326 lines
11 KiB
Plaintext
326 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "db8736a7-ed94-441c-9556-831fa57b5a10",
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"metadata": {},
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"source": [
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"# The Product Pricer Fine Tuning\n",
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"\n",
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"Submitted By: Bharat Puri\n",
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"\n",
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"A model that can estimate how much something costs, from its description.\n"
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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": "681c717b-4c24-4ac3-a5f3-3c5881d6e70a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import re\n",
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"import math\n",
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"import json\n",
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"import random\n",
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"from dotenv import load_dotenv\n",
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"from huggingface_hub import login\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import pickle\n",
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"from collections import Counter\n",
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"import sys\n",
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"sys.path.append(os.path.abspath(os.path.join(\"..\", \"..\"))) \n",
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"from openai import OpenAI\n",
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"from anthropic import Anthropic\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.metrics import mean_absolute_error\n"
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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": "36d05bdc-0155-4c72-a7ee-aa4e614ffd3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# environment\n",
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"\n",
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"load_dotenv(override=True)\n",
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')"
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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": null,
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"id": "4dd3aad2-6f99-433c-8792-e461d2f06622",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Log in to HuggingFace\n",
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"\n",
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"hf_token = os.environ['HF_TOKEN']\n",
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"login(hf_token, add_to_git_credential=True)"
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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": null,
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"id": "884a50bd-8cae-425e-8e56-f079fc3e65ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 1 – Load and Inspect Dataset (CSV files)\n",
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"# =============================================\n",
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"\n",
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"df_input = pd.read_csv(\"../../human_input.csv\")\n",
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"df_output = pd.read_csv(\"../../human_output.csv\")\n",
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"\n",
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"print(\"Input columns:\", df_input.columns.tolist())\n",
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"print(\"Output columns:\", df_output.columns.tolist())\n",
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"\n",
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"# Detect correct column names automatically\n",
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"input_col = df_input.columns[0] # first column name\n",
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"output_col = df_output.columns[0] # first column name\n",
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"\n",
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"data = pd.DataFrame({\n",
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" \"prompt\": df_input[input_col].astype(str),\n",
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" \"completion\": df_output[output_col].astype(str)\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": null,
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"id": "b0a6fb86-74a4-403c-ab25-6db2d74e9d2b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 2 – Split into Train and Validation Sets\n",
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"# =============================================\n",
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"\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"# Keep this small to minimize cost\n",
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"train_df, val_df = train_test_split(data, test_size=0.2, random_state=42)\n",
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"\n",
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"print(f\"Training samples: {len(train_df)} | Validation samples: {len(val_df)}\")\n",
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"\n",
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"# Save to JSONL format (required by OpenAI fine-tuning API)\n",
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"train_df.to_json(\"train.jsonl\", orient=\"records\", lines=True)\n",
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"val_df.to_json(\"val.jsonl\", orient=\"records\", lines=True)\n",
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"\n",
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"print(\"✅ Train and validation data prepared successfully.\")"
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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": null,
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"id": "c830ed3e-24ee-4af6-a07b-a1bfdcd39278",
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"metadata": {},
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"outputs": [],
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"source": [
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"train_df.head(3)\n",
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"val_df.head(3)\n"
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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": null,
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"id": "5c9b05f4-c9eb-462c-8d86-de9140a2d985",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 3 – Define Fine-Tuning Configuration\n",
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"# =============================================\n",
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"\n",
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"hyperparams = {\n",
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" \"model\": \"gpt-4o-mini\", \n",
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" \"n_epochs\": 1, \n",
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" \"batch_size\": 4, # Small batch = less token use\n",
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" \"learning_rate_multiplier\": 0.5, # Gentle learning rate\n",
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" \"suffix\": \"week6_lowcost_bharat\" # Custom suffix for tracking\n",
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"}\n",
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"\n",
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"print(\"✅ Fine-tuning configuration defined:\")\n",
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"for k, v in hyperparams.items():\n",
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" print(f\"{k:25}: {v}\")\n"
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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": null,
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"id": "e8367135-f40e-43e1-8f3c-09e990ab1194",
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"metadata": {},
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"outputs": [],
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"source": [
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"# OpenAI recommends fine-tuning with populations of 50-100 examples\n",
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"# But as our examples are very small, I'm suggesting we go with 200 examples (and 1 epoch)\n",
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"\n",
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"fine_tune_train = train[:200]\n",
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"fine_tune_validation = train[200:250]"
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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": null,
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"id": "8ae2fb3c-1cff-4ce3-911e-627c970edd7b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 4 – Launch Fine-Tuning Job or Simulate\n",
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"# =============================================\n",
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"\n",
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"import time\n",
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"from openai import OpenAI\n",
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"\n",
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"# Initialize the OpenAI client\n",
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"client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))\n",
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"\n",
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"# Toggle this flag to switch between simulation and real fine-tuning\n",
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"simulate = True # ✅ Default: Free simulation mode\n",
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"\n",
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"if simulate:\n",
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" print(\"\\n⚙️ Simulating fine-tuning process (no API cost)...\")\n",
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" for i in range(hyperparams['n_epochs']):\n",
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" print(f\"Epoch {i+1}/{hyperparams['n_epochs']} training...\")\n",
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" time.sleep(1)\n",
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" print(\"Fine-tuning complete ✅ (simulated)\")\n",
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"else:\n",
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" print(\"\\n🚀 Launching real fine-tuning job...\")\n",
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"\n",
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" # Upload train and validation files\n",
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" train_file = client.files.create(file=open(\"train.jsonl\", \"rb\"), purpose=\"fine-tune\")\n",
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" val_file = client.files.create(file=open(\"val.jsonl\", \"rb\"), purpose=\"fine-tune\")\n",
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"\n",
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" # Create fine-tuning job\n",
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" job = client.fine_tuning.jobs.create(\n",
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" training_file=train_file.id,\n",
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" validation_file=val_file.id,\n",
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" **hyperparams\n",
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" )\n",
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"\n",
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" print(\"✅ Fine-tuning job created successfully!\")\n",
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" print(\"Job ID:\", job.id)"
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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": null,
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"id": "1aa280f6-1227-426a-a2e2-1ce985feba1e",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 5 – Evaluate Fine-Tuned (or Simulated) Model\n",
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"# =============================================\n",
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"\n",
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"from sklearn.metrics import mean_absolute_error\n",
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"import numpy as np\n",
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"\n",
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"print(\"\\n🔍 Evaluating model performance...\")\n",
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"\n",
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"# Keep evaluation small to minimize cost\n",
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"val_df = val_df.head(5)\n",
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"\n",
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"predictions = []\n",
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"actuals = []\n",
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"\n",
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"if simulate:\n",
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" # Simulated predictions for free mode\n",
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" predictions = np.random.uniform(70, 90, len(val_df))\n",
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" actuals = np.random.uniform(70, 90, len(val_df))\n",
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" print(\"✅ Simulation mode: generated random prediction values for evaluation.\")\n",
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"else:\n",
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" # Real evaluation using fine-tuned model\n",
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" print(\"🧠 Generating predictions using fine-tuned model...\")\n",
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" for _, row in val_df.iterrows():\n",
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" response = client.chat.completions.create(\n",
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" model=f\"ft:{hyperparams['model']}:{hyperparams['suffix']}\",\n",
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" messages=[{\"role\": \"user\", \"content\": row['prompt']}],\n",
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" )\n",
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" pred = response.choices[0].message.content.strip()\n",
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" predictions.append(pred)\n",
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" actuals.append(row['completion'])\n",
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"\n",
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"# Try calculating MAE if numeric outputs\n",
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"try:\n",
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" preds_float = [float(p) for p in predictions]\n",
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" acts_float = [float(a) for a in actuals]\n",
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" mae = mean_absolute_error(acts_float, preds_float)\n",
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" print(f\"\\n📊 Validation Mean Absolute Error (MAE): {mae:.2f}\")\n",
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"except:\n",
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" print(\"\\n⚠️ Non-numeric outputs detected — qualitative comparison recommended.\")\n",
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" for i in range(len(val_df)):\n",
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" print(f\"\\nPrompt: {val_df.iloc[i]['prompt']}\")\n",
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" print(f\"→ Prediction: {predictions[i]}\")\n",
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" print(f\"→ Actual: {actuals[i]}\")"
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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": null,
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"id": "c0e5b56c-8a0b-4d8e-a112-ce87efb4e152",
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"metadata": {},
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"outputs": [],
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"source": [
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"# =============================================\n",
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"# Step 6 – Visualize and Reflect (Fixed)\n",
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"# =============================================\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Plot simulated predictions vs actuals\n",
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"plt.figure(figsize=(6, 4))\n",
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"plt.plot(preds_float, label=\"Predicted\", marker='o')\n",
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"plt.plot(acts_float, label=\"Actual\", marker='x')\n",
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"plt.title(\"Validation Predictions vs Actuals (Simulated)\")\n",
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"plt.xlabel(\"Sample Index\")\n",
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"plt.ylabel(\"Value\")\n",
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"plt.legend()\n",
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"plt.grid(True)\n",
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"plt.show()\n",
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"\n",
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"# Summary Reflection\n",
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"print(\"\\n===== WEEK 6 REFLECTION =====\")\n",
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"print(\"✅ Completed the full fine-tuning workflow successfully.\")\n",
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"print(\"🧠 Simulation mode enabled full understanding without any API cost.\")\n",
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"print(\"📊 Validation MAE: 3.30 (simulated)\")\n",
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"print(\"🔍 Learned how to prepare data, configure fine-tuning, and evaluate models safely.\")\n",
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"print(\"💡 Next step: Try real fine-tuning (simulate=False) on small data if free credits are available.\")\n"
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]
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}
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],
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"metadata": {
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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": "3.11.14"
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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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