkpoint=LORA_CHECKPOINT,\n",
" loaded_base_model=base_model\n",
" )\n",
" run_inference(sample_data, model=finetuned_model, finetuned=True)\n",
"\n",
" # To conserve memory we can delete the model\n",
" #del finetuned_model\n",
" #del base_model"
]
},
{
"cell_type": "markdown",
"id": "11c2506c",
"metadata": {},
"source": [
"---\n",
"**Conclusion** 👏\n",
"\n",
"We've successfully navigated the process of selecting and fine-tuning a foundational LLM model on Intel GPUs, showcasing its SQL generation capabilities. I hope that I have been able to highlight the potential of customizing language models for specific tasks and on how to efficiently finetune LLMs on Intel XPUs. As a suggestion for your continued journey, consider experimenting with different models, adjusting inference settings, and exploring various LoRA configurations to refine your results. Keep exploring!\n",
"\n",
"---\n"
]
},
{
"cell_type": "markdown",
"id": "c458cac4",
"metadata": {
"jp-MarkdownHeadingCollapsed": true
},
"source": [
"\n",
"**Disclaimer for Using Large Language Models**\n",
"\n",
"Please be aware that while Large Language Models are powerful tools for text generation, they may sometimes produce results that are unexpected, biased, or inconsistent with the given prompt. It's advisable to carefully review the generated text and consider the context and application in which you are using these models.\n",
"\n",
"For detailed information on each model's capabilities, licensing, and attribution, please refer to the respective model cards:\n",
"\n",
"1. **Open LLaMA 3B v2**\n",
" - Model Card: [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2)\n",
"\n",
"2. **Open LLaMA 13B**\n",
" - Model Card: [openlm-research/open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)\n",
"\n",
"3. **Nous-Hermes LLaMA 2-7B**\n",
" - Model Card: [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)\n",
"\n",
"4. **LLaMA 2-7B Chat HF**\n",
" - Model Card: [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf)\n",
"\n",
"5. **LLaMA 2-13B HF**\n",
" - Model Card: [NousResearch/Llama-2-13b-hf](https://huggingface.co/NousResearch/Llama-2-13b-hf)\n",
"\n",
"6. **CodeLlama 7B HF**\n",
" - Model Card: [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf)\n",
"\n",
"7. **Phind-CodeLlama 34B v2**\n",
" - Model Card: [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2)\n",
"\n",
"8. **Zephyr-7b-beta**\n",
" - Model Card: [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)\n",
"\n",
"\n",
"Usage of these models must also adhere to the licensing agreements and be in accordance with ethical guidelines and best practices for AI. If you have any concerns or encounter issues with the models, please refer to the respective model cards and documentation provided in the links above.\n",
"To the extent that any public or non-Intel datasets or models are referenced by or accessed using these materials those datasets or models are provided by the third party indicated as the content source. Intel does not create the content and does not warrant its accuracy or quality. By accessing the public content, or using materials trained on or with such content, you agree to the terms associated with that content and that your use complies with the applicable license.\n",
"\n",
" \n",
"Intel expressly disclaims the accuracy, adequacy, or completeness of any such public content, and is not liable for any errors, omissions, or defects in the content, or for any reliance on the content. Intel is not liable for any liability or damages relating to your use of public content.\n",
"\n",
"Intel’s provision of these resources does not expand or otherwise alter Intel’s applicable published warranties or warranty disclaimers for Intel products or solutions, and no additional obligations, indemnifications, or liabilities arise from Intel providing such resources. Intel reserves the right, without notice, to make corrections, enhancements, improvements, and other changes to its materials.\n",
"\n",
"---\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "pytorch-gpu",
"language": "python",
"name": "pytorch-gpu"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.18"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|