Merge pull request #884 from smqd19/week-6-submission
Gen AI Bootcamp - Week 6 Fine Tuning Frontier Model
This commit is contained in:
Ed Donner
GitHub
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b44aa468",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pickle\n",
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"import json\n",
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"import time\n",
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"import random\n",
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"from openai import OpenAI\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from datetime import datetime\n",
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"from items import Item \n",
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"from testing import Tester "
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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": "a8348f3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"client = OpenAI(api_key=os.getenv(\"OPENAI_API_KEY\"))"
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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": 3,
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"id": "f4cf994b",
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"metadata": {},
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"outputs": [],
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"source": [
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"BASE_MODEL = \"gpt-4o-mini-2024-07-18\"\n",
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"EPOCHS = 5\n",
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"BATCH_SIZE = 8\n",
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"LR_MULT = 0.3"
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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": "d2f0ac12",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(\"train.pkl\", \"rb\") as f:\n",
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" train_subset = pickle.load(f)\n",
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"\n",
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"with open(\"test.pkl\", \"rb\") as f:\n",
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" val_subset = pickle.load(f) \n",
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"\n",
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"print(f\"Loaded {len(train_subset)} training and {len(val_subset)} validation items.\")"
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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": "3aad35ac",
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"metadata": {},
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"outputs": [],
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"source": [
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"random.shuffle(train_subset)\n",
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"random.shuffle(val_subset)\n",
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"\n",
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"TRAIN_LIMIT = 5000 \n",
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"VAL_LIMIT = 1000 \n",
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"\n",
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"train_subset = train_subset[:TRAIN_LIMIT]\n",
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"val_subset = val_subset[:VAL_LIMIT]\n",
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"\n",
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"print(f\"Using {len(train_subset)} training and {len(val_subset)} validation samples.\")"
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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": 6,
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"id": "2c85d07e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def build_prompt(item):\n",
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" return f\"\"\"\n",
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"### CONTEXT\n",
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"You are a price estimation assistant for e-commerce listings.\n",
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"Each product is described by its title, category, key features, and details.\n",
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"\n",
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"### TASK\n",
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"Estimate the most likely retail price in USD.\n",
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"Think step-by-step about product type, quality, and included components \n",
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"before stating the final answer as \"Predicted Price: $<amount>\".\n",
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"\n",
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"### EXAMPLES\n",
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"- Wireless earbuds with active noise cancellation -> Predicted Price: $89\n",
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"- Stainless steel kitchen knife set (6-piece) -> Predicted Price: $45\n",
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"- Laptop stand aluminum adjustable -> Predicted Price: $32\n",
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"\n",
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"### PRODUCT TITLE\n",
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"{item.title}\n",
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"\n",
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"### CATEGORY\n",
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"{item.category}\n",
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"\n",
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"### DETAILS\n",
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"{item.details}\n",
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"\n",
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"### YOUR REASONING\n",
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"(Think about product quality, features, and typical market range.)\n",
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"\n",
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"### FINAL ANSWER\n",
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"Predicted Price: $\n",
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"\"\"\"\n",
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"\n",
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"def build_completion(item):\n",
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" return f\"Predicted Price: ${round(item.price)}.00\""
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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": "83eca819",
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"metadata": {},
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"outputs": [],
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"source": [
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"def write_jsonl(data, filename):\n",
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" with open(filename, \"w\", encoding=\"utf-8\") as f:\n",
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" for item in data:\n",
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" if getattr(item, \"include\", True):\n",
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" prompt = build_prompt(item)\n",
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" completion = build_completion(item)\n",
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" json_obj = {\n",
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" \"messages\": [\n",
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" {\"role\": \"user\", \"content\": prompt},\n",
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" {\"role\": \"assistant\", \"content\": completion}\n",
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" ]\n",
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" }\n",
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" f.write(json.dumps(json_obj) + \"\\n\")\n",
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" print(f\"Wrote {len(data)} samples to {filename}\")\n",
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"\n",
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"TRAIN_JSONL = \"train_prepared.jsonl\"\n",
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"VAL_JSONL = \"val_prepared.jsonl\"\n",
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"\n",
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"write_jsonl(train_subset, TRAIN_JSONL)\n",
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"write_jsonl(val_subset, VAL_JSONL)"
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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": 8,
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"id": "d25bfc85",
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"metadata": {},
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"outputs": [],
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"source": [
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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\")"
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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": "ef4e092a",
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"metadata": {},
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"outputs": [],
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"source": [
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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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" model=BASE_MODEL,\n",
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" hyperparameters={\n",
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" \"n_epochs\": EPOCHS,\n",
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" \"batch_size\": BATCH_SIZE,\n",
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" \"learning_rate_multiplier\": LR_MULT\n",
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" }\n",
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")\n",
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"\n",
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"print(\"Job started:\", 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": "c8d58b5e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_finetune_events(job_id, poll_interval=30):\n",
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" print(f\"Tracking fine-tuning job: {job_id}\\n\")\n",
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" seen = set()\n",
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" loss_data = []\n",
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" \n",
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" while True:\n",
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" job = client.fine_tuning.jobs.retrieve(job_id)\n",
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" events = client.fine_tuning.jobs.list_events(job_id)\n",
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" \n",
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" for e in events.data[::-1]:\n",
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" if e.id not in seen:\n",
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" seen.add(e.id)\n",
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" ts = datetime.fromtimestamp(e.created_at)\n",
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" msg = e.message\n",
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" print(f\"[{ts:%Y-%m-%d %H:%M:%S}] {msg}\")\n",
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" \n",
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" if \"training_loss\" in msg:\n",
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" try:\n",
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" step = int(msg.split(\"Step \")[1].split(\"/\")[0])\n",
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" train_loss = float(msg.split(\"training_loss: \")[1].split(\",\")[0])\n",
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" val_loss = None\n",
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" if \"val_loss\" in msg:\n",
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" val_loss = float(msg.split(\"val_loss: \")[1].split(\",\")[0])\n",
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" loss_data.append((step, train_loss, val_loss))\n",
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" except Exception:\n",
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" pass\n",
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" \n",
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" if job.status == \"succeeded\":\n",
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" print(\"\\nFine-tuning complete!\")\n",
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" print(\"Fine-tuned model ID:\", job.fine_tuned_model)\n",
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" \n",
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" if loss_data:\n",
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" steps = [d[0] for d in loss_data]\n",
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" train_losses = [d[1] for d in loss_data]\n",
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" val_losses = [d[2] for d in loss_data if d[2] is not None]\n",
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"\n",
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" plt.figure(figsize=(8, 5))\n",
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" plt.plot(steps, train_losses, marker=\"o\", color=\"teal\", label=\"Training Loss\")\n",
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" if val_losses:\n",
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" plt.plot(steps[:len(val_losses)], val_losses, marker=\"o\", color=\"orange\", label=\"Validation Loss\")\n",
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" plt.xlabel(\"Step\")\n",
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" plt.ylabel(\"Loss\")\n",
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" plt.title(f\"Fine-Tuning Progress — {job_id}\")\n",
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" plt.legend()\n",
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" plt.grid(alpha=0.3)\n",
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" plt.show()\n",
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" else:\n",
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" print(\"No loss data found. Fine-tuning may have completed too quickly to log metrics.\")\n",
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"\n",
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" return job.fine_tuned_model\n",
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"\n",
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" elif job.status in [\"failed\", \"cancelled\"]:\n",
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" print(f\"\\nFine-tuning {job.status}.\")\n",
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" if job.error:\n",
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" print(\"Error:\", job.error)\n",
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" return None\n",
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"\n",
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" time.sleep(poll_interval)\n",
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"\n",
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"MODEL_ID = stream_finetune_events(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": "f3ed9c0e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def test_model(model_id, test_items, max_samples=100):\n",
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" y_true, y_pred = [], []\n",
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" for i, item in enumerate(test_items[:max_samples]):\n",
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" prompt = build_prompt(item)\n",
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" response = client.chat.completions.create(\n",
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" model=model_id,\n",
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" messages=[{\"role\": \"user\", \"content\": prompt}],\n",
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" temperature=0\n",
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" )\n",
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" output = response.choices[0].message.content\n",
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" try:\n",
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" pred_price = float(output.split(\"$\")[1].split()[0])\n",
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" except:\n",
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" continue\n",
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" y_true.append(item.price)\n",
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" y_pred.append(pred_price)\n",
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" print(f\"{i+1}. {item.title[:50]} | Actual: ${item.price} | Pred: ${pred_price}\")\n",
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" return y_true, y_pred\n",
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"\n",
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"y_true, y_pred = test_model(MODEL_ID, val_subset)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "347c22cb",
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"metadata": {},
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"outputs": [],
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"source": [
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"errors = np.abs(np.array(y_true) - np.array(y_pred))\n",
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"colors = [\"green\" if e < 10 else \"orange\" if e < 25 else \"red\" for e in errors]\n",
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"\n",
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"plt.figure(figsize=(10,6))\n",
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"plt.scatter(range(len(y_true)), y_true, color='blue', label='Actual', alpha=0.6)\n",
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"plt.scatter(range(len(y_pred)), y_pred, color=colors, label='Predicted', alpha=0.8)\n",
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"plt.title(\"Fine-tuned Price Prediction Performance (Color-Coded by Error)\")\n",
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"plt.xlabel(\"Sample Index\")\n",
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"plt.ylabel(\"Price ($)\")\n",
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"plt.legend()\n",
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"plt.show()\n",
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"\n",
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"avg_error = np.mean(errors)\n",
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"print(f\"\\nAverage error: ${avg_error:.2f}\")"
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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": "llm-engineering",
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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": "3.12.12"
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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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