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example code to use the finetuned model
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "id": "1545a16b-bc8d-4e49-b9a6-db6631e7483d", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<table style=\"width:100%\">\n", | ||
| "<tr>\n", | ||
| "<td style=\"vertical-align:middle; text-align:left;\">\n", | ||
| "<font size=\"2\">\n", | ||
| "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", | ||
| "<br>Code repository: <a href=\"https://github.com/rasbt/LLMs-from-scratch\">https://github.com/rasbt/LLMs-from-scratch</a>\n", | ||
| "</font>\n", | ||
| "</td>\n", | ||
| "<td style=\"vertical-align:middle; text-align:left;\">\n", | ||
| "<a href=\"http://mng.bz/orYv\"><img src=\"https://sebastianraschka.com/images/LLMs-from-scratch-images/cover-small.webp\" width=\"100px\"></a>\n", | ||
| "</td>\n", | ||
| "</tr>\n", | ||
| "</table>" | ||
| ] | ||
| }, | ||
| { | ||
| "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 | ||
| } |