{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "collapsed": true, "id": "pMAzkFCvb8Sn", "outputId": "7fe8df0f-a511-4c67-f62c-afac548031d4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting datasets\n", " Downloading datasets-3.1.0-py3-none-any.whl.metadata (20 kB)\n", "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (0.34.2)\n", "Collecting bitsandbytes\n", " Downloading bitsandbytes-0.44.1-py3-none-manylinux_2_24_x86_64.whl.metadata (3.5 kB)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.16.1)\n", "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n", "Requirement already satisfied: pyarrow>=15.0.0 in 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StepTraining Loss
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n", "/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py:825: UserWarning: cuDNN SDPA backward got grad_output.strides() != output.strides(), attempting to materialize a grad_output with matching strides... (Triggered internally at ../aten/src/ATen/native/cudnn/MHA.cpp:674.)\n", " return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass\n" ] }, { "data": { "text/plain": [ "TrainOutput(global_step=1025, training_loss=0.583683032524295, metrics={'train_runtime': 2589.4824, 'train_samples_per_second': 6.338, 'train_steps_per_second': 0.396, 'total_flos': 3.783418353549312e+17, 'train_loss': 0.583683032524295, 'epoch': 0.9992688276870583})" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"/content/medquad.csv\")\n", "dataset = Dataset.from_pandas(df)\n", "\n", "def preprocess_function(examples):\n", " question = examples[\"question\"] if examples[\"question\"] is not None else \"\"\n", " answer = examples[\"answer\"] if examples[\"answer\"] is not None else \"\"\n", "\n", " examples[\"text\"] = \" \" + question + \" \" + answer + \" \"\n", " return examples\n", "\n", "dataset = dataset.map(preprocess_function)\n", "def tokenize_function(examples):\n", " return tokenizer(examples[\"text\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n", "\n", "tokenized_datasets = dataset.map(tokenize_function, batched=True)\n", "\n", "tokenized_datasets = tokenized_datasets.map(lambda examples: {'labels': examples['input_ids']}, batched=True)\n", "\n", "# Training arguments\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " per_device_train_batch_size=4, # Reduced batch size from 16 to 4\n", " gradient_accumulation_steps=4, # Increased gradient accumulation steps from 2 to 4\n", " num_train_epochs=1,\n", " fp16=True,\n", " logging_dir=\"./logs\",\n", " learning_rate=2e-5,\n", " save_steps=100,\n", " dataloader_num_workers=8, # Utilize more CPU cores for data loading\n", " remove_unused_columns=True, # Remove unused columns from the dataset\n", ")\n", "\n", "# Trainer initialization\n", "trainer = Trainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=tokenized_datasets,\n", ")\n", "\n", "# Start training\n", "trainer.train()\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2N_8NspTsmxA", "outputId": "82b87812-5bbc-4653-949a-f4fb2d65793d" }, "outputs": [ { "data": { "text/plain": [ "('/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/tokenizer_config.json',\n", " '/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/special_tokens_map.json',\n", " '/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned/tokenizer/tokenizer.json')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import os\n", "\n", "\n", "model_folder = \"/content/drive/MyDrive/Bio-Medical-Llama-3-8B-finetuned\"\n", "\n", "\n", "os.makedirs(model_folder, exist_ok=True)\n", "\n", "\n", "trainer.save_model(os.path.join(model_folder, \"model\"))\n", "tokenizer.save_pretrained(os.path.join(model_folder, \"tokenizer\"))" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "04a82101b6704de193d126f740f8ba0a": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "ProgressStyleModel", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "07883ddd58e74c028db1f071f6b79523": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": "FloatProgressModel", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", 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