diff --git a/ch07/01_main-chapter-code/README.md b/ch07/01_main-chapter-code/README.md index 6e88af7..c84be1c 100644 --- a/ch07/01_main-chapter-code/README.md +++ b/ch07/01_main-chapter-code/README.md @@ -8,4 +8,4 @@ ### Optional Code -In progress ... \ No newline at end of file +- [load-finetuned-model.ipynb](load-finetuned-model.ipynb) is a standalone Jupyter notebook to load the instruction finetuned model we created in this chapter \ No newline at end of file diff --git a/ch07/01_main-chapter-code/load-finetuned-model.ipynb b/ch07/01_main-chapter-code/load-finetuned-model.ipynb new file mode 100644 index 0000000..95d463e --- /dev/null +++ b/ch07/01_main-chapter-code/load-finetuned-model.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d", + "metadata": {}, + "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": {}, + "source": [ + "# Load And Use Finetuned Model" + ] + }, + { + "cell_type": "markdown", + "id": "466b564e-4fd5-4d76-a3a1-63f9f0993b7e", + "metadata": {}, + "source": [ + "This notebook contains minimal code to load the finetuned model that was instruction finetuned and saved in chapter 7 via [ch07.ipynb](ch07.ipynb)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fd80e5f5-0f79-4a6c-bf31-2026e7d30e52", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tiktoken version: 0.7.0\n", + "torch version: 2.3.1\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": {}, + "outputs": [], + "source": [ + "from pathlib import Path\n", + "\n", + "finetuned_model_path = Path(\"gpt2-medium355M-sft.pth\")\n", + "if not finetuned_model_path.exists():\n", + " print(\n", + " f\"Could not find '{finetuned_model_path}'.\\n\"\n", + " \"Run the `ch07.ipynb` notebook to finetune and save finetuned model.\"\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "fb02584a-5e31-45d5-8377-794876907bc6", + "metadata": {}, + "outputs": [], + "source": [ + "from gpt_download import download_and_load_gpt2\n", + "from previous_chapters import GPTModel, load_weights_into_gpt\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-medium (355M)\"\n", + "\n", + "BASE_CONFIG.update(model_configs[CHOOSE_MODEL])\n", + "\n", + "model_size = CHOOSE_MODEL.split(\" \")[-1].lstrip(\"(\").rstrip(\")\")\n", + "model = GPTModel(BASE_CONFIG)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "f1ccf2b7-176e-4cfd-af7a-53fb76010b94", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "\n", + "model.load_state_dict(torch.load(\"gpt2-medium355M-sft.pth\", map_location=torch.device(\"cpu\")))\n", + "model.eval();" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a1fd174e-9555-46c5-8780-19b0aa4f26e5", + "metadata": {}, + "outputs": [], + "source": [ + "import tiktoken\n", + "\n", + "tokenizer = tiktoken.get_encoding(\"gpt2\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2a4c0129-efe5-46e9-bb90-ba08d407c1a2", + "metadata": {}, + "outputs": [], + "source": [ + "prompt = \"\"\"Below is an instruction that describes a task. Write a response \n", + "that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Convert the active sentence to passive: 'The chef cooks the meal every day.'\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1e26862c-10b5-4a0f-9dd6-b6ddbad2fc3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The meal is cooked every day by the chef.\n" + ] + } + ], + "source": [ + "from previous_chapters import (\n", + " generate,\n", + " text_to_token_ids,\n", + " token_ids_to_text\n", + ")\n", + "\n", + "def extract_response(response_text, input_text):\n", + " return response_text[len(input_text):].replace(\"### Response:\", \"\").strip()\n", + "\n", + "torch.manual_seed(123)\n", + "\n", + "token_ids = generate(\n", + " model=model,\n", + " idx=text_to_token_ids(prompt, tokenizer),\n", + " max_new_tokens=35,\n", + " context_size=BASE_CONFIG[\"context_length\"],\n", + " eos_id=50256\n", + ")\n", + "\n", + "response = token_ids_to_text(token_ids, tokenizer)\n", + "response = extract_response(response, prompt)\n", + "print(response)" + ] + } + ], + "metadata": { + "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 +}