mirror of
https://github.com/rasbt/LLMs-from-scratch.git
synced 2025-10-31 18:00:08 +00:00
289 lines
8.1 KiB
Plaintext
289 lines
8.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d",
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"metadata": {
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"id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d"
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},
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"source": [
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"<table style=\"width:100%\">\n",
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"<tr>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<font size=\"2\">\n",
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"Supplementary code for the <a href=\"http://mng.bz/orYv\">Build a Large Language Model From Scratch</a> book by <a href=\"https://sebastianraschka.com\">Sebastian Raschka</a><br>\n",
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"<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n",
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"</font>\n",
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"</td>\n",
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"<td style=\"vertical-align:middle; text-align:left;\">\n",
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"<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n",
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"</td>\n",
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"</tr>\n",
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"</table>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f3f83194-82b9-4478-9550-5ad793467bd0",
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"metadata": {
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"id": "f3f83194-82b9-4478-9550-5ad793467bd0"
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},
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"source": [
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"# Load And Use Finetuned Model"
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]
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},
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{
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"cell_type": "markdown",
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"id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e",
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"metadata": {
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"id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e"
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},
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"source": [
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"This notebook contains minimal code to load the finetuned model that was created and saved in chapter 6 via [ch06.ipynb](ch06.ipynb)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
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"outputId": "9eeefb8e-a7eb-4d62-cf78-c797b3ed4e2e"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"tiktoken version: 0.7.0\n",
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"torch version: 2.4.0\n"
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]
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}
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],
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"source": [
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"from importlib.metadata import version\n",
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"\n",
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"pkgs = [\n",
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" \"tiktoken\", # Tokenizer\n",
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" \"torch\", # Deep learning library\n",
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"]\n",
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"for p in pkgs:\n",
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" print(f\"{p} version: {version(p)}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201",
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"metadata": {
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"id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201"
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},
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"outputs": [],
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"source": [
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"from pathlib import Path\n",
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"\n",
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"finetuned_model_path = Path(\"review_classifier.pth\")\n",
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"if not finetuned_model_path.exists():\n",
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" print(\n",
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" f\"Could not find '{finetuned_model_path}'.\\n\"\n",
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" \"Run the `ch06.ipynb` notebook to finetune and save the finetuned model.\"\n",
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" )"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "fb02584a-5e31-45d5-8377-794876907bc6",
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"metadata": {
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"id": "fb02584a-5e31-45d5-8377-794876907bc6"
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},
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"outputs": [],
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"source": [
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"from previous_chapters import GPTModel\n",
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"\n",
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"\n",
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"BASE_CONFIG = {\n",
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" \"vocab_size\": 50257, # Vocabulary size\n",
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" \"context_length\": 1024, # Context length\n",
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" \"drop_rate\": 0.0, # Dropout rate\n",
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" \"qkv_bias\": True # Query-key-value bias\n",
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"}\n",
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"\n",
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"model_configs = {\n",
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" \"gpt2-small (124M)\": {\"emb_dim\": 768, \"n_layers\": 12, \"n_heads\": 12},\n",
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" \"gpt2-medium (355M)\": {\"emb_dim\": 1024, \"n_layers\": 24, \"n_heads\": 16},\n",
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" \"gpt2-large (774M)\": {\"emb_dim\": 1280, \"n_layers\": 36, \"n_heads\": 20},\n",
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" \"gpt2-xl (1558M)\": {\"emb_dim\": 1600, \"n_layers\": 48, \"n_heads\": 25},\n",
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"}\n",
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"\n",
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"CHOOSE_MODEL = \"gpt2-small (124M)\"\n",
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"\n",
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"BASE_CONFIG.update(model_configs[CHOOSE_MODEL])\n",
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"\n",
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"# Initialize base model\n",
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"model = GPTModel(BASE_CONFIG)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94",
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"metadata": {
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"id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94"
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"# Convert model to classifier as in section 6.5 in ch06.ipynb\n",
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"num_classes = 2\n",
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"model.out_head = torch.nn.Linear(in_features=BASE_CONFIG[\"emb_dim\"], out_features=num_classes)\n",
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"\n",
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"# Then load pretrained weights\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"model.load_state_dict(torch.load(\"review_classifier.pth\", map_location=device, weights_only=True))\n",
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"model.to(device)\n",
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"model.eval();"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5",
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"metadata": {
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"id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5"
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},
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"outputs": [],
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"source": [
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"import tiktoken\n",
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"\n",
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"tokenizer = tiktoken.get_encoding(\"gpt2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2",
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"metadata": {
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"id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2"
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},
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"outputs": [],
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"source": [
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"# This function was implemented in ch06.ipynb\n",
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"def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):\n",
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" model.eval()\n",
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"\n",
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" # Prepare inputs to the model\n",
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" input_ids = tokenizer.encode(text)\n",
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" supported_context_length = model.pos_emb.weight.shape[0]\n",
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"\n",
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" # Truncate sequences if they too long\n",
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" input_ids = input_ids[:min(max_length, supported_context_length)]\n",
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"\n",
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" # Pad sequences to the longest sequence\n",
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" input_ids += [pad_token_id] * (max_length - len(input_ids))\n",
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" input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension\n",
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"\n",
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" # Model inference\n",
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" with torch.no_grad():\n",
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" logits = model(input_tensor.to(device))[:, -1, :] # Logits of the last output token\n",
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" predicted_label = torch.argmax(logits, dim=-1).item()\n",
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"\n",
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" # Return the classified result\n",
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" return \"spam\" if predicted_label == 1 else \"not spam\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
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"outputId": "28eb2c02-0e38-4356-b2a3-2bf6accb5316"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spam\n"
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]
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}
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],
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"source": [
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"text_1 = (\n",
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" \"You are a winner you have been specially\"\n",
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" \" selected to receive $1000 cash or a $2000 award.\"\n",
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")\n",
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"\n",
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"print(classify_review(\n",
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" text_1, model, tokenizer, device, max_length=120\n",
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"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
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"outputId": "0cd3cd62-f407-45f3-fa4f-51ff665355eb"
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"not spam\n"
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]
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}
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],
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"source": [
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"text_2 = (\n",
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" \"Hey, just wanted to check if we're still on\"\n",
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" \" for dinner tonight? Let me know!\"\n",
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")\n",
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"\n",
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"print(classify_review(\n",
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" text_2, model, tokenizer, device, max_length=120\n",
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"))"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"gpuType": "L4",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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