{ "cells": [ { "cell_type": "markdown", "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d", "metadata": { "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d" }, "source": [ "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "Supplementary code for the Build a Large Language Model From Scratch book by Sebastian Raschka
\n", "
Code repository: https://github.com/rasbt/LLMs-from-scratch\n", "\n", "		\n", "\n", "
" ] }, { "cell_type": "markdown", "id": "f3f83194-82b9-4478-9550-5ad793467bd0", "metadata": { "id": "f3f83194-82b9-4478-9550-5ad793467bd0" }, "source": [ "# Load And Use Finetuned Model" ] }, { "cell_type": "markdown", "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e", "metadata": { "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e" }, "source": [ "This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)." ] }, { "cell_type": "code", "execution_count": 1, "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52", "outputId": "9eeefb8e-a7eb-4d62-cf78-c797b3ed4e2e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tiktoken version: 0.7.0\n", "torch version: 2.4.0\n" ] } ], "source": [ "from importlib.metadata import version\n", "\n", "pkgs = [\n", " \"tiktoken\", # Tokenizer\n", " \"torch\", # Deep learning library\n", "]\n", "for p in pkgs:\n", " print(f\"{p} version: {version(p)}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201", "metadata": { "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201" }, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "finetuned_model_path = Path(\"review_classifier.pth\")\n", "if not finetuned_model_path.exists():\n", " print(\n", " f\"Could not find '{finetuned_model_path}'.\\n\"\n", " \"Run the `ch06.ipynb` notebook to finetune and save the finetuned model.\"\n", " )" ] }, { "cell_type": "code", "execution_count": 3, "id": "fb02584a-5e31-45d5-8377-794876907bc6", "metadata": { "id": "fb02584a-5e31-45d5-8377-794876907bc6" }, "outputs": [], "source": [ "from previous_chapters import GPTModel\n", "\n", "\n", "BASE_CONFIG = {\n", " \"vocab_size\": 50257, # Vocabulary size\n", " \"context_length\": 1024, # Context length\n", " \"drop_rate\": 0.0, # Dropout rate\n", " \"qkv_bias\": True # Query-key-value bias\n", "}\n", "\n", "model_configs = {\n", " \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n", " \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n", " \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n", " \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n", "}\n", "\n", "CHOOSE_MODEL = \"gpt2-small (124M)\"\n", "\n", "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])\n", "\n", "# Initialize base model\n", "model = GPTModel(BASE_CONFIG)" ] }, { "cell_type": "code", "execution_count": 4, "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94", "metadata": { "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94" }, "outputs": [], "source": [ "import torch\n", "\n", "# Convert model to classifier as in section 6.5 in ch06.ipynb\n", "num_classes = 2\n", "model.out_head = torch.nn.Linear(in_features=BASE_CONFIG[\"emb_dim\"], out_features=num_classes)\n", "\n", "# Then load pretrained weights\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device, weights_only=True))\n", "model.to(device)\n", "model.eval();" ] }, { "cell_type": "code", "execution_count": 5, "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5", "metadata": { "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5" }, "outputs": [], "source": [ "import tiktoken\n", "\n", "tokenizer = tiktoken.get_encoding(\"gpt2\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2", "metadata": { "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2" }, "outputs": [], "source": [ "# This function was implemented in ch06.ipynb\n", "def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):\n", " model.eval()\n", "\n", " # Prepare inputs to the model\n", " input_ids = tokenizer.encode(text)\n", " supported_context_length = model.pos_emb.weight.shape[0]\n", "\n", " # Truncate sequences if they too long\n", " input_ids = input_ids[:min(max_length, supported_context_length)]\n", "\n", " # Pad sequences to the longest sequence\n", " input_ids += [pad_token_id] * (max_length - len(input_ids))\n", " input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension\n", "\n", " # Model inference\n", " with torch.no_grad():\n", " logits = model(input_tensor.to(device))[:, -1, :] # Logits of the last output token\n", " predicted_label = torch.argmax(logits, dim=-1).item()\n", "\n", " # Return the classified result\n", " return \"spam\" if predicted_label == 1 else \"not spam\"" ] }, { "cell_type": "code", "execution_count": 7, "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f", "outputId": "28eb2c02-0e38-4356-b2a3-2bf6accb5316" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "spam\n" ] } ], "source": [ "text_1 = (\n", " \"You are a winner you have been specially\"\n", " \" selected to receive $1000 cash or a $2000 award.\"\n", ")\n", "\n", "print(classify_review(\n", " text_1, model, tokenizer, device, max_length=120\n", "))" ] }, { "cell_type": "code", "execution_count": 8, "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8", "outputId": "0cd3cd62-f407-45f3-fa4f-51ff665355eb" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "not spam\n" ] } ], "source": [ "text_2 = (\n", " \"Hey, just wanted to check if we're still on\"\n", " \" for dinner tonight? Let me know!\"\n", ")\n", "\n", "print(classify_review(\n", " text_2, model, tokenizer, device, max_length=120\n", "))" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "L4", "provenance": [] }, "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 5 }