{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nYQNJK5hILPg", "outputId": "5442efec-88b0-4afc-a357-eaa3edb7b336" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.8/51.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m2.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.1/104.1 kB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta 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eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m86.8/86.8 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for rouge-score (setup.py) ... \u001b[?25l\u001b[?25hdone\n" ] } ], "source": [ "!pip install -q transformers datasets peft accelerate evaluate rouge-score nltk sacrebleu torch optimum[onnxruntime]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "7IZr99e2IRpS" }, "outputs": [], "source": [ "from datasets import load_dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 296, "referenced_widgets": [ "aecce17a6dd14284ac1b228c5bfc2c28", "e1b6c3a07c0c465dbfde5f65fd0a451d", "1f9d2261f13a4f90a69a238ebe762b17", "23e945fb213f4bb6abff49bb0eb081bd", "ff475050855c4524b7d598bcc52d3f69", "2366670474ad4fb4afb73c7a150cc4da", "3bcaa26b078b4fbc945a42bfd69ebbc0", "0bc2bb75acb64d90b81c96667b7b2349", "fe98d5ec1e334fa28816cc985414055e", "6baf0fe0e0de48028776aca87e20fcbb", "8423bb658f9943168901ca7b86cb3f7f", "4ee1d5f108aa4e5bb3e5b9eebb8b6417", "c0f0954cb281453591908cd24e62b6f2", "4c0747dbd717493ab11523d45a437920", "7260674b083f4457ba8d254ccf5fe1e8", "cb5282a788e74926a2146c60c3d7af3a", "25004871f40649039c582d2792d96336", "364bfd258ba941a5bb99dc8b006b1751", "0764b19c58234e8db7718362e76f70a7", "2e88d0e6d7c446378556d8aeec0f731d", "00d0c8e41765470c9cac83de7d2c6a88", "b46227d80ce9496cb2580953413bdc8f", "0437cfd15fbe4452a2dbdc3f6feabbce", "a1f362ef1a11420fb573d9f7c95921d4", "8b221f8b85b44494a45442137a1b3671", "6c3970a27484431b9463b4eb63e0a041", "ab657d4464eb4dd096a5e99e939eba2e", "68fe0cabb24e4cc3a5ae703aa3b35d04", "af4ee2833f5a44409e913d842bc28123", "d2c795b9ed094af8aeba9656f88f8b7b", "f47e92cd0a1148ccb6163ccbe2c33c57", "f06ae10cb1894fa1973da0688e2483a3", "4afcdccef3854391b017393d972d6e4b" ] }, "id": "scgQp7wEIWzZ", "outputId": "48958a9c-2837-40b8-d5e4-cbe743ddff63" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aecce17a6dd14284ac1b228c5bfc2c28", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/118 [00:00\n", " \n", " \n", " [6310/6310 4:05:14, Epoch 1/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining LossValidation Loss
5003.3591003.246274
10003.2324003.146441
15003.1864003.092566
20003.1432003.057678
25003.1389003.031778
30003.1073003.010683
35003.1046002.998122
40003.0802002.984522
45003.0821002.975530
50003.0603002.965659
55003.0538002.962169
60003.0523002.957626

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "Passing a tuple of `past_key_values` is deprecated and will be removed in Transformers v4.58.0. You should pass an instance of `EncoderDecoderCache` instead, e.g. `past_key_values=EncoderDecoderCache.from_legacy_cache(past_key_values)`.\n" ] } ], "source": [ "train_result = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "cSeTPTNNKQ4_", "outputId": "38c9b1e9-a9bf-4974-ae72-d2ffd6c48672" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss: 3.1545\n" ] } ], "source": [ "print(f\"Loss: {train_result.training_loss:.4f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ij-thamsKWfM" }, "source": [ "### Evaluate" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "ifhuhzePKUPy", "outputId": "3c8ef747-dba4-4954-e941-adc6ab13ee50" }, "outputs": [ { "data": { "text/html": [ "\n", "

\n", " \n", " \n", " [1403/1403 05:48]\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eval_results = trainer.evaluate()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "9h4SsWV8Kiiv", "outputId": "24686d0c-8527-4b65-9233-3d03b57c2eb9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loss: 2.9576\n", "Perplexity: 19.2522\n" ] } ], "source": [ "print(f\"Loss: {eval_results['eval_loss']:.4f}\")\n", "print(f\"Perplexity: {eval_results['eval_perplexity']:.4f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "j36Abz3IK3SD" }, "source": [ "### Save Original + Merged" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "OAPIen-JKkGk", "outputId": "f1e333b3-424e-47b5-fab1-6614dddf97e1" }, "outputs": [ { "data": { "text/plain": [ "('./biogpt-lora-balanced/tokenizer_config.json',\n", " './biogpt-lora-balanced/special_tokens_map.json',\n", " './biogpt-lora-balanced/vocab.json',\n", " './biogpt-lora-balanced/merges.txt',\n", " './biogpt-lora-balanced/added_tokens.json')" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save LoRA adapter (small)\n", "trainer.save_model(\"./biogpt-lora-balanced\")\n", "tokenizer.save_pretrained(\"./biogpt-lora-balanced\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "lT6E4gxXLJjk", "outputId": "2c26bbf3-b702-47e2-fa44-ff60d6c8b01e" }, "outputs": [ { "data": { "text/plain": [ "('./biogpt-lora-balanced-merged/tokenizer_config.json',\n", " './biogpt-lora-balanced-merged/special_tokens_map.json',\n", " './biogpt-lora-balanced-merged/vocab.json',\n", " './biogpt-lora-balanced-merged/merges.txt',\n", " './biogpt-lora-balanced-merged/added_tokens.json')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Save merged model for CPU\n", "model_merged = model.merge_and_unload()\n", "model_merged_fp32 = model_merged.to(torch.float32)\n", "model_merged_fp32.save_pretrained(\"./biogpt-lora-balanced-merged\")\n", "tokenizer.save_pretrained(\"./biogpt-lora-balanced-merged\")" ] }, { "cell_type": "markdown", "metadata": { "id": "HqpTmu11LNSX" }, "source": [ "### Comprehensive Metrics (20 samples)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "ABi6BrVVLKmy" }, "outputs": [], "source": [ "import evaluate\n", "from tqdm import tqdm" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "referenced_widgets": [ "9cb9b8cc70af487aab9115871eb5fee1", "4e5cb2fa093241dabc38e73ce48acd47", "e3cda79f50774528801820ffcbb8b95d" ] }, "id": "7uaG8lTHLSpy", "outputId": "705833ff-cd8f-4ede-e072-f1d882b8a820" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9cb9b8cc70af487aab9115871eb5fee1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4e5cb2fa093241dabc38e73ce48acd47", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e3cda79f50774528801820ffcbb8b95d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading builder script: 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n", "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n" ] } ], "source": [ "bleu_metric = evaluate.load(\"sacrebleu\")\n", "rouge_metric = evaluate.load(\"rouge\")\n", "meteor_metric = evaluate.load(\"meteor\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "wYZrTM7rLVuj", "outputId": "c02cbfcf-b73a-4e0e-a7a9-97a6cdae59b5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Evaluating: 100%|██████████| 20/20 [00:34<00:00, 1.71s/it]\n" ] } ], "source": [ "model_merged.eval()\n", "sample_indices = np.random.choice(len(dataset['validation']), 20, replace=False)\n", "\n", "predictions = []\n", "references = []\n", "\n", "for idx in tqdm(sample_indices, desc=\"Evaluating\"):\n", " sample = dataset['validation'][int(idx)]\n", "\n", " instruction = sample.get('instruction', '')\n", " input_text = sample.get('input', '')\n", " output_text = sample.get('output', '')\n", "\n", " prompt = f\"{instruction} {input_text}\".strip() if input_text else instruction.strip()\n", "\n", " inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=256)\n", " inputs = {k: v.to(model_merged.device) for k, v in inputs.items()}\n", "\n", " with torch.no_grad():\n", " outputs = model_merged.generate(\n", " **inputs,\n", " max_new_tokens=100,\n", " temperature=0.7, # Balanced creativity\n", " top_p=0.9,\n", " do_sample=True,\n", " repetition_penalty=1.2, # Avoid repetition\n", " length_penalty=1.0, # Natural length\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " generated = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", " prediction = generated[len(prompt):].strip() if generated.startswith(prompt) else generated.strip()\n", "\n", " predictions.append(prediction or \"No response\")\n", " references.append(output_text.strip())\n", "\n", "# Calculate metrics\n", "metrics = {}\n", "\n", "try:\n", " bleu = bleu_metric.compute(predictions=predictions, references=[[r] for r in references])\n", " metrics['BLEU'] = bleu['score']\n", "except:\n", " metrics['BLEU'] = 0.0\n", "\n", "try:\n", " rouge = rouge_metric.compute(predictions=predictions, references=references)\n", " metrics['ROUGE-1'] = rouge['rouge1']\n", " metrics['ROUGE-2'] = rouge['rouge2']\n", " metrics['ROUGE-L'] = rouge['rougeL']\n", "except:\n", " metrics['ROUGE-1'] = metrics['ROUGE-2'] = metrics['ROUGE-L'] = 0.0\n", "\n", "try:\n", " meteor = meteor_metric.compute(predictions=predictions, references=references)\n", " metrics['METEOR'] = meteor['meteor']\n", "except:\n", " metrics['METEOR'] = 0.0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "VF9Fow9KLc5t", "outputId": "95b3b231-090e-49ca-e245-6df28173599d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BLEU: 2.0768\n", "ROUGE-1: 0.2743\n", "ROUGE-2: 0.0379\n", "ROUGE-L: 0.1302\n", "METEOR: 0.2364\n" ] } ], "source": [ "for key, value in metrics.items():\n", " print(f\"{key}: {value:.4f}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "4BKfefT2Lvks" }, "source": [ "### Quality Test with Analysis" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true }, "id": "5n0h4P_BLjmS", "outputId": "9c071927-dc0b-4d32-9cac-ca362c4c3e92" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "1. Q: What are the symptoms of diabetes?\n", " A: If you are a doctor, please answer the medical questions based on the patient's description. Hello, I have been suffering from this problem for about 6 months now. I have had a routine test and blood sugar levels. The results show that they were not in normal range but there is some evidence to suggest a glucose level above which my blood sugar level goes up. They also feel very tired and exhausted when doing so. Please advise me what could be causing these problems. It would seem reasonable if I follow a diet low in fat and high in protein, as it has been shown that an increase in carbohydrate intake leads to more weight gain than with any other type of dietary change. You should also start taking supplements\n", " Keyword coverage: 20% (1/5)\n", " Found: weight\n", "\n", "2. Q: How to treat high blood pressure?\n", " A: What should I do if you have been having headaches or any other symptoms. Hello, I am Chat Doctor. I am a female patient with low back pain and headache for about 2 years now. The first visit was in January 2011, since then I got my doctor's advice regarding me. I had some blood tests done and they were all normal. They did not give me enough information concerning the reason of your headache (which is sometimes very severe). If I want to know what might be causing this thing then you can consult a neurologist and get an MRI scan done. You may also need an ultrasound of abdomen / pelvis for abdominal evaluation. This will help you understand how much there is an underlying pathology behind your pain\n", " Keyword coverage: 0% (0/5)\n", " Found: \n", "\n", "3. Q: What causes migraine headaches?\n", " A: Are you really worried about? Hello, I have a family history of migraine. My son has been diagnosed with migraine since birth and currently is on an anti-inflammatory medication for my headache. Is there any reason to worry about his daughter? Thanks for your help! Migraine headaches can be caused by many factors such as genetic predisposition, hormonal changes, sleep disturbance, stress, anxiety etc. The most important thing that must be taken into consideration in the management of this disorder is how long it will last if we do not wait until he has developed serious problems like hypertension or heart disease. If he does develop these issues then he should consult a doctor who may suggest him to go for further investigations (lab tests) to see whether there\n", " Keyword coverage: 60% (3/5)\n", " Found: stress, hormonal, sleep\n", "\n", "======================================================================\n", "QUALITY SCORE: 26.7%\n", "======================================================================\n", "\n", " Expected: 60-80% coverage = Good quality\n", " Higher coverage = More comprehensive responses\n" ] } ], "source": [ "test_cases = [\n", " {\n", " \"question\": \"What are the symptoms of diabetes?\",\n", " \"expected_keywords\": [\"thirst\", \"urination\", \"fatigue\", \"weight\", \"vision\"],\n", " },\n", " {\n", " \"question\": \"How to treat high blood pressure?\",\n", " \"expected_keywords\": [\"lifestyle\", \"diet\", \"exercise\", \"medication\", \"salt\"],\n", " },\n", " {\n", " \"question\": \"What causes migraine headaches?\",\n", " \"expected_keywords\": [\"trigger\", \"stress\", \"food\", \"hormonal\", \"sleep\"],\n", " },\n", "]\n", "\n", "total_score = 0\n", "\n", "for i, test in enumerate(test_cases, 1):\n", " prompt = test[\"question\"]\n", " keywords = test[\"expected_keywords\"]\n", "\n", " inputs = tokenizer(prompt, return_tensors=\"pt\")\n", " inputs = {k: v.to(model_merged.device) for k, v in inputs.items()}\n", "\n", " with torch.no_grad():\n", " outputs = model_merged.generate(\n", " **inputs,\n", " max_new_tokens=150,\n", " temperature=0.7,\n", " top_p=0.9,\n", " do_sample=True,\n", " repetition_penalty=1.2,\n", " pad_token_id=tokenizer.pad_token_id\n", " )\n", "\n", " text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n", " response = text[len(prompt):].strip().lower()\n", "\n", " # Check keyword coverage\n", " found_keywords = [kw for kw in keywords if kw in response]\n", " coverage = len(found_keywords) / len(keywords) * 100\n", " total_score += coverage\n", "\n", " print(f\"\\n{i}. Q: {prompt}\")\n", " print(f\" A: {text[len(prompt):].strip()}\")\n", " print(f\" Keyword coverage: {coverage:.0f}% ({len(found_keywords)}/{len(keywords)})\")\n", " print(f\" Found: {', '.join(found_keywords)}\")\n", "\n", "avg_score = total_score / len(test_cases)\n", "print(f\"\\n\" + \"=\" * 70)\n", "print(f\"QUALITY SCORE: {avg_score:.1f}%\")\n", "print(\"=\" * 70)\n", "print(\"\\n Expected: 60-80% coverage = Good quality\")\n", "print(\" Higher coverage = More comprehensive responses\")" ] }, { "cell_type": "markdown", "metadata": { "id": "mLm_plb1MDC1" }, "source": [ "### Download" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "2dfbjjwLNkEH" }, "outputs": [], "source": [ "import shutil\n", "from google.colab import files" ] }, { "cell_type": "code", "source": [ "# For small download (LoRA adapter only - ~20MB):\n", "shutil.make_archive('biogpt-lora', 'zip', './biogpt-lora-balanced')\n", "files.download('biogpt-lora.zip')" ], "metadata": { "id": 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