From beacdf756ceac9e8c2bbfc476adb42e71d58d5ca Mon Sep 17 00:00:00 2001 From: Nik Date: Sun, 26 Oct 2025 22:11:51 +0530 Subject: [PATCH] Copy base notebook. --- .../nikhil_raut/week6_challenge.ipynb | 571 ++++++++++++++++++ 1 file changed, 571 insertions(+) create mode 100644 week6/community-contributions/nikhil_raut/week6_challenge.ipynb diff --git a/week6/community-contributions/nikhil_raut/week6_challenge.ipynb b/week6/community-contributions/nikhil_raut/week6_challenge.ipynb new file mode 100644 index 0000000..14abeab --- /dev/null +++ b/week6/community-contributions/nikhil_raut/week6_challenge.ipynb @@ -0,0 +1,571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "db8736a7-ed94-441c-9556-831fa57b5a10", + "metadata": {}, + "source": [ + "# The Product Pricer Continued\n", + "\n", + "A model that can estimate how much something costs, from its description.\n", + "\n", + "## AT LAST - it's time for Fine Tuning!\n", + "\n", + "After all this data preparation, and old school machine learning, we've finally arrived at the moment you've been waiting for. Fine-tuning a model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a", + "metadata": {}, + "outputs": [], + "source": [ + "# imports\n", + "\n", + "import os\n", + "import re\n", + "import math\n", + "import json\n", + "import random\n", + "from dotenv import load_dotenv\n", + "from huggingface_hub import login\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pickle\n", + "from collections import Counter\n", + "from openai import OpenAI\n", + "from anthropic import Anthropic" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c", + "metadata": {}, + "outputs": [], + "source": [ + "# environment\n", + "\n", + "load_dotenv(override=True)\n", + "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", + "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n", + "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4dd3aad2-6f99-433c-8792-e461d2f06622", + "metadata": {}, + "outputs": [], + "source": [ + "# Log in to HuggingFace\n", + "\n", + "hf_token = os.environ['HF_TOKEN']\n", + "login(hf_token, add_to_git_credential=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "884a50bd-8cae-425e-8e56-f079fc3e65ce", + "metadata": {}, + "outputs": [], + "source": [ + "# moved our Tester into a separate package\n", + "# call it with Tester.test(function_name, test_dataset)\n", + "\n", + "from items import Item\n", + "from testing import Tester" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b", + "metadata": {}, + "outputs": [], + "source": [ + "openai = OpenAI()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's avoid curating all our data again! Load in the pickle files:\n", + "\n", + "with open('train.pkl', 'rb') as file:\n", + " train = pickle.load(file)\n", + "\n", + "with open('test.pkl', 'rb') as file:\n", + " test = pickle.load(file)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8367135-f40e-43e1-8f3c-09e990ab1194", + "metadata": {}, + "outputs": [], + "source": [ + "# OpenAI recommends fine-tuning with populations of 50-100 examples\n", + "# But as our examples are very small, I'm suggesting we go with 200 examples (and 1 epoch)\n", + "\n", + "fine_tune_train = train[:200]\n", + "fine_tune_validation = train[200:250]" + ] + }, + { + "cell_type": "markdown", + "id": "8be4a889-81c3-42b1-a2fc-034cdc7321a6", + "metadata": {}, + "source": [ + "# Step 1\n", + "\n", + "Prepare our data for fine-tuning in JSONL (JSON Lines) format and upload to OpenAI" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8ae2fb3c-1cff-4ce3-911e-627c970edd7b", + "metadata": {}, + "outputs": [], + "source": [ + "# First let's work on a good prompt for a Frontier model\n", + "# Notice that I'm removing the \" to the nearest dollar\"\n", + "# When we train our own models, we'll need to make the problem as easy as possible,\n", + "# but a Frontier model needs no such simplification.\n", + "\n", + "def messages_for(item):\n", + " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", + " user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " {\"role\": \"assistant\", \"content\": f\"Price is ${item.price:.2f}\"}\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1aa280f6-1227-426a-a2e2-1ce985feba1e", + "metadata": {}, + "outputs": [], + "source": [ + "messages_for(train[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c0e5b56c-8a0b-4d8e-a112-ce87efb4e152", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the items into a list of json objects - a \"jsonl\" string\n", + "# Each row represents a message in the form:\n", + "# {\"messages\" : [{\"role\": \"system\", \"content\": \"You estimate prices...\n", + "\n", + "\n", + "def make_jsonl(items):\n", + " result = \"\"\n", + " for item in items:\n", + " messages = messages_for(item)\n", + " messages_str = json.dumps(messages)\n", + " result += '{\"messages\": ' + messages_str +'}\\n'\n", + " return result.strip()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e72de93-a6a6-4b35-855e-15786b97bf5f", + "metadata": {}, + "outputs": [], + "source": [ + "print(make_jsonl(train[:3]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7734bff0-95c4-4e67-a87e-7e2254e2c67d", + "metadata": {}, + "outputs": [], + "source": [ + "# Convert the items into jsonl and write them to a file\n", + "\n", + "def write_jsonl(items, filename):\n", + " with open(filename, \"w\") as f:\n", + " jsonl = make_jsonl(items)\n", + " f.write(jsonl)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "393d3ad8-999a-4f99-8c04-339d9166d604", + "metadata": {}, + "outputs": [], + "source": [ + "write_jsonl(fine_tune_train, \"fine_tune_train.jsonl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8e23927f-d73e-4668-ac20-abe6f14a56cb", + "metadata": {}, + "outputs": [], + "source": [ + "write_jsonl(fine_tune_validation, \"fine_tune_validation.jsonl\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d59ad8d2-c61a-448e-b7ed-232f1606970f", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"fine_tune_train.jsonl\", \"rb\") as f:\n", + " train_file = openai.files.create(file=f, purpose=\"fine-tune\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "083fefba-fd54-47ce-9ff3-aabbc200846f", + "metadata": {}, + "outputs": [], + "source": [ + "train_file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "97df3360-0760-4422-a556-5f26d23de6dc", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"fine_tune_validation.jsonl\", \"rb\") as f:\n", + " validation_file = openai.files.create(file=f, purpose=\"fine-tune\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1abb8f3-9e52-4061-970c-fcf399d8ffa3", + "metadata": {}, + "outputs": [], + "source": [ + "validation_file" + ] + }, + { + "cell_type": "markdown", + "id": "466052b9-9fb9-48f6-8cf9-c74e6ddc1394", + "metadata": {}, + "source": [ + "# Step 2\n", + "\n", + "I love Weights and Biases - a beautiful, free platform for monitoring training runs. \n", + "Weights and Biases is integrated with OpenAI for fine-tuning.\n", + "\n", + "First set up your weights & biases free account at:\n", + "\n", + "https://wandb.ai\n", + "\n", + "From the Avatar >> Settings menu, near the bottom, you can create an API key.\n", + "\n", + "Then visit the OpenAI dashboard at:\n", + "\n", + "https://platform.openai.com/account/organization\n", + "\n", + "In the integrations section, you can add your Weights & Biases key.\n", + "\n", + "## And now time to Fine-tune!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7add1a7-a746-4d6e-a5f8-e25629b8b527", + "metadata": {}, + "outputs": [], + "source": [ + "wandb_integration = {\"type\": \"wandb\", \"wandb\": {\"project\": \"gpt-pricer\"}}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "49801e69-9277-4deb-9f33-99efb6b45ac2", + "metadata": {}, + "outputs": [], + "source": [ + "train_file.id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45421b86-5531-4e42-ab19-d6abbb8f4c13", + "metadata": {}, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.create(\n", + " training_file=train_file.id,\n", + " validation_file=validation_file.id,\n", + " model=\"gpt-4o-mini-2024-07-18\",\n", + " seed=42,\n", + " hyperparameters={\"n_epochs\": 1},\n", + " integrations = [wandb_integration],\n", + " suffix=\"pricer\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aeb9de2e-542c-4e83-81c7-b6745133e48b", + "metadata": {}, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.list(limit=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40d24873-8ff5-413f-b0d4-8f77c28f18e1", + "metadata": {}, + "outputs": [], + "source": [ + "job_id = openai.fine_tuning.jobs.list(limit=1).data[0].id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a32aef35-4b38-436c-ad00-d082f758efa7", + "metadata": {}, + "outputs": [], + "source": [ + "job_id" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a7e01247-c133-48e1-93d3-c79c399e6178", + "metadata": {}, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.retrieve(job_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f5150e1-b8de-485f-8eba-cf1e5b00c117", + "metadata": {}, + "outputs": [], + "source": [ + "openai.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id, limit=10).data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b19ea9e9", + "metadata": {}, + "outputs": [], + "source": [ + "import wandb\n", + "from wandb.integration.openai.fine_tuning import WandbLogger\n", + "\n", + "# Log in to Weights & Biases.\n", + "wandb.login()\n", + "# Sync the fine-tuning job with Weights & Biases.\n", + "WandbLogger.sync(fine_tune_job_id=job_id, project=\"gpt-pricer\")" + ] + }, + { + "cell_type": "markdown", + "id": "066fef03-8338-4526-9df3-89b649ad4f0a", + "metadata": {}, + "source": [ + "# Step 3\n", + "\n", + "Test our fine tuned model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa4488cb-3c17-4eda-abd1-53c1c68a491b", + "metadata": {}, + "outputs": [], + "source": [ + "fine_tuned_model_name = openai.fine_tuning.jobs.retrieve(job_id).fine_tuned_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9370937-5a6f-4724-8265-b208663b4450", + "metadata": {}, + "outputs": [], + "source": [ + "fine_tuned_model_name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66ea68e8-ab1b-4f0d-aba4-a59574d8f85e", + "metadata": {}, + "outputs": [], + "source": [ + "# The prompt\n", + "\n", + "def messages_for(item):\n", + " system_message = \"You estimate prices of items. Reply only with the price, no explanation\"\n", + " user_prompt = item.test_prompt().replace(\" to the nearest dollar\",\"\").replace(\"\\n\\nPrice is $\",\"\")\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_message},\n", + " {\"role\": \"user\", \"content\": user_prompt},\n", + " {\"role\": \"assistant\", \"content\": \"Price is $\"}\n", + " ]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ff92d61-0d27-4b0d-8b32-c9891016509b", + "metadata": {}, + "outputs": [], + "source": [ + "# Try this out\n", + "\n", + "messages_for(test[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b1af1888-f94a-4106-b0d8-8a70939eec4e", + "metadata": {}, + "outputs": [], + "source": [ + "# A utility function to extract the price from a string\n", + "\n", + "def get_price(s):\n", + " s = s.replace('$','').replace(',','')\n", + " match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", s)\n", + " return float(match.group()) if match else 0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f138c5b7-bcc1-4085-aced-68dad1bf36b4", + "metadata": {}, + "outputs": [], + "source": [ + "get_price(\"The price is roughly $99.99 because blah blah\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "501a2a7a-69c8-451b-bbc0-398bcb9e1612", + "metadata": {}, + "outputs": [], + "source": [ + "# The function for gpt-4o-mini\n", + "\n", + "def gpt_fine_tuned(item):\n", + " response = openai.chat.completions.create(\n", + " model=fine_tuned_model_name,\n", + " messages=messages_for(item),\n", + " seed=42,\n", + " max_tokens=7\n", + " )\n", + " reply = response.choices[0].message.content\n", + " return get_price(reply)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "843d88b4-364a-431b-b48b-8a7c1f68b786", + "metadata": {}, + "outputs": [], + "source": [ + "print(test[0].price)\n", + "print(gpt_fine_tuned(test[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edd7ada0-15b7-42ec-bbbb-1250e0eb9af1", + "metadata": {}, + "outputs": [], + "source": [ + "print(test[0].test_prompt())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36bdd2c9-1859-4f99-a09f-3ec83b845b30", + "metadata": {}, + "outputs": [], + "source": [ + "Tester.test(gpt_fine_tuned, test)" + ] + } + ], + "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": "3.11.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}