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fine tuning model

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  1. fine_tuning_.ipynb +632 -0
fine_tuning_.ipynb ADDED
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390
+ " const quickchartButtonEl =\n",
391
+ " document.querySelector('#' + key + ' button');\n",
392
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
393
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
394
+ " try {\n",
395
+ " const charts = await google.colab.kernel.invokeFunction(\n",
396
+ " 'suggestCharts', [key], {});\n",
397
+ " } catch (error) {\n",
398
+ " console.error('Error during call to suggestCharts:', error);\n",
399
+ " }\n",
400
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
401
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
402
+ " }\n",
403
+ " (() => {\n",
404
+ " let quickchartButtonEl =\n",
405
+ " document.querySelector('#df-7a3554a8-e00d-4ba4-88ed-15c7da18b7db button');\n",
406
+ " quickchartButtonEl.style.display =\n",
407
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
408
+ " })();\n",
409
+ " </script>\n",
410
+ "</div>\n",
411
+ "\n",
412
+ " <div id=\"id_63d1b66c-c721-4f22-ae5a-f946ac5f93d4\">\n",
413
+ " <style>\n",
414
+ " .colab-df-generate {\n",
415
+ " background-color: #E8F0FE;\n",
416
+ " border: none;\n",
417
+ " border-radius: 50%;\n",
418
+ " cursor: pointer;\n",
419
+ " display: none;\n",
420
+ " fill: #1967D2;\n",
421
+ " height: 32px;\n",
422
+ " padding: 0 0 0 0;\n",
423
+ " width: 32px;\n",
424
+ " }\n",
425
+ "\n",
426
+ " .colab-df-generate:hover {\n",
427
+ " background-color: #E2EBFA;\n",
428
+ " box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
429
+ " fill: #174EA6;\n",
430
+ " }\n",
431
+ "\n",
432
+ " [theme=dark] .colab-df-generate {\n",
433
+ " background-color: #3B4455;\n",
434
+ " fill: #D2E3FC;\n",
435
+ " }\n",
436
+ "\n",
437
+ " [theme=dark] .colab-df-generate:hover {\n",
438
+ " background-color: #434B5C;\n",
439
+ " box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
440
+ " filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
441
+ " fill: #FFFFFF;\n",
442
+ " }\n",
443
+ " </style>\n",
444
+ " <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
445
+ " title=\"Generate code using this dataframe.\"\n",
446
+ " style=\"display:none;\">\n",
447
+ "\n",
448
+ " <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
449
+ " width=\"24px\">\n",
450
+ " <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
451
+ " </svg>\n",
452
+ " </button>\n",
453
+ " <script>\n",
454
+ " (() => {\n",
455
+ " const buttonEl =\n",
456
+ " document.querySelector('#id_63d1b66c-c721-4f22-ae5a-f946ac5f93d4 button.colab-df-generate');\n",
457
+ " buttonEl.style.display =\n",
458
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
459
+ "\n",
460
+ " buttonEl.onclick = () => {\n",
461
+ " google.colab.notebook.generateWithVariable('df');\n",
462
+ " }\n",
463
+ " })();\n",
464
+ " </script>\n",
465
+ " </div>\n",
466
+ "\n",
467
+ " </div>\n",
468
+ " </div>\n"
469
+ ],
470
+ "application/vnd.google.colaboratory.intrinsic+json": {
471
+ "type": "dataframe",
472
+ "variable_name": "df",
473
+ "summary": "{\n \"name\": \"df\",\n \"rows\": 1349,\n \"fields\": [\n {\n \"column\": \"prompts\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 1052,\n \"samples\": [\n \"i have sharp chest pain, abusing alcohol, sharp abdominal pain, vomiting, diarrhea, back pain, burning abdominal pain, side pain, lower body pain, upper abdominal pain. what disease do i have?\",\n \"i have wrist pain, hand or finger swelling, arm pain, knee pain, foot or toe pain, ankle pain, shoulder pain. what disease do i have?\",\n \"i have sharp chest pain, leg pain, sharp abdominal pain, vomiting, lower abdominal pain, low back pain. what disease do i have?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"results\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 455,\n \"samples\": [\n \"you have temporary or benign blood in urine.\",\n \"You have Hypoglycemia.\",\n \"you have sebaceous cyst.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
474
+ }
475
+ },
476
+ "metadata": {},
477
+ "execution_count": 3
478
+ }
479
+ ]
480
+ },
481
+ {
482
+ "cell_type": "code",
483
+ "source": [
484
+ "\n",
485
+ "BATCH_SIZE = 100\n",
486
+ "NUM_EPOCHS = 1\n"
487
+ ],
488
+ "metadata": {
489
+ "id": "0_f3sqBtpXpQ"
490
+ },
491
+ "execution_count": null,
492
+ "outputs": []
493
+ },
494
+ {
495
+ "cell_type": "code",
496
+ "source": [
497
+ "def create_model_adapter(gradient):\n",
498
+ " base_model = gradient.get_base_model(base_model_slug=\"nous-hermes2\")\n",
499
+ " new_model_adapter = base_model.create_model_adapter(\n",
500
+ " name=\"meta/llama-2-7b:73001d654114dad81ec65da3b834e2f691af1e1526453189b7bf36fb3f32d0f9\"\n",
501
+ " )\n",
502
+ " print(f\"Created model adapter with id {new_model_adapter.id}\")\n",
503
+ " return new_model_adapter"
504
+ ],
505
+ "metadata": {
506
+ "id": "bW9Qin9MpcRU"
507
+ },
508
+ "execution_count": null,
509
+ "outputs": []
510
+ },
511
+ {
512
+ "cell_type": "code",
513
+ "source": [
514
+ "def fine_tune_in_batches(df, gradient, batch_size, num_epochs):\n",
515
+ " new_model_adapter = create_model_adapter(gradient)\n",
516
+ "\n",
517
+ " # Split the DataFrame into batches\n",
518
+ " batches = [df[i:i + batch_size] for i in range(0, len(df), batch_size)]\n",
519
+ "\n",
520
+ " # Iterate over batches and perform fine-tuning\n",
521
+ " for batch_index, batch in enumerate(batches):\n",
522
+ " fine_tuning_samples = []\n",
523
+ " for _, row in batch.iterrows():\n",
524
+ " fine_tuning_samples.append({\n",
525
+ " \"inputs\": f\"### Instruction: {row['prompts']}\",\n",
526
+ " \"targets\": f\"### Response: {row['results']}\"\n",
527
+ " })\n",
528
+ "\n",
529
+ " # Fine-tune for the given number of epochs\n",
530
+ " for epoch in range(num_epochs):\n",
531
+ " print(f\"Fine-tuning batch {batch_index + 1} (epoch {epoch + 1})\")\n",
532
+ " new_model_adapter.fine_tune(samples=fine_tuning_samples)\n",
533
+ "\n",
534
+ " return new_model_adapter"
535
+ ],
536
+ "metadata": {
537
+ "id": "XfDSZVfspe9c"
538
+ },
539
+ "execution_count": null,
540
+ "outputs": []
541
+ },
542
+ {
543
+ "cell_type": "code",
544
+ "source": [
545
+ "from gradientai import Gradient\n",
546
+ "\n",
547
+ "def main():\n",
548
+ " # Initialize the Gradient API\n",
549
+ " gradient = Gradient()\n",
550
+ "\n",
551
+ "\n",
552
+ " # Fine-tune in batches and retain the final model adapter\n",
553
+ " model_adapter = fine_tune_in_batches(df, gradient, BATCH_SIZE, NUM_EPOCHS)\n",
554
+ "\n",
555
+ " # Test the model after fine-tuning\n",
556
+ " sample_query = f\"### Instruction: {df['prompts'][0]} \\n\\n### Response:\"\n",
557
+ " completion = model_adapter.complete(query=sample_query, max_generated_token_count=100).generated_output\n",
558
+ " print(f\"Generated (after fine-tuning): {completion}\")\n",
559
+ "\n",
560
+ " # Clean up the model adapter\n",
561
+ " model_adapter.delete()\n",
562
+ " gradient.close()\n",
563
+ "\n",
564
+ "if __name__ == \"__main__\":\n",
565
+ " main()"
566
+ ],
567
+ "metadata": {
568
+ "colab": {
569
+ "base_uri": "https://localhost:8080/"
570
+ },
571
+ "id": "RzHdyX0nkPtl",
572
+ "outputId": "b5ae54fd-6246-414b-cffb-5421a5e8c8a5"
573
+ },
574
+ "execution_count": null,
575
+ "outputs": [
576
+ {
577
+ "output_type": "stream",
578
+ "name": "stdout",
579
+ "text": [
580
+ "Created model adapter with id 9aebddd0-336b-4b61-8910-d8be7ef38f43_model_adapter\n",
581
+ "Fine-tuning batch 1 (epoch 1)\n",
582
+ "Fine-tuning batch 2 (epoch 1)\n",
583
+ "Fine-tuning batch 3 (epoch 1)\n",
584
+ "Fine-tuning batch 4 (epoch 1)\n",
585
+ "Fine-tuning batch 5 (epoch 1)\n",
586
+ "Fine-tuning batch 6 (epoch 1)\n",
587
+ "Fine-tuning batch 7 (epoch 1)\n",
588
+ "Fine-tuning batch 8 (epoch 1)\n",
589
+ "Fine-tuning batch 9 (epoch 1)\n",
590
+ "Fine-tuning batch 10 (epoch 1)\n",
591
+ "Fine-tuning batch 11 (epoch 1)\n",
592
+ "Fine-tuning batch 12 (epoch 1)\n",
593
+ "Fine-tuning batch 13 (epoch 1)\n",
594
+ "Fine-tuning batch 14 (epoch 1)\n",
595
+ "Generated (after fine-tuning): You may have Pneumonia.\n"
596
+ ]
597
+ }
598
+ ]
599
+ },
600
+ {
601
+ "cell_type": "code",
602
+ "source": [
603
+ "sample_query = f\"### Instruction: {df['prompts'][0]} \\n\\n### Response:\"\n",
604
+ "sample_query"
605
+ ],
606
+ "metadata": {
607
+ "colab": {
608
+ "base_uri": "https://localhost:8080/",
609
+ "height": 35
610
+ },
611
+ "id": "LLfG-8dG3gF3",
612
+ "outputId": "bd7192a7-6a07-4262-eaf6-fb62a5b278c6"
613
+ },
614
+ "execution_count": null,
615
+ "outputs": [
616
+ {
617
+ "output_type": "execute_result",
618
+ "data": {
619
+ "text/plain": [
620
+ "'### Instruction: I have Fever, Fatigue, Difficulty Breathing. What disease do i have? \\n\\n### Response:'"
621
+ ],
622
+ "application/vnd.google.colaboratory.intrinsic+json": {
623
+ "type": "string"
624
+ }
625
+ },
626
+ "metadata": {},
627
+ "execution_count": 20
628
+ }
629
+ ]
630
+ }
631
+ ]
632
+ }