ishwarraja commited on
Commit
4d66dc6
·
verified ·
1 Parent(s): bab4efe

Upload initial character-level GPT model

Browse files
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+ 2025-11-20 14:30:17,665 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool ═╣
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+ 2025-11-20 14:30:17,678 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-20250627154417.tar.gz HTTP/1.1" 200 12962791
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+
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371
+
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+ 2025-11-20 14:30:17,752 INFO ___FILE_ONLY___ ╠═ Downloading: Cloud Storage Command Line Tool (Platfor... ═╣
373
+
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+ 2025-11-20 14:30:17,752 INFO ___FILE_ONLY___ ╚
375
+ 2025-11-20 14:30:17,755 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
376
+ 2025-11-20 14:30:17,793 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gsutil-nix-20251031202127.tar.gz HTTP/1.1" 200 1949
377
+ 2025-11-20 14:30:17,793 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
378
+ 2025-11-20 14:30:17,793 INFO ___FILE_ONLY___ ╝
379
+
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381
+
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383
+
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+ 2025-11-20 14:30:17,795 INFO ___FILE_ONLY___ ╚
385
+ 2025-11-20 14:30:17,795 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
386
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387
+
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389
+
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+ 2025-11-20 14:30:17,797 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CLI Core Libraries (Platfor... ═╣
391
+
392
+ 2025-11-20 14:30:17,797 INFO ___FILE_ONLY___ ╚
393
+ 2025-11-20 14:30:17,800 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
394
+ 2025-11-20 14:30:17,810 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-core-nix-20251031202127.tar.gz HTTP/1.1" 200 2326
395
+ 2025-11-20 14:30:17,811 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
396
+ 2025-11-20 14:30:17,811 INFO ___FILE_ONLY___ ╝
397
+
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399
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+ 2025-11-20 14:30:17,813 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool ═╣
401
+
402
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403
+ 2025-11-20 14:30:17,813 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
404
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405
+
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407
+
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+ 2025-11-20 14:30:17,815 INFO ___FILE_ONLY___ ╠═ Downloading: Google Cloud CRC32C Hash Tool (Platform ... ═╣
409
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410
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411
+ 2025-11-20 14:30:17,817 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
412
+ 2025-11-20 14:30:17,831 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-crc32c-linux-x86_64-20251031202127.tar.gz HTTP/1.1" 200 1531280
413
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414
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419
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420
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421
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425
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427
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429
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430
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431
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432
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439
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442
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443
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444
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445
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446
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448
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450
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451
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452
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453
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454
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455
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456
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457
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458
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459
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460
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461
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462
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463
+ 2025-11-20 14:30:17,846 INFO ___FILE_ONLY___ ═
464
+ 2025-11-20 14:30:17,846 INFO ___FILE_ONLY___ ═
465
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
466
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
467
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
468
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
469
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
470
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
471
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
472
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ═
473
+ 2025-11-20 14:30:17,847 INFO ___FILE_ONLY___ ╝
474
+
475
+ 2025-11-20 14:30:17,849 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
476
+
477
+ 2025-11-20 14:30:17,849 INFO ___FILE_ONLY___ ╠═ Downloading: gcloud cli dependencies (Platform Specific) ═╣
478
+
479
+ 2025-11-20 14:30:17,849 INFO ___FILE_ONLY___ ╚
480
+ 2025-11-20 14:30:17,852 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
481
+ 2025-11-20 14:30:17,863 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components/google-cloud-sdk-gcloud-deps-linux-x86_64-20210416153011.tar.gz HTTP/1.1" 200 104
482
+ 2025-11-20 14:30:17,863 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
483
+ 2025-11-20 14:30:17,863 INFO ___FILE_ONLY___ ╝
484
+
485
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486
+
487
+ 2025-11-20 14:30:17,865 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool ═╣
488
+
489
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490
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491
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492
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499
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500
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501
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502
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503
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504
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506
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507
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508
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509
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510
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511
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515
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516
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519
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523
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526
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527
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528
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529
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530
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531
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532
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533
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534
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535
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536
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537
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541
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542
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543
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544
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545
+ 2025-11-20 14:30:18,101 INFO ___FILE_ONLY___ ═
546
+ 2025-11-20 14:30:18,104 INFO ___FILE_ONLY___ ═
547
+ 2025-11-20 14:30:18,106 INFO ___FILE_ONLY___ ═
548
+ 2025-11-20 14:30:18,108 INFO ___FILE_ONLY___ ═
549
+ 2025-11-20 14:30:18,110 INFO ___FILE_ONLY___ ═
550
+ 2025-11-20 14:30:18,110 INFO ___FILE_ONLY___ ╝
551
+
552
+ 2025-11-20 14:30:18,118 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
553
+
554
+ 2025-11-20 14:30:18,118 INFO ___FILE_ONLY___ ╠═ Installing: BigQuery Command Line Tool (Platform Spec... ═╣
555
+
556
+ 2025-11-20 14:30:18,118 INFO ___FILE_ONLY___ ╚
557
+ 2025-11-20 14:30:18,119 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
558
+ 2025-11-20 14:30:18,119 INFO ___FILE_ONLY___ ╝
559
+
560
+ 2025-11-20 14:30:18,123 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
561
+
562
+ 2025-11-20 14:30:18,123 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.13 ═╣
563
+
564
+ 2025-11-20 14:30:18,123 INFO ___FILE_ONLY___ ╚
565
+ 2025-11-20 14:30:18,126 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
566
+ 2025-11-20 14:30:18,126 INFO ___FILE_ONLY___ ╝
567
+
568
+ 2025-11-20 14:30:18,128 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
569
+
570
+ 2025-11-20 14:30:18,128 INFO ___FILE_ONLY___ ╠═ Installing: Bundled Python 3.13 (Platform Specific) ═╣
571
+
572
+ 2025-11-20 14:30:18,128 INFO ___FILE_ONLY___ ╚
573
+ 2025-11-20 14:30:21,213 INFO ___FILE_ONLY___ ═
574
+ 2025-11-20 14:30:21,228 INFO ___FILE_ONLY___ ═
575
+ 2025-11-20 14:30:21,242 INFO ___FILE_ONLY___ ═
576
+ 2025-11-20 14:30:23,539 INFO ___FILE_ONLY___ ═
577
+ 2025-11-20 14:30:23,564 INFO ___FILE_ONLY___ ═
578
+ 2025-11-20 14:30:23,593 INFO ___FILE_ONLY___ ═
579
+ 2025-11-20 14:30:23,613 INFO ___FILE_ONLY___ ═
580
+ 2025-11-20 14:30:23,643 INFO ___FILE_ONLY___ ═
581
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582
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583
+ 2025-11-20 14:30:23,770 INFO ___FILE_ONLY___ ═
584
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585
+ 2025-11-20 14:30:23,816 INFO ___FILE_ONLY___ ═
586
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587
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588
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589
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590
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591
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592
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593
+ 2025-11-20 14:30:23,942 INFO ___FILE_ONLY___ ═
594
+ 2025-11-20 14:30:23,960 INFO ___FILE_ONLY___ ═
595
+ 2025-11-20 14:30:23,975 INFO ___FILE_ONLY___ ═
596
+ 2025-11-20 14:30:24,123 INFO ___FILE_ONLY___ ═
597
+ 2025-11-20 14:30:24,597 INFO ___FILE_ONLY___ ═
598
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599
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600
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601
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602
+ 2025-11-20 14:30:24,682 INFO ___FILE_ONLY___ ═
603
+ 2025-11-20 14:30:24,695 INFO ___FILE_ONLY___ ═
604
+ 2025-11-20 14:30:24,709 INFO ___FILE_ONLY___ ═
605
+ 2025-11-20 14:30:24,723 INFO ___FILE_ONLY___ ═
606
+ 2025-11-20 14:30:24,736 INFO ___FILE_ONLY___ ═
607
+ 2025-11-20 14:30:24,749 INFO ___FILE_ONLY___ ═
608
+ 2025-11-20 14:30:24,762 INFO ___FILE_ONLY___ ═
609
+ 2025-11-20 14:30:24,775 INFO ___FILE_ONLY___ ═
610
+ 2025-11-20 14:30:24,788 INFO ___FILE_ONLY___ ═
611
+ 2025-11-20 14:30:24,801 INFO ___FILE_ONLY___ ═
612
+ 2025-11-20 14:30:24,815 INFO ___FILE_ONLY___ ═
613
+ 2025-11-20 14:30:24,828 INFO ___FILE_ONLY___ ═
614
+ 2025-11-20 14:30:24,842 INFO ___FILE_ONLY___ ═
615
+ 2025-11-20 14:30:24,855 INFO ___FILE_ONLY___ ═
616
+ 2025-11-20 14:30:24,868 INFO ___FILE_ONLY___ ═
617
+ 2025-11-20 14:30:24,881 INFO ___FILE_ONLY___ ═
618
+ 2025-11-20 14:30:24,894 INFO ___FILE_ONLY___ ═
619
+ 2025-11-20 14:30:24,908 INFO ___FILE_ONLY___ ═
620
+ 2025-11-20 14:30:24,921 INFO ___FILE_ONLY___ ═
621
+ 2025-11-20 14:30:24,934 INFO ___FILE_ONLY___ ═
622
+ 2025-11-20 14:30:24,947 INFO ___FILE_ONLY___ ═
623
+ 2025-11-20 14:30:24,960 INFO ___FILE_ONLY___ ═
624
+ 2025-11-20 14:30:24,974 INFO ___FILE_ONLY___ ═
625
+ 2025-11-20 14:30:24,987 INFO ___FILE_ONLY___ ═
626
+ 2025-11-20 14:30:25,000 INFO ___FILE_ONLY___ ═
627
+ 2025-11-20 14:30:25,014 INFO ___FILE_ONLY___ ═
628
+ 2025-11-20 14:30:25,027 INFO ___FILE_ONLY___ ═
629
+ 2025-11-20 14:30:25,041 INFO ___FILE_ONLY___ ═
630
+ 2025-11-20 14:30:25,054 INFO ___FILE_ONLY___ ═
631
+ 2025-11-20 14:30:25,067 INFO ___FILE_ONLY___ ═
632
+ 2025-11-20 14:30:25,080 INFO ___FILE_ONLY___ ═
633
+ 2025-11-20 14:30:25,081 INFO ___FILE_ONLY___ ╝
634
+
635
+ 2025-11-20 14:30:25,139 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
636
+
637
+ 2025-11-20 14:30:25,139 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool ═╣
638
+
639
+ 2025-11-20 14:30:25,139 INFO ___FILE_ONLY___ ╚
640
+ 2025-11-20 14:30:25,678 INFO ___FILE_ONLY___ ═
641
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642
+ 2025-11-20 14:30:25,699 INFO ___FILE_ONLY___ ═
643
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644
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645
+ 2025-11-20 14:30:25,749 INFO ___FILE_ONLY___ ═
646
+ 2025-11-20 14:30:25,762 INFO ___FILE_ONLY___ ═
647
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648
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649
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650
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651
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652
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653
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654
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655
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656
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657
+ 2025-11-20 14:30:25,893 INFO ___FILE_ONLY___ ═
658
+ 2025-11-20 14:30:25,904 INFO ___FILE_ONLY___ ═
659
+ 2025-11-20 14:30:25,914 INFO ___FILE_ONLY___ ═
660
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661
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662
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663
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664
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665
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666
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667
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668
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669
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670
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671
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672
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673
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674
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675
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676
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677
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678
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679
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680
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681
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682
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683
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684
+ 2025-11-20 14:30:26,225 INFO ___FILE_ONLY___ ═
685
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686
+ 2025-11-20 14:30:26,243 INFO ___FILE_ONLY___ ═
687
+ 2025-11-20 14:30:26,251 INFO ___FILE_ONLY___ ═
688
+ 2025-11-20 14:30:26,260 INFO ___FILE_ONLY___ ═
689
+ 2025-11-20 14:30:26,272 INFO ___FILE_ONLY___ ═
690
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691
+ 2025-11-20 14:30:26,297 INFO ___FILE_ONLY___ ═
692
+ 2025-11-20 14:30:26,309 INFO ___FILE_ONLY___ ═
693
+ 2025-11-20 14:30:26,322 INFO ___FILE_ONLY___ ═
694
+ 2025-11-20 14:30:26,345 INFO ___FILE_ONLY___ ═
695
+ 2025-11-20 14:30:26,359 INFO ___FILE_ONLY___ ═
696
+ 2025-11-20 14:30:26,373 INFO ___FILE_ONLY___ ═
697
+ 2025-11-20 14:30:26,393 INFO ___FILE_ONLY___ ═
698
+ 2025-11-20 14:30:26,409 INFO ___FILE_ONLY___ ═
699
+ 2025-11-20 14:30:26,426 INFO ___FILE_ONLY___ ═
700
+ 2025-11-20 14:30:26,426 INFO ___FILE_ONLY___ ╝
701
+
702
+ 2025-11-20 14:30:26,456 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
703
+
704
+ 2025-11-20 14:30:26,456 INFO ___FILE_ONLY___ ╠═ Installing: Cloud Storage Command Line Tool (Platform... ═╣
705
+
706
+ 2025-11-20 14:30:26,456 INFO ___FILE_ONLY___ ╚
707
+ 2025-11-20 14:30:26,457 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
708
+ 2025-11-20 14:30:26,457 INFO ___FILE_ONLY___ ╝
709
+
710
+ 2025-11-20 14:30:26,461 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
711
+
712
+ 2025-11-20 14:30:26,461 INFO ___FILE_ONLY___ ╠═ Installing: Default set of gcloud commands ═╣
713
+
714
+ 2025-11-20 14:30:26,461 INFO ___FILE_ONLY___ ╚
715
+ 2025-11-20 14:30:26,463 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
716
+ 2025-11-20 14:30:26,463 INFO ___FILE_ONLY___ ╝
717
+
718
+ 2025-11-20 14:30:26,465 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
719
+
720
+ 2025-11-20 14:30:26,465 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CLI Core Libraries (Platform... ═╣
721
+
722
+ 2025-11-20 14:30:26,465 INFO ___FILE_ONLY___ ╚
723
+ 2025-11-20 14:30:26,466 INFO ___FILE_ONLY___ ══════════════════════════════
724
+ 2025-11-20 14:30:26,466 INFO ___FILE_ONLY___ ══════════════════════════════
725
+ 2025-11-20 14:30:26,467 INFO ___FILE_ONLY___ ╝
726
+
727
+ 2025-11-20 14:30:26,470 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
728
+
729
+ 2025-11-20 14:30:26,471 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool ═╣
730
+
731
+ 2025-11-20 14:30:26,471 INFO ___FILE_ONLY___ ╚
732
+ 2025-11-20 14:30:26,473 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
733
+ 2025-11-20 14:30:26,473 INFO ___FILE_ONLY___ ╝
734
+
735
+ 2025-11-20 14:30:26,475 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
736
+
737
+ 2025-11-20 14:30:26,475 INFO ___FILE_ONLY___ ╠═ Installing: Google Cloud CRC32C Hash Tool (Platform S... ═╣
738
+
739
+ 2025-11-20 14:30:26,475 INFO ___FILE_ONLY___ ╚
740
+ 2025-11-20 14:30:26,510 INFO ___FILE_ONLY___ ══════════════════════════════
741
+ 2025-11-20 14:30:26,510 INFO ___FILE_ONLY___ ══════════════════════════════
742
+ 2025-11-20 14:30:26,510 INFO ___FILE_ONLY___ ╝
743
+
744
+ 2025-11-20 14:30:26,515 INFO ___FILE_ONLY___ ╔════════════════════════════════════════════════════════════╗
745
+
746
+ 2025-11-20 14:30:26,515 INFO ___FILE_ONLY___ ╠═ Installing: gcloud cli dependencies (Platform Specific) ═╣
747
+
748
+ 2025-11-20 14:30:26,515 INFO ___FILE_ONLY___ ╚
749
+ 2025-11-20 14:30:26,515 INFO ___FILE_ONLY___ ════════════════════════════════════════════════════════════
750
+ 2025-11-20 14:30:26,515 INFO ___FILE_ONLY___ ╝
751
+
752
+ 2025-11-20 14:30:26,520 DEBUG root Updating notification cache...
753
+ 2025-11-20 14:30:26,521 INFO ___FILE_ONLY___
754
+
755
+ 2025-11-20 14:30:26,522 INFO ___FILE_ONLY___ Performing post processing steps...
756
+ 2025-11-20 14:30:26,523 DEBUG root Executing command: ['/tools/google-cloud-sdk/bin/gcloud', 'components', 'post-process']
757
+ 2025-11-20 14:30:34,735 DEBUG ___FILE_ONLY___
758
+ 2025-11-20 14:30:34,736 DEBUG ___FILE_ONLY___
759
+ 2025-11-20 14:30:34,791 INFO root descriptor_list: [{'universeDomain': 'googleapis.com', 'universeShortName': '', 'authenticationDomain': 'auth.cloud.google.com', 'projectPrefix': '', 'cloudWebDomain': 'cloud.google.com', 'documentationDomain': 'cloud.google.com', 'version': '1.0.0', 'state': 'primary', 'artifactRegistryDomain': 'pkg.dev'}]
760
+ 2025-11-20 14:30:34,791 INFO ___FILE_ONLY___
761
+ Update done!
762
+
763
+
764
+ 2025-11-20 14:30:34,794 DEBUG root Chosen display Format:none
765
+ 2025-11-20 14:30:34,795 INFO root Display format: "none"
.config/logs/2025.11.20/14.30.27.010422.log ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ 2025-11-20 14:30:27,011 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-11-20 14:30:27,013 DEBUG root Loaded Command Group: ['gcloud', 'components', 'post_process']
3
+ 2025-11-20 14:30:27,014 DEBUG root Running [gcloud.components.post-process] with arguments: []
4
+ 2025-11-20 14:30:34,644 DEBUG root Chosen display Format:none
5
+ 2025-11-20 14:30:34,644 INFO root Display format: "none"
.config/logs/2025.11.20/14.30.35.382199.log ADDED
@@ -0,0 +1,153 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 2025-11-20 14:30:35,383 DEBUG root Loaded Command Group: ['gcloud', 'components']
2
+ 2025-11-20 14:30:35,414 DEBUG root Loaded Command Group: ['gcloud', 'components', 'update']
3
+ 2025-11-20 14:30:35,417 DEBUG root Running [gcloud.components.update] with arguments: [--quiet: "True", COMPONENT-IDS:8: "['gcloud', 'core', 'bq', 'gsutil', 'compute', 'preview', 'alpha', 'beta']"]
4
+ 2025-11-20 14:30:35,418 INFO ___FILE_ONLY___ Beginning update. This process may take several minutes.
5
+
6
+ 2025-11-20 14:30:35,426 DEBUG urllib3.connectionpool Starting new HTTPS connection (1): dl.google.com:443
7
+ 2025-11-20 14:30:35,445 DEBUG urllib3.connectionpool https://dl.google.com:443 "GET /dl/cloudsdk/channels/rapid/components-2.json HTTP/1.1" 200 24782
8
+ 2025-11-20 14:30:35,456 WARNING root Component [compute] no longer exists.
9
+ 2025-11-20 14:30:35,457 INFO ___FILE_ONLY___
10
+
11
+ 2025-11-20 14:30:35,457 INFO ___FILE_ONLY___
12
+ Your current Google Cloud CLI version is: 548.0.0
13
+
14
+ 2025-11-20 14:30:35,457 INFO ___FILE_ONLY___ Installing components from version: 548.0.0
15
+
16
+ 2025-11-20 14:30:35,457 INFO ___FILE_ONLY___
17
+
18
+ 2025-11-20 14:30:35,458 DEBUG root Chosen display Format:table[box,title="These components will be removed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
19
+ 2025-11-20 14:30:35,458 DEBUG root Chosen display Format:table[box,title="These components will be updated."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
20
+ 2025-11-20 14:30:35,458 DEBUG root Chosen display Format:table[box,title="These components will be installed."](details.display_name:label=Name:align=left,version.version_string:label=Version:align=right,data.size.size(zero="",min=1048576):label=Size:align=right)
21
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___ ┌────────────────────────────────────────────────┐
22
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___
23
+
24
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___ │ These components will be installed. │
25
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___
26
+
27
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___ ├─────────────────────────┬────────────┬─────────┤
28
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___
29
+
30
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___ │ Name │ Version │ Size │
31
+ 2025-11-20 14:30:35,474 INFO ___FILE_ONLY___
32
+
33
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ ├─────────────────────────┼────────────┼─────────┤
34
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___
35
+
36
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ │
37
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ gcloud Alpha Commands
38
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___
39
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ │
40
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ 2025.11.17
41
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___
42
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ │
43
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___ < 1 MiB
44
+ 2025-11-20 14:30:35,475 INFO ___FILE_ONLY___
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.gitattributes CHANGED
@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ sample_data/mnist_test.csv filter=lfs diff=lfs merge=lfs -text
37
+ sample_data/mnist_train_small.csv filter=lfs diff=lfs merge=lfs -text
README.md ADDED
@@ -0,0 +1,243 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ ---
3
+ base_model: GPT-2-like-character-level
4
+ tags:
5
+ - text-generation
6
+ - character-level
7
+ - pytorch
8
+ ---
9
+
10
+ # Character-level GPT Model
11
+
12
+ This is a custom character-level GPT model trained on a text dataset (e.g., Shakespeare). It's a minimal implementation designed for educational purposes.
13
+
14
+ ## Model Architecture
15
+
16
+ The model is a Transformer-based decoder-only architecture, similar to GPT-2, but operating at the character level.
17
+
18
+ - `block_size`: 1024
19
+ - `vocab_size`: Dynamically determined from training data
20
+ - `n_layer`: 12
21
+ - `n_head`: 12
22
+ - `n_embd`: 768
23
+
24
+ ## How to Use
25
+
26
+ To use this model, you'll need the `pytorch_model.bin` (weights) and `vocab.json` (character mappings).
27
+
28
+ ```python
29
+ import torch
30
+ import json
31
+ from dataclasses import dataclass
32
+ import torch.nn as nn
33
+ from torch.nn import functional as F
34
+ import math
35
+
36
+ # --- Define your model classes (GPTConfig, CausalSelfAttention, MLP, Block, GPT) here ---
37
+ # Copy the relevant classes from your training script.
38
+
39
+ @dataclass
40
+ class GPTConfig:
41
+ block_size: int = 1024
42
+ vocab_size: int = 50257
43
+ n_layer: int = 12
44
+ n_head: int = 12
45
+ n_embd: int = 768
46
+
47
+ # ... (CausalSelfAttention, MLP, Block, GPT class definitions) ...
48
+
49
+ class CausalSelfAttention(nn.Module):
50
+ '''A minimal Causal Self-Attention block.'''
51
+ def __init__(self, config):
52
+ super().__init__()
53
+ assert config.n_embd % config.n_head == 0
54
+ self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
55
+ self.c_proj = nn.Linear(config.n_embd, config.n_embd)
56
+ self.c_proj.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)
57
+ self.n_head = config.n_head
58
+ self.n_embd = config.n_embd
59
+ self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
60
+ .view(1, 1, config.block_size, config.block_size))
61
+
62
+ def forward(self, x):
63
+ B, T, C = x.size()
64
+ q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
65
+ k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
66
+ q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
67
+ v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
68
+ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
69
+ att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
70
+ att = F.softmax(att, dim=-1)
71
+ y = att @ v
72
+ y = y.transpose(1, 2).contiguous().view(B, T, C)
73
+ y = self.c_proj(y)
74
+ return y
75
+
76
+ class MLP(nn.Module):
77
+ '''A minimal Multi-Layer Perceptron block.'''
78
+ def __init__(self, config):
79
+ super().__init__()
80
+ self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
81
+ self.gelu = nn.GELU(approximate='tanh')
82
+ self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
83
+ self.c_proj.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)
84
+
85
+ def forward(self, x):
86
+ x = self.c_fc(x)
87
+ x = self.gelu(x)
88
+ x = self.c_proj(x)
89
+ return x
90
+
91
+ class Block(nn.Module):
92
+ '''A minimal Transformer Block consisting of Attention and MLP.'''
93
+ def __init__(self, config):
94
+ super().__init__()
95
+ self.ln_1 = nn.LayerNorm(config.n_embd)
96
+ self.attn = CausalSelfAttention(config)
97
+ self.ln_2 = nn.LayerNorm(config.n_embd)
98
+ self.mlp = MLP(config)
99
+
100
+ def forward(self, x):
101
+ x = x + self.attn(self.ln_1(x))
102
+ x = x + self.mlp(self.ln_2(x))
103
+ return x
104
+
105
+ class GPT(nn.Module):
106
+ '''The full GPT model composed of Blocks.'''
107
+ def __init__(self, config):
108
+ super().__init__()
109
+ self.config = config
110
+ self.transformer = nn.ModuleDict(dict(
111
+ wte = nn.Embedding(config.vocab_size, config.n_embd),
112
+ wpe = nn.Embedding(config.block_size, config.n_embd),
113
+ h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
114
+ ln_f = nn.LayerNorm(config.n_embd),
115
+ ))
116
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
117
+ self.transformer.wte.weight = self.lm_head.weight # Weight tying
118
+ self.apply(self._init_weights)
119
+
120
+ def get_num_params(self, non_embedding=True):
121
+ n_params = sum(p.numel() for p in self.parameters())
122
+ if non_embedding:
123
+ n_params -= self.transformer.wpe.weight.numel()
124
+ return n_params
125
+
126
+ def _init_weights(self, module):
127
+ if isinstance(module, nn.Linear):
128
+ std = 0.02
129
+ if hasattr(module, 'NANGPT_SCALE_INIT'):
130
+ std *= module.NANGPT_SCALE_INIT
131
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std)
132
+ if module.bias is not None:
133
+ torch.nn.init.zeros_(module.bias)
134
+ elif isinstance(module, nn.Embedding):
135
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
136
+ elif isinstance(module, nn.LayerNorm):
137
+ torch.nn.init.zeros_(module.bias)
138
+ torch.nn.init.ones_(module.weight)
139
+
140
+ def forward(self, idx, targets=None):
141
+ device = idx.device
142
+ B, T = idx.size()
143
+ assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
144
+
145
+ pos = torch.arange(0, T, dtype=torch.long, device=device).unsqueeze(0)
146
+
147
+ tok_emb = self.transformer.wte(idx)
148
+ pos_emb = self.transformer.wpe(pos)
149
+ x = tok_emb + pos_emb
150
+
151
+ for block in self.transformer.h:
152
+ x = block(x)
153
+
154
+ x = self.transformer.ln_f(x)
155
+ logits = self.lm_head(x)
156
+
157
+ loss = None
158
+ if targets is not None:
159
+ loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
160
+
161
+ return logits, loss
162
+
163
+
164
+ # --- Custom tokenizer based on vocab.json ---
165
+ class SimpleCharTokenizer:
166
+ def __init__(self, vocab_file):
167
+ with open(vocab_file, 'r') as f:
168
+ vocab_data = json.load(f)
169
+ self.stoi = vocab_data['stoi']
170
+ self.itos = {int(k): v for k, v in vocab_data['itos'].items()} # keys are string in json
171
+ self.vocab_size = vocab_data['vocab_size']
172
+
173
+ def encode(self, s):
174
+ return [self.stoi[c] for c in s]
175
+
176
+ def decode(self, l):
177
+ return ''.join([self.itos[i] for i in l])
178
+
179
+
180
+ # --- Generation function (simplified) ---
181
+ def generate_from_hf(model, tokenizer, start_str, max_new_tokens, temperature=1.0, top_k=50, device='cpu'):
182
+ model.eval()
183
+ B, T_model = 1, model.config.block_size # Model's block_size
184
+
185
+ start_ids = tokenizer.encode(start_str)
186
+ x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
187
+
188
+ x = x[:, -T_model:] # Truncate if start string is too long for model's block_size
189
+
190
+ for _ in range(max_new_tokens):
191
+ # crop context if necessary
192
+ x_cond = x if x.size(1) <= T_model else x[:, -T_model:]
193
+
194
+ with torch.no_grad():
195
+ logits, _ = model(x_cond)
196
+ logits = logits[:, -1, :] / temperature
197
+ if top_k is not None:
198
+ v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
199
+ logits[logits < v[:, [-1]]] = -float('Inf')
200
+
201
+ probs = F.softmax(logits, dim=-1)
202
+ idx_next = torch.multinomial(probs, num_samples=1)
203
+
204
+ x = torch.cat((x, idx_next), dim=1)
205
+
206
+ if tokenizer.stoi.get('
207
+ ') is not None and idx_next.item() == tokenizer.stoi.get('
208
+ '):
209
+ break
210
+
211
+ return tokenizer.decode(x[0].tolist())
212
+
213
+
214
+
215
+ # Example usage:
216
+ # device = 'cuda' if torch.cuda.is_available() else 'cpu'
217
+ # # Load config and vocab
218
+ # with open('my_gpt_model/config.json', 'r') as f:
219
+ # model_config_dict = json.load(f)
220
+ # model_config = GPTConfig(**model_config_dict)
221
+ #
222
+ # tokenizer = SimpleCharTokenizer('my_gpt_model/vocab.json')
223
+ # model = GPT(model_config).to(device)
224
+ # model.load_state_dict(torch.load('my_gpt_model/pytorch_model.bin', map_location=device))
225
+ #
226
+ # prompt = "First Citizen:"
227
+ # generated_text = generate_from_hf(model, tokenizer, prompt, max_new_tokens=200, temperature=0.9, device=device)
228
+ # print(generated_text)
229
+
230
+ ```
231
+
232
+ ## Files in `.` directory:
233
+ - `pytorch_model.bin`: Contains the model's state dictionary (weights).
234
+ - `vocab.json`: Contains the character-to-integer (`stoi`) and integer-to-character (`itos`) mappings.
235
+ - `config.json`: Contains the model's configuration parameters (`GPTConfig`).
236
+
237
+ ## How to Load and Generate Text
238
+
239
+ ```python
240
+ # (Refer to the example usage in the code block above for loading and generating text)
241
+ ```
242
+
243
+ **Note**: The model architecture classes (`GPTConfig`, `CausalSelfAttention`, `MLP`, `Block`, `GPT`) and the `generate` function itself are part of the model's definition and would need to be present in your environment when loading the model from Hugging Face. The `README.md` includes these definitions for clarity and ease of use.
input.txt ADDED
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my_gpt_model/README.md ADDED
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1
+
2
+ ---
3
+ base_model: GPT-2-like-character-level
4
+ tags:
5
+ - text-generation
6
+ - character-level
7
+ - pytorch
8
+ ---
9
+
10
+ # Character-level GPT Model
11
+
12
+ This is a custom character-level GPT model trained on a text dataset (e.g., Shakespeare). It's a minimal implementation designed for educational purposes.
13
+
14
+ ## Model Architecture
15
+
16
+ The model is a Transformer-based decoder-only architecture, similar to GPT-2, but operating at the character level.
17
+
18
+ - `block_size`: 1024
19
+ - `vocab_size`: Dynamically determined from training data
20
+ - `n_layer`: 12
21
+ - `n_head`: 12
22
+ - `n_embd`: 768
23
+
24
+ ## How to Use
25
+
26
+ To use this model, you'll need the `pytorch_model.bin` (weights) and `vocab.json` (character mappings).
27
+
28
+ ```python
29
+ import torch
30
+ import json
31
+ from dataclasses import dataclass
32
+ import torch.nn as nn
33
+ from torch.nn import functional as F
34
+ import math
35
+
36
+ # --- Define your model classes (GPTConfig, CausalSelfAttention, MLP, Block, GPT) here ---
37
+ # Copy the relevant classes from your training script.
38
+
39
+ @dataclass
40
+ class GPTConfig:
41
+ block_size: int = 1024
42
+ vocab_size: int = 50257
43
+ n_layer: int = 12
44
+ n_head: int = 12
45
+ n_embd: int = 768
46
+
47
+ # ... (CausalSelfAttention, MLP, Block, GPT class definitions) ...
48
+
49
+ class CausalSelfAttention(nn.Module):
50
+ '''A minimal Causal Self-Attention block.'''
51
+ def __init__(self, config):
52
+ super().__init__()
53
+ assert config.n_embd % config.n_head == 0
54
+ self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
55
+ self.c_proj = nn.Linear(config.n_embd, config.n_embd)
56
+ self.c_proj.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)
57
+ self.n_head = config.n_head
58
+ self.n_embd = config.n_embd
59
+ self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
60
+ .view(1, 1, config.block_size, config.block_size))
61
+
62
+ def forward(self, x):
63
+ B, T, C = x.size()
64
+ q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
65
+ k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
66
+ q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
67
+ v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
68
+ att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
69
+ att = att.masked_fill(self.bias[:,:,:T,:T] == 0, float('-inf'))
70
+ att = F.softmax(att, dim=-1)
71
+ y = att @ v
72
+ y = y.transpose(1, 2).contiguous().view(B, T, C)
73
+ y = self.c_proj(y)
74
+ return y
75
+
76
+ class MLP(nn.Module):
77
+ '''A minimal Multi-Layer Perceptron block.'''
78
+ def __init__(self, config):
79
+ super().__init__()
80
+ self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
81
+ self.gelu = nn.GELU(approximate='tanh')
82
+ self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
83
+ self.c_proj.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)
84
+
85
+ def forward(self, x):
86
+ x = self.c_fc(x)
87
+ x = self.gelu(x)
88
+ x = self.c_proj(x)
89
+ return x
90
+
91
+ class Block(nn.Module):
92
+ '''A minimal Transformer Block consisting of Attention and MLP.'''
93
+ def __init__(self, config):
94
+ super().__init__()
95
+ self.ln_1 = nn.LayerNorm(config.n_embd)
96
+ self.attn = CausalSelfAttention(config)
97
+ self.ln_2 = nn.LayerNorm(config.n_embd)
98
+ self.mlp = MLP(config)
99
+
100
+ def forward(self, x):
101
+ x = x + self.attn(self.ln_1(x))
102
+ x = x + self.mlp(self.ln_2(x))
103
+ return x
104
+
105
+ class GPT(nn.Module):
106
+ '''The full GPT model composed of Blocks.'''
107
+ def __init__(self, config):
108
+ super().__init__()
109
+ self.config = config
110
+ self.transformer = nn.ModuleDict(dict(
111
+ wte = nn.Embedding(config.vocab_size, config.n_embd),
112
+ wpe = nn.Embedding(config.block_size, config.n_embd),
113
+ h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
114
+ ln_f = nn.LayerNorm(config.n_embd),
115
+ ))
116
+ self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
117
+ self.transformer.wte.weight = self.lm_head.weight # Weight tying
118
+ self.apply(self._init_weights)
119
+
120
+ def get_num_params(self, non_embedding=True):
121
+ n_params = sum(p.numel() for p in self.parameters())
122
+ if non_embedding:
123
+ n_params -= self.transformer.wpe.weight.numel()
124
+ return n_params
125
+
126
+ def _init_weights(self, module):
127
+ if isinstance(module, nn.Linear):
128
+ std = 0.02
129
+ if hasattr(module, 'NANGPT_SCALE_INIT'):
130
+ std *= module.NANGPT_SCALE_INIT
131
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std)
132
+ if module.bias is not None:
133
+ torch.nn.init.zeros_(module.bias)
134
+ elif isinstance(module, nn.Embedding):
135
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
136
+ elif isinstance(module, nn.LayerNorm):
137
+ torch.nn.init.zeros_(module.bias)
138
+ torch.nn.init.ones_(module.weight)
139
+
140
+ def forward(self, idx, targets=None):
141
+ device = idx.device
142
+ B, T = idx.size()
143
+ assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
144
+
145
+ pos = torch.arange(0, T, dtype=torch.long, device=device).unsqueeze(0)
146
+
147
+ tok_emb = self.transformer.wte(idx)
148
+ pos_emb = self.transformer.wpe(pos)
149
+ x = tok_emb + pos_emb
150
+
151
+ for block in self.transformer.h:
152
+ x = block(x)
153
+
154
+ x = self.transformer.ln_f(x)
155
+ logits = self.lm_head(x)
156
+
157
+ loss = None
158
+ if targets is not None:
159
+ loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
160
+
161
+ return logits, loss
162
+
163
+
164
+ # --- Custom tokenizer based on vocab.json ---
165
+ class SimpleCharTokenizer:
166
+ def __init__(self, vocab_file):
167
+ with open(vocab_file, 'r') as f:
168
+ vocab_data = json.load(f)
169
+ self.stoi = vocab_data['stoi']
170
+ self.itos = {int(k): v for k, v in vocab_data['itos'].items()} # keys are string in json
171
+ self.vocab_size = vocab_data['vocab_size']
172
+
173
+ def encode(self, s):
174
+ return [self.stoi[c] for c in s]
175
+
176
+ def decode(self, l):
177
+ return ''.join([self.itos[i] for i in l])
178
+
179
+
180
+ # --- Generation function (simplified) ---
181
+ def generate_from_hf(model, tokenizer, start_str, max_new_tokens, temperature=1.0, top_k=50, device='cpu'):
182
+ model.eval()
183
+ B, T_model = 1, model.config.block_size # Model's block_size
184
+
185
+ start_ids = tokenizer.encode(start_str)
186
+ x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
187
+
188
+ x = x[:, -T_model:] # Truncate if start string is too long for model's block_size
189
+
190
+ for _ in range(max_new_tokens):
191
+ # crop context if necessary
192
+ x_cond = x if x.size(1) <= T_model else x[:, -T_model:]
193
+
194
+ with torch.no_grad():
195
+ logits, _ = model(x_cond)
196
+ logits = logits[:, -1, :] / temperature
197
+ if top_k is not None:
198
+ v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
199
+ logits[logits < v[:, [-1]]] = -float('Inf')
200
+
201
+ probs = F.softmax(logits, dim=-1)
202
+ idx_next = torch.multinomial(probs, num_samples=1)
203
+
204
+ x = torch.cat((x, idx_next), dim=1)
205
+
206
+ if tokenizer.stoi.get('
207
+ ') is not None and idx_next.item() == tokenizer.stoi.get('
208
+ '):
209
+ break
210
+
211
+ return tokenizer.decode(x[0].tolist())
212
+
213
+
214
+
215
+ # Example usage:
216
+ # device = 'cuda' if torch.cuda.is_available() else 'cpu'
217
+ # # Load config and vocab
218
+ # with open('my_gpt_model/config.json', 'r') as f:
219
+ # model_config_dict = json.load(f)
220
+ # model_config = GPTConfig(**model_config_dict)
221
+ #
222
+ # tokenizer = SimpleCharTokenizer('my_gpt_model/vocab.json')
223
+ # model = GPT(model_config).to(device)
224
+ # model.load_state_dict(torch.load('my_gpt_model/pytorch_model.bin', map_location=device))
225
+ #
226
+ # prompt = "First Citizen:"
227
+ # generated_text = generate_from_hf(model, tokenizer, prompt, max_new_tokens=200, temperature=0.9, device=device)
228
+ # print(generated_text)
229
+
230
+ ```
231
+
232
+ ## Files in `my_gpt_model` directory:
233
+ - `pytorch_model.bin`: Contains the model's state dictionary (weights).
234
+ - `vocab.json`: Contains the character-to-integer (`stoi`) and integer-to-character (`itos`) mappings.
235
+ - `config.json`: Contains the model's configuration parameters (`GPTConfig`).
236
+
237
+ ## How to Load and Generate Text
238
+
239
+ ```python
240
+ # (Refer to the example usage in the code block above for loading and generating text)
241
+ ```
242
+
243
+ **Note**: The model architecture classes (`GPTConfig`, `CausalSelfAttention`, `MLP`, `Block`, `GPT`) and the `generate` function itself are part of the model's definition and would need to be present in your environment when loading the model from Hugging Face. The `README.md` includes these definitions for clarity and ease of use.
sample_data/README.md ADDED
@@ -0,0 +1,19 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ This directory includes a few sample datasets to get you started.
2
+
3
+ * `california_housing_data*.csv` is California housing data from the 1990 US
4
+ Census; more information is available at:
5
+ https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pub
6
+
7
+ * `mnist_*.csv` is a small sample of the
8
+ [MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
9
+ described at: http://yann.lecun.com/exdb/mnist/
10
+
11
+ * `anscombe.json` contains a copy of
12
+ [Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
13
+ was originally described in
14
+
15
+ Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
16
+ Statistician. 27 (1): 17-21. JSTOR 2682899.
17
+
18
+ and our copy was prepared by the
19
+ [vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
sample_data/anscombe.json ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {"Series":"IV", "X":8.0, "Y":6.89}
49
+ ]
sample_data/california_housing_test.csv ADDED
The diff for this file is too large to render. See raw diff
 
sample_data/california_housing_train.csv ADDED
The diff for this file is too large to render. See raw diff
 
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