File size: 32,502 Bytes
64b038b | 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 | {
"cells": [
{
"cell_type": "markdown",
"id": "e2381cb4",
"metadata": {},
"source": [
"# Fine-tune DiffusionGemma on PubMedQA with Before and After Evaluation\n"
]
},
{
"cell_type": "markdown",
"id": "533a0d2a",
"metadata": {},
"source": [
"## 1. Check GPU\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "bbeca747",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CUDA available: True\n",
"GPU: NVIDIA H100 80GB HBM3\n",
"Free GPU memory: 84.5 GB\n",
"Total GPU memory: 85.0 GB\n"
]
}
],
"source": [
"import torch\n",
"\n",
"print(\"CUDA available:\", torch.cuda.is_available())\n",
"\n",
"if torch.cuda.is_available():\n",
" print(\"GPU:\", torch.cuda.get_device_name(0))\n",
" free_gb, total_gb = torch.cuda.mem_get_info()\n",
" print(f\"Free GPU memory: {free_gb / 1e9:.1f} GB\")\n",
" print(f\"Total GPU memory: {total_gb / 1e9:.1f} GB\")\n"
]
},
{
"cell_type": "markdown",
"id": "49f1a81b",
"metadata": {},
"source": [
"## 2. Install Packages\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6b58f698",
"metadata": {},
"outputs": [],
"source": [
"# %%capture\n",
"# %pip install --upgrade pip wheel setuptools packaging ninja\n",
"# %pip install unsloth\n",
"# %pip install --no-deps --upgrade --force-reinstall git+https://github.com/unslothai/unsloth-zoo.git git+https://github.com/unslothai/unsloth.git\n",
"# %pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n",
"# %pip install --no-deps bitsandbytes accelerate peft trl triton\n",
"# %pip install --no-deps --upgrade \"torchao>=0.16.0\"\n",
"# %pip install --no-deps transformers==5.11.0 \"tokenizers>=0.22.0,<=0.23.0\"\n"
]
},
{
"cell_type": "markdown",
"id": "871befcc",
"metadata": {},
"source": [
"## 3. Import Libraries\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5f38209d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
"Torch: 2.10.0+cu128\n",
"CUDA available: True\n",
"GPU: NVIDIA H100 80GB HBM3\n"
]
}
],
"source": [
"import copy\n",
"import os\n",
"import random\n",
"import time\n",
"\n",
"import torch\n",
"from datasets import load_dataset\n",
"from unsloth import FastModel\n",
"\n",
"os.environ[\"HF_HUB_ENABLE_HF_TRANSFER\"] = \"1\"\n",
"torch._dynamo.config.recompile_limit = 64\n",
"\n",
"print(\"Torch:\", torch.__version__)\n",
"print(\"CUDA available:\", torch.cuda.is_available())\n",
"print(\"GPU:\", torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"None\")\n"
]
},
{
"cell_type": "markdown",
"id": "157d7536",
"metadata": {},
"source": [
"## 4. Set Config\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "5d20e819",
"metadata": {},
"outputs": [],
"source": [
"MODEL_NAME = \"unsloth/diffusiongemma-26B-A4B-it\"\n",
"DATASET_NAME = \"qiaojin/PubMedQA\"\n",
"\n",
"TRAIN_SUBSET = \"pqa_artificial\"\n",
"EVAL_SUBSET = \"pqa_labeled\"\n",
"\n",
"N_TRAIN = 3000\n",
"N_EVAL = 200\n",
"\n",
"MAX_CONTEXT_CHARS = 2500\n",
"\n",
"STEPS = 60\n",
"GRAD_ACCUM = 4\n",
"LR = 1e-4\n",
"T_LO = 0.1\n",
"\n",
"EVAL_TOTAL = 50\n",
"EVAL_DENOISING_STEPS = 16\n",
"\n",
"OUTPUT_DIR = \"diffusiongemma_pubmedqa_lora\"\n"
]
},
{
"cell_type": "markdown",
"id": "4ccd2a0b",
"metadata": {},
"source": [
"## 5. Load DiffusionGemma\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "13e33bdf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"==(( Unsloth: FastDiffusionModel (slow / transformers-only path) ))==\n",
" Model: unsloth/diffusiongemma-26B-A4B-it | class: DiffusionGemmaForBlockDiffusion | model_type: diffusion_gemma\n",
" dtype: torch.bfloat16 | 4bit: False | 8bit: False | attn: eager\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5c4a128418044a0dbb45bd077a6ac9d2",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Loading weights: 0%| | 0/1047 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vocab size: 262144\n",
"Canvas length: 256\n",
"Model device: cuda:0\n"
]
}
],
"source": [
"model, tokenizer = FastModel.from_pretrained(\n",
" model_name=MODEL_NAME,\n",
" dtype=torch.bfloat16,\n",
" load_in_4bit=False,\n",
")\n",
"\n",
"processor = tokenizer\n",
"tok = processor.tokenizer if hasattr(processor, \"tokenizer\") else processor\n",
"\n",
"vocab = model.config.text_config.vocab_size\n",
"canvas_len = model.config.canvas_length\n",
"\n",
"dev = next(\n",
" (p.device for p in model.parameters() if p.device.type != \"meta\"),\n",
" torch.device(\"cuda\"),\n",
")\n",
"\n",
"print(\"Vocab size:\", vocab)\n",
"print(\"Canvas length:\", canvas_len)\n",
"print(\"Model device:\", dev)\n"
]
},
{
"cell_type": "markdown",
"id": "02869124",
"metadata": {},
"source": [
"## 6. Add LoRA\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "590ec647",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainable params: 149,630,976 || all params: 25,973,409,840 || trainable%: 0.5761\n"
]
}
],
"source": [
"model = FastModel.get_peft_model(\n",
" model,\n",
" r=64,\n",
" lora_alpha=128,\n",
" use_gradient_checkpointing=False,\n",
")\n"
]
},
{
"cell_type": "markdown",
"id": "546f0664",
"metadata": {},
"source": [
"## 7. Load PubMedQA\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e1f4b158",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train size: 211269\n",
"Eval size: 1000\n",
"{'pubid': 25429730, 'question': 'Are group 2 innate lymphoid cells ( ILC2s ) increased in chronic rhinosinusitis with nasal polyps or eosinophilia?', 'context': {'contexts': ['Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 innate lymphoid cells (ILC2s) represent a recently discovered cell population which has been implicated in driving Th2 inflammation in CRS; however, their relationship with clinical disease characteristics has yet to be investigated.', 'The aim of this study was to identify ILC2s in sinus mucosa in patients with CRS and controls and compare ILC2s across characteristics of disease.', 'A cross-sectional study of patients with CRS undergoing endoscopic sinus surgery was conducted. Sinus mucosal biopsies were obtained during surgery and control tissue from patients undergoing pituitary tumour resection through transphenoidal approach. ILC2s were identified as CD45(+) Lin(-) CD127(+) CD4(-) CD8(-) CRTH2(CD294)(+) CD161(+) cells in single cell suspensions through flow cytometry. ILC2 frequencies, measured as a percentage of CD45(+) cells, were compared across CRS phenotype, endotype, inflammatory CRS subtype and other disease characteristics including blood eosinophils, serum IgE, asthma status and nasal symptom score.', '35 patients (40% female, age 48 ± 17 years) including 13 with eosinophilic CRS (eCRS), 13 with non-eCRS and 9 controls were recruited. ILC2 frequencies were associated with the presence of nasal polyps (P = 0.002) as well as high tissue eosinophilia (P = 0.004) and eosinophil-dominant CRS (P = 0.001) (Mann-Whitney U). They were also associated with increased blood eosinophilia (P = 0.005). There were no significant associations found between ILC2s and serum total IgE and allergic disease. In the CRS with nasal polyps (CRSwNP) population, ILC2s were increased in patients with co-existing asthma (P = 0.03). ILC2s were also correlated with worsening nasal symptom score in CRS (P = 0.04).'], 'labels': ['BACKGROUND', 'OBJECTIVE', 'METHODS', 'RESULTS'], 'meshes': ['Adult', 'Aged', 'Antigens, Surface', 'Case-Control Studies', 'Chronic Disease', 'Eosinophilia', 'Female', 'Humans', 'Hypersensitivity', 'Immunity, Innate', 'Immunoglobulin E', 'Immunophenotyping', 'Leukocyte Count', 'Lymphocyte Subsets', 'Male', 'Middle Aged', 'Nasal Mucosa', 'Nasal Polyps', 'Neutrophil Infiltration', 'Patient Outcome Assessment', 'Rhinitis', 'Sinusitis', 'Young Adult']}, 'long_answer': 'As ILC2s are elevated in patients with CRSwNP, they may drive nasal polyp formation in CRS. ILC2s are also linked with high tissue and blood eosinophilia and have a potential role in the activation and survival of eosinophils during the Th2 immune response. The association of innate lymphoid cells in CRS provides insights into its pathogenesis.', 'final_decision': 'yes'}\n"
]
}
],
"source": [
"train_data = load_dataset(DATASET_NAME, TRAIN_SUBSET, split=\"train\")\n",
"eval_data = load_dataset(DATASET_NAME, EVAL_SUBSET, split=\"train\")\n",
"\n",
"print(\"Train size:\", len(train_data))\n",
"print(\"Eval size:\", len(eval_data))\n",
"print(train_data[0])\n"
]
},
{
"cell_type": "markdown",
"id": "5f7ff8e7",
"metadata": {},
"source": [
"## 8. Convert Dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8c697a7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Prepared train examples: 3000\n",
"Prepared eval examples: 200\n",
"Answer the biomedical research question using only the context.\n",
"\n",
"Context:\n",
"Chronic rhinosinusitis (CRS) is a heterogeneous disease with an uncertain pathogenesis. Group 2 innate lymphoid cells (ILC2s) represent a recently discovered cell population which has been implicated in driving Th2 inflammation in CRS; however, their relationship with clinical disease characteristics has yet to be investigated. The aim of this study was to identify ILC2s in sinus mucosa in patients with CRS and controls and compare ILC2s across characteristics of disease. A cross-sectional study of patients with CRS undergoing endoscopic sinus surgery was conducted. Sinus mucosal biopsies were obtained during surgery and control tissue from patients undergoing pituitary tumour resection through transphenoidal approach. ILC2s were identified as CD45(+) Lin(-) CD127(+) CD4(-) CD8(-) CRTH2(CD294)(+) CD161(+) cells in single cell suspensions through flow cytometry. ILC2 frequencies, measured as a percentage of CD45(+) cells, were compared across CRS phenotype, endotype, inflammatory CRS subtype and other disease characteristics including blood eosinophils, serum IgE, asthma status and nasal symptom score. 35 patients (40% female, age 48 ± 17 years) including 13 with eosinophilic CRS (eCRS), 13 with non-eCRS and 9 controls were recruited. ILC2 frequencies were associated with the presence of nasal polyps (P = 0.002) as well as high tissue eosinophilia (P = 0.004) and eosinophil-dominant CRS (P = 0.001) (Mann-Whitney U). They were also associated with increased blood eosinophilia (P = 0.005). There were no significant associations found between ILC2s and serum total IgE and allergic disease. In the CRS with nasal polyps (CRSwNP) population, ILC2s were increased in patients with co-existing asthma (P = 0.03). ILC2s were also correlated with worsening nasal symptom score in CRS (P = 0.04).\n",
"\n",
"Question:\n",
"Are group 2 innate lymphoid cells ( ILC2s ) increased in chronic rhinosinusitis with nasal polyps or eosinophilia?\n",
"\n",
"Answer with only one word: yes, no, or maybe.\n",
"Answer: yes\n"
]
}
],
"source": [
"def make_prompt(row):\n",
" context = \" \".join(row[\"context\"][\"contexts\"])\n",
" context = context[:MAX_CONTEXT_CHARS]\n",
" question = row[\"question\"]\n",
"\n",
" return f\"\"\"Answer the biomedical research question using only the context.\n",
"\n",
"Context:\n",
"{context}\n",
"\n",
"Question:\n",
"{question}\n",
"\n",
"Answer with only one word: yes, no, or maybe.\"\"\"\n",
"\n",
"\n",
"def make_answer(row):\n",
" return row[\"final_decision\"].strip().lower()\n",
"\n",
"\n",
"def convert_row(row):\n",
" answer = make_answer(row)\n",
"\n",
" if answer not in [\"yes\", \"no\", \"maybe\"]:\n",
" return None\n",
"\n",
" return {\n",
" \"messages\": [\n",
" {\"role\": \"user\", \"content\": make_prompt(row)},\n",
" {\"role\": \"assistant\", \"content\": answer},\n",
" ]\n",
" }\n",
"\n",
"\n",
"train_rows = []\n",
"for row in train_data.select(range(N_TRAIN)):\n",
" item = convert_row(row)\n",
" if item is not None:\n",
" train_rows.append(item)\n",
"\n",
"eval_rows = []\n",
"for row in eval_data.select(range(N_EVAL)):\n",
" item = convert_row(row)\n",
" if item is not None:\n",
" eval_rows.append(item)\n",
"\n",
"print(\"Prepared train examples:\", len(train_rows))\n",
"print(\"Prepared eval examples:\", len(eval_rows))\n",
"print(train_rows[0][\"messages\"][0][\"content\"])\n",
"print(\"Answer:\", train_rows[0][\"messages\"][1][\"content\"])\n"
]
},
{
"cell_type": "markdown",
"id": "6b448b84",
"metadata": {},
"source": [
"## 9. Build Diffusion Training Examples\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "df6f8b40",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Usable training examples: 3000\n"
]
}
],
"source": [
"eos = model.generation_config.eos_token_id or [1]\n",
"eos = eos[0] if isinstance(eos, (list, tuple)) else eos\n",
"\n",
"pad = tok.pad_token_id if tok.pad_token_id is not None else eos\n",
"\n",
"\n",
"def build_examples(rows):\n",
" examples = []\n",
"\n",
" for row in rows:\n",
" user_message = row[\"messages\"][0]\n",
" assistant_message = row[\"messages\"][1]\n",
"\n",
" prompt_ids = processor.apply_chat_template(\n",
" [user_message],\n",
" tokenize=True,\n",
" add_generation_prompt=True,\n",
" return_tensors=\"pt\",\n",
" )[0]\n",
"\n",
" answer_ids = tok.encode(\n",
" assistant_message[\"content\"],\n",
" add_special_tokens=False,\n",
" )\n",
"\n",
" content = answer_ids + [eos]\n",
" n = len(content)\n",
"\n",
" if n > canvas_len:\n",
" continue\n",
"\n",
" x0 = torch.tensor(\n",
" content + [pad] * (canvas_len - n),\n",
" dtype=torch.long,\n",
" )\n",
"\n",
" loss_mask = torch.zeros(canvas_len, dtype=torch.bool)\n",
" loss_mask[:n] = True\n",
"\n",
" examples.append((prompt_ids, x0, loss_mask))\n",
"\n",
" return examples\n",
"\n",
"\n",
"examples = build_examples(train_rows)\n",
"print(\"Usable training examples:\", len(examples))\n"
]
},
{
"cell_type": "markdown",
"id": "077c118d",
"metadata": {},
"source": [
"## 10. Inference and Evaluation Functions\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "107d2d21",
"metadata": {},
"outputs": [],
"source": [
"def answer_question(prompt, steps=64):\n",
" input_ids = processor.apply_chat_template(\n",
" [{\"role\": \"user\", \"content\": prompt}],\n",
" tokenize=True,\n",
" add_generation_prompt=True,\n",
" return_tensors=\"pt\",\n",
" ).to(dev)\n",
"\n",
" gen_config = copy.deepcopy(model.generation_config)\n",
" gen_config.max_denoising_steps = steps\n",
" gen_config.max_new_tokens = canvas_len\n",
"\n",
" model.eval()\n",
"\n",
" with torch.no_grad():\n",
" output = model.generate(\n",
" input_ids=input_ids,\n",
" generation_config=gen_config,\n",
" )\n",
"\n",
" generated = output.sequences[0, input_ids.shape[1]:]\n",
" text = tok.decode(generated.tolist(), skip_special_tokens=True)\n",
" return text.strip().lower()\n",
"\n",
"\n",
"def clean_prediction(text):\n",
" text = text.lower().strip()\n",
"\n",
" if text.startswith(\"yes\"):\n",
" return \"yes\"\n",
" if text.startswith(\"no\"):\n",
" return \"no\"\n",
" if text.startswith(\"maybe\"):\n",
" return \"maybe\"\n",
"\n",
" words = text.replace(\".\", \" \").replace(\",\", \" \").split()\n",
"\n",
" for word in words:\n",
" if word in [\"yes\", \"no\", \"maybe\"]:\n",
" return word\n",
"\n",
" return \"unknown\"\n",
"\n",
"\n",
"def evaluate_model(rows, total=50, steps=64, title=\"Evaluation\"):\n",
" correct = 0\n",
" results = []\n",
" total = min(total, len(rows))\n",
"\n",
" print(title)\n",
" print(\"-\" * len(title))\n",
"\n",
" for i, row in enumerate(rows[:total], start=1):\n",
" prompt = row[\"messages\"][0][\"content\"]\n",
" gold = row[\"messages\"][1][\"content\"]\n",
"\n",
" raw_pred = answer_question(prompt, steps=steps)\n",
" pred = clean_prediction(raw_pred)\n",
"\n",
" is_correct = pred == gold\n",
" correct += int(is_correct)\n",
"\n",
" results.append({\n",
" \"index\": i,\n",
" \"gold\": gold,\n",
" \"prediction\": pred,\n",
" \"raw_prediction\": raw_pred,\n",
" \"correct\": is_correct,\n",
" })\n",
"\n",
" print(f\"{i:02d}. Gold: {gold} | Pred: {pred} | Correct: {is_correct}\")\n",
"\n",
" accuracy = correct / total if total else 0\n",
"\n",
" print()\n",
" print(\"Accuracy:\", accuracy)\n",
" print()\n",
"\n",
" return {\n",
" \"accuracy\": accuracy,\n",
" \"correct\": correct,\n",
" \"total\": total,\n",
" \"results\": results,\n",
" }\n"
]
},
{
"cell_type": "markdown",
"id": "e779832f",
"metadata": {},
"source": [
"## 11. Evaluate Before Fine-Tuning\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "4ac8ca3d",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before Fine-Tuning Evaluation\n",
"-----------------------------\n",
"01. Gold: yes | Pred: yes | Correct: True\n",
"02. Gold: no | Pred: yes | Correct: False\n",
"03. Gold: yes | Pred: maybe | Correct: False\n",
"04. Gold: no | Pred: maybe | Correct: False\n",
"05. Gold: yes | Pred: yes | Correct: True\n",
"06. Gold: yes | Pred: maybe | Correct: False\n",
"07. Gold: maybe | Pred: yes | Correct: False\n",
"08. Gold: no | Pred: yes | Correct: False\n",
"09. Gold: no | Pred: no | Correct: True\n",
"10. Gold: yes | Pred: yes | Correct: True\n",
"11. Gold: yes | Pred: yes | Correct: True\n",
"12. Gold: no | Pred: maybe | Correct: False\n",
"13. Gold: yes | Pred: yes | Correct: True\n",
"14. Gold: no | Pred: no | Correct: True\n",
"15. Gold: yes | Pred: yes | Correct: True\n",
"16. Gold: yes | Pred: yes | Correct: True\n",
"17. Gold: yes | Pred: maybe | Correct: False\n",
"18. Gold: yes | Pred: yes | Correct: True\n",
"19. Gold: yes | Pred: yes | Correct: True\n",
"20. Gold: yes | Pred: yes | Correct: True\n",
"21. Gold: yes | Pred: maybe | Correct: False\n",
"22. Gold: yes | Pred: yes | Correct: True\n",
"23. Gold: yes | Pred: yes | Correct: True\n",
"24. Gold: yes | Pred: yes | Correct: True\n",
"25. Gold: yes | Pred: maybe | Correct: False\n",
"26. Gold: no | Pred: no | Correct: True\n",
"27. Gold: yes | Pred: no | Correct: False\n",
"28. Gold: maybe | Pred: yes | Correct: False\n",
"29. Gold: yes | Pred: yes | Correct: True\n",
"30. Gold: yes | Pred: yes | Correct: True\n",
"31. Gold: no | Pred: maybe | Correct: False\n",
"32. Gold: maybe | Pred: yes | Correct: False\n",
"33. Gold: no | Pred: maybe | Correct: False\n",
"34. Gold: yes | Pred: yes | Correct: True\n",
"35. Gold: yes | Pred: yes | Correct: True\n",
"36. Gold: no | Pred: no | Correct: True\n",
"37. Gold: no | Pred: maybe | Correct: False\n",
"38. Gold: no | Pred: yes | Correct: False\n",
"39. Gold: yes | Pred: yes | Correct: True\n",
"40. Gold: no | Pred: no | Correct: True\n",
"41. Gold: yes | Pred: yes | Correct: True\n",
"42. Gold: yes | Pred: yes | Correct: True\n",
"43. Gold: maybe | Pred: maybe | Correct: True\n",
"44. Gold: no | Pred: maybe | Correct: False\n",
"45. Gold: yes | Pred: maybe | Correct: False\n",
"46. Gold: yes | Pred: yes | Correct: True\n",
"47. Gold: yes | Pred: yes | Correct: True\n",
"48. Gold: yes | Pred: yes | Correct: True\n",
"49. Gold: yes | Pred: yes | Correct: True\n",
"50. Gold: no | Pred: maybe | Correct: False\n",
"\n",
"Accuracy: 0.6\n",
"\n"
]
}
],
"source": [
"before_eval = evaluate_model(\n",
" eval_rows,\n",
" total=EVAL_TOTAL,\n",
" steps=EVAL_DENOISING_STEPS,\n",
" title=\"Before Fine-Tuning Evaluation\",\n",
")\n"
]
},
{
"cell_type": "markdown",
"id": "adb680c8",
"metadata": {},
"source": [
"## 12. Set Up Training\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "a9ccf9b6",
"metadata": {},
"outputs": [],
"source": [
"model.config.use_cache = True\n",
"model.train()\n",
"\n",
"opt = torch.optim.AdamW(\n",
" [p for p in model.parameters() if p.requires_grad],\n",
" lr=LR,\n",
" betas=(0.9, 0.95),\n",
" weight_decay=0.0,\n",
")\n",
"\n",
"sched = torch.optim.lr_scheduler.OneCycleLR(\n",
" opt,\n",
" max_lr=LR,\n",
" total_steps=STEPS,\n",
" pct_start=0.03,\n",
" anneal_strategy=\"cos\",\n",
")\n",
"\n",
"\n",
"def corrupt(x0):\n",
" noise_level = random.uniform(T_LO, 1.0)\n",
" xt = x0.to(dev).clone()\n",
" noise_mask = torch.rand(canvas_len, device=dev) < noise_level\n",
" xt[noise_mask] = torch.randint(0, vocab, (canvas_len,), device=dev)[noise_mask]\n",
" return xt.unsqueeze(0)\n"
]
},
{
"cell_type": "markdown",
"id": "d44af4fa",
"metadata": {},
"source": [
"## 13. Train\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "ea5e6bcf",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step 20/60 | loss 0.0019 | 43s\n",
"step 40/60 | loss 0.0003 | 85s\n",
"step 60/60 | loss 0.0001 | 126s\n"
]
}
],
"source": [
"order = list(range(len(examples)))\n",
"ptr = 0\n",
"start_time = time.time()\n",
"\n",
"opt.zero_grad(set_to_none=True)\n",
"\n",
"for step in range(1, STEPS + 1):\n",
" step_loss = 0.0\n",
"\n",
" for _ in range(GRAD_ACCUM):\n",
" if ptr >= len(order):\n",
" random.shuffle(order)\n",
" ptr = 0\n",
"\n",
" prompt_ids, x0, loss_mask = examples[order[ptr]]\n",
" ptr += 1\n",
"\n",
" output = model(\n",
" input_ids=prompt_ids.unsqueeze(0).to(dev),\n",
" canvas_ids=corrupt(x0),\n",
" self_conditioning_logits=None,\n",
" )\n",
"\n",
" logits = output.logits[0].float()\n",
" mask = loss_mask.to(dev)\n",
"\n",
" loss = torch.nn.functional.cross_entropy(\n",
" logits[mask],\n",
" x0.to(dev)[mask],\n",
" )\n",
"\n",
" (loss / GRAD_ACCUM).backward()\n",
" step_loss += loss.item() / GRAD_ACCUM\n",
"\n",
" torch.nn.utils.clip_grad_norm_(\n",
" [p for p in model.parameters() if p.requires_grad],\n",
" 1.0,\n",
" )\n",
"\n",
" opt.step()\n",
" sched.step()\n",
" opt.zero_grad(set_to_none=True)\n",
"\n",
" if step % 20 == 0:\n",
" elapsed = time.time() - start_time\n",
" print(f\"step {step}/{STEPS} | loss {step_loss:.4f} | {elapsed:.0f}s\")\n"
]
},
{
"cell_type": "markdown",
"id": "ce4f69d9",
"metadata": {},
"source": [
"## 14. Evaluate After Fine-Tuning\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2117d937",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"After Fine-Tuning Evaluation\n",
"----------------------------\n",
"01. Gold: yes | Pred: yes | Correct: True\n",
"02. Gold: no | Pred: yes | Correct: False\n",
"03. Gold: yes | Pred: yes | Correct: True\n",
"04. Gold: no | Pred: yes | Correct: False\n",
"05. Gold: yes | Pred: yes | Correct: True\n",
"06. Gold: yes | Pred: yes | Correct: True\n",
"07. Gold: maybe | Pred: yes | Correct: False\n",
"08. Gold: no | Pred: yes | Correct: False\n",
"09. Gold: no | Pred: no | Correct: True\n",
"10. Gold: yes | Pred: yes | Correct: True\n",
"11. Gold: yes | Pred: yes | Correct: True\n",
"12. Gold: no | Pred: no | Correct: True\n",
"13. Gold: yes | Pred: yes | Correct: True\n",
"14. Gold: no | Pred: no | Correct: True\n",
"15. Gold: yes | Pred: yes | Correct: True\n",
"16. Gold: yes | Pred: yes | Correct: True\n",
"17. Gold: yes | Pred: yes | Correct: True\n",
"18. Gold: yes | Pred: yes | Correct: True\n",
"19. Gold: yes | Pred: yes | Correct: True\n",
"20. Gold: yes | Pred: yes | Correct: True\n",
"21. Gold: yes | Pred: yes | Correct: True\n",
"22. Gold: yes | Pred: yes | Correct: True\n",
"23. Gold: yes | Pred: yes | Correct: True\n",
"24. Gold: yes | Pred: yes | Correct: True\n",
"25. Gold: yes | Pred: yes | Correct: True\n",
"26. Gold: no | Pred: no | Correct: True\n",
"27. Gold: yes | Pred: yes | Correct: True\n",
"28. Gold: maybe | Pred: yes | Correct: False\n",
"29. Gold: yes | Pred: yes | Correct: True\n",
"30. Gold: yes | Pred: yes | Correct: True\n",
"31. Gold: no | Pred: no | Correct: True\n",
"32. Gold: maybe | Pred: yes | Correct: False\n",
"33. Gold: no | Pred: no | Correct: True\n",
"34. Gold: yes | Pred: yes | Correct: True\n",
"35. Gold: yes | Pred: yes | Correct: True\n",
"36. Gold: no | Pred: yes | Correct: False\n",
"37. Gold: no | Pred: yes | Correct: False\n",
"38. Gold: no | Pred: yes | Correct: False\n",
"39. Gold: yes | Pred: yes | Correct: True\n",
"40. Gold: no | Pred: no | Correct: True\n",
"41. Gold: yes | Pred: yes | Correct: True\n",
"42. Gold: yes | Pred: yes | Correct: True\n",
"43. Gold: maybe | Pred: no | Correct: False\n",
"44. Gold: no | Pred: no | Correct: True\n",
"45. Gold: yes | Pred: yes | Correct: True\n",
"46. Gold: yes | Pred: yes | Correct: True\n",
"47. Gold: yes | Pred: yes | Correct: True\n",
"48. Gold: yes | Pred: yes | Correct: True\n",
"49. Gold: yes | Pred: yes | Correct: True\n",
"50. Gold: no | Pred: no | Correct: True\n",
"\n",
"Accuracy: 0.8\n",
"\n"
]
}
],
"source": [
"after_eval = evaluate_model(\n",
" eval_rows,\n",
" total=EVAL_TOTAL,\n",
" steps=EVAL_DENOISING_STEPS,\n",
" title=\"After Fine-Tuning Evaluation\",\n",
")\n"
]
},
{
"cell_type": "markdown",
"id": "c4a8273e",
"metadata": {},
"source": [
"## 15. Compare Before and After\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "aa62e0f3",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Before fine-tuning accuracy: 0.6\n",
"After fine-tuning accuracy: 0.8\n",
"Improvement: 0.20000000000000007\n"
]
}
],
"source": [
"before_accuracy = before_eval[\"accuracy\"]\n",
"after_accuracy = after_eval[\"accuracy\"]\n",
"improvement = after_accuracy - before_accuracy\n",
"\n",
"print(\"Before fine-tuning accuracy:\", before_accuracy)\n",
"print(\"After fine-tuning accuracy:\", after_accuracy)\n",
"print(\"Improvement:\", improvement)\n"
]
},
{
"cell_type": "markdown",
"id": "9800fdcd",
"metadata": {},
"source": [
"## 16. Save Adapter\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "9e30c8e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Saved LoRA adapter to: diffusiongemma_pubmedqa_lora\n"
]
}
],
"source": [
"model.save_pretrained(OUTPUT_DIR)\n",
"processor.save_pretrained(OUTPUT_DIR)\n",
"\n",
"print(f\"Saved LoRA adapter to: {OUTPUT_DIR}\")\n"
]
},
{
"cell_type": "markdown",
"id": "8c900a6a",
"metadata": {},
"source": [
"## 17. Push to Hugging Face\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "d63ce717",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"No files have been modified since last commit. Skipping to prevent empty commit.\n",
"[huggingface_hub.hf_api|WARNING]No files have been modified since last commit. Skipping to prevent empty commit.\n"
]
},
{
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/kingabzpro/diffusiongemma_pubmedqa/commit/080c9609c8ac80355bb27e6ce1e9e6478b297fcb', commit_message='Upload processor', commit_description='', oid='080c9609c8ac80355bb27e6ce1e9e6478b297fcb', pr_url=None, repo_url=RepoUrl('https://huggingface.co/kingabzpro/diffusiongemma_pubmedqa', endpoint='https://huggingface.co', repo_type='model', repo_id='kingabzpro/diffusiongemma_pubmedqa'), pr_revision=None, pr_num=None)"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"model.push_to_hub(\"kingabzpro/diffusiongemma_pubmedqa\")\n",
"processor.push_to_hub(\"kingabzpro/diffusiongemma_pubmedqa\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|