diff --git a/ch06/01_main-chapter-code/load-finetuned-model.ipynb b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
index 7ac6f5e6..4e931695 100644
--- a/ch06/01_main-chapter-code/load-finetuned-model.ipynb
+++ b/ch06/01_main-chapter-code/load-finetuned-model.ipynb
@@ -3,7 +3,9 @@
{
"cell_type": "markdown",
"id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d",
- "metadata": {},
+ "metadata": {
+ "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d"
+ },
"source": [
"
\n",
"\n",
@@ -23,7 +25,9 @@
{
"cell_type": "markdown",
"id": "f3f83194-82b9-4478-9550-5ad793467bd0",
- "metadata": {},
+ "metadata": {
+ "id": "f3f83194-82b9-4478-9550-5ad793467bd0"
+ },
"source": [
"# Load And Use Finetuned Model"
]
@@ -31,7 +35,9 @@
{
"cell_type": "markdown",
"id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e",
- "metadata": {},
+ "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)."
]
@@ -40,7 +46,13 @@
"cell_type": "code",
"execution_count": 1,
"id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
- "metadata": {},
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52",
+ "outputId": "9eeefb8e-a7eb-4d62-cf78-c797b3ed4e2e"
+ },
"outputs": [
{
"name": "stdout",
@@ -66,7 +78,9 @@
"cell_type": "code",
"execution_count": 2,
"id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201",
- "metadata": {},
+ "metadata": {
+ "id": "ed86d6b7-f32d-4601-b585-a2ea3dbf7201"
+ },
"outputs": [],
"source": [
"from pathlib import Path\n",
@@ -83,7 +97,9 @@
"cell_type": "code",
"execution_count": 3,
"id": "fb02584a-5e31-45d5-8377-794876907bc6",
- "metadata": {},
+ "metadata": {
+ "id": "fb02584a-5e31-45d5-8377-794876907bc6"
+ },
"outputs": [],
"source": [
"from previous_chapters import GPTModel\n",
@@ -116,7 +132,9 @@
"cell_type": "code",
"execution_count": 4,
"id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94",
- "metadata": {},
+ "metadata": {
+ "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94"
+ },
"outputs": [],
"source": [
"import torch\n",
@@ -128,6 +146,7 @@
"# 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();"
]
},
@@ -135,7 +154,9 @@
"cell_type": "code",
"execution_count": 5,
"id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5",
- "metadata": {},
+ "metadata": {
+ "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5"
+ },
"outputs": [],
"source": [
"import tiktoken\n",
@@ -147,7 +168,9 @@
"cell_type": "code",
"execution_count": 6,
"id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2",
- "metadata": {},
+ "metadata": {
+ "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2"
+ },
"outputs": [],
"source": [
"# This function was implemented in ch06.ipynb\n",
@@ -167,7 +190,7 @@
"\n",
" # Model inference\n",
" with torch.no_grad():\n",
- " logits = model(input_tensor)[:, -1, :] # Logits of the last output token\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",
@@ -178,7 +201,13 @@
"cell_type": "code",
"execution_count": 7,
"id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
- "metadata": {},
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f",
+ "outputId": "28eb2c02-0e38-4356-b2a3-2bf6accb5316"
+ },
"outputs": [
{
"name": "stdout",
@@ -203,7 +232,13 @@
"cell_type": "code",
"execution_count": 8,
"id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
- "metadata": {},
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "78472e05-cb4e-4ec4-82e8-23777aa90cf8",
+ "outputId": "0cd3cd62-f407-45f3-fa4f-51ff665355eb"
+ },
"outputs": [
{
"name": "stdout",
@@ -226,6 +261,11 @@
}
],
"metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "L4",
+ "provenance": []
+ },
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",