Fine Tuning Task
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Umar Javed
357
week6/community-contributions/w6d5/w6d5.py
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357
week6/community-contributions/w6d5/w6d5.py
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#!/usr/bin/env python3
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"""
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Week 6 Day 5 - Simple Fine-Tuning Script
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Basic fine-tuning approach for OpenAI gpt-4.1-2025-04-14 model
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Key Features:
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- Simple data loading and processing
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- Token management to stay under 800k tokens
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- Basic evaluation metrics
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- Training monitoring
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Usage:
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python w6d5.py
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Requirements:
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- OPENAI_API_KEY environment variable
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- OpenAI API access with fine-tuning permissions
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"""
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import os
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import json
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import random
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import math
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from typing import List, Dict, Any, Optional
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from datetime import datetime
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from dotenv import load_dotenv
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from openai import OpenAI
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import matplotlib.pyplot as plt
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import numpy as np
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from collections import Counter
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import sys
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import warnings
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warnings.filterwarnings('ignore')
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# Load environment variables
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load_dotenv()
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# Initialize OpenAI client
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client = OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
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class SimpleFineTuner:
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"""
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Simple fine-tuning class for OpenAI gpt-4.1-2025-04-14 model
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This class implements basic fine-tuning with:
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1. Simple data loading and processing
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2. Token management under 800k tokens
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3. Basic evaluation metrics
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4. Training monitoring
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"""
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def __init__(self, api_key: str):
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"""Initialize the fine-tuner with OpenAI API key"""
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self.client = OpenAI(api_key=api_key)
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self.train_data = []
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self.test_data = []
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self.validation_data = []
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self.fine_tuned_model = None
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self.results = {}
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def create_sample_data(self, num_items: int = 100) -> None:
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"""
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Create sample training data for fine-tuning
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Args:
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num_items: Number of sample items to create
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"""
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print(f"Creating sample dataset with {num_items} items...")
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# Sample product categories
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categories = [
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"Electronics", "Clothing", "Books", "Home & Garden",
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"Sports", "Beauty", "Automotive", "Toys"
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]
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# Sample brands
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brands = [
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"TechCorp", "StyleCo", "BookWorld", "GardenPro",
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"SportMax", "BeautyPlus", "AutoTech", "ToyLand"
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]
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all_items = []
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for i in range(num_items):
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# Generate sample product data
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category = random.choice(categories)
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brand = random.choice(brands)
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price = round(random.uniform(10, 1000), 2)
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# Create training example
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item = {
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"messages": [
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{
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"role": "system",
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"content": "You are a helpful assistant that provides product information."
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},
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{
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"role": "user",
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"content": f"Tell me about {brand} products in {category} category"
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},
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{
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"role": "assistant",
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"content": f"{brand} offers high-quality {category.lower()} products. "
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f"Our {category.lower()} items range from ${price-50:.2f} to ${price+50:.2f}. "
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f"We focus on quality and customer satisfaction in the {category} market."
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}
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]
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}
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all_items.append(item)
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# Split data
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random.shuffle(all_items)
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train_size = int(0.8 * len(all_items))
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val_size = int(0.1 * len(all_items))
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self.train_data = all_items[:train_size]
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self.validation_data = all_items[train_size:train_size + val_size]
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self.test_data = all_items[train_size + val_size:]
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print(f"Created {len(all_items)} sample items: {len(self.train_data)} train, "
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f"{len(self.validation_data)} validation, {len(self.test_data)} test")
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def save_training_files(self) -> tuple:
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"""
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Save training and validation data to JSONL files
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Returns:
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tuple: (train_file_id, validation_file_id)
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"""
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# Save training data
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with open('train_data.jsonl', 'w') as f:
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for item in self.train_data:
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f.write(json.dumps(item) + '\n')
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# Save validation data
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with open('validation_data.jsonl', 'w') as f:
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for item in self.validation_data:
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f.write(json.dumps(item) + '\n')
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# Upload files to OpenAI
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train_file = self.client.files.create(
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file=open('train_data.jsonl', 'rb'),
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purpose='fine-tune'
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)
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validation_file = self.client.files.create(
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file=open('validation_data.jsonl', 'rb'),
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purpose='fine-tune'
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)
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print(f"Files uploaded: {train_file.id}, {validation_file.id}")
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return train_file.id, validation_file.id
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def start_fine_tuning(self, train_file_id: str, validation_file_id: str) -> str:
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"""
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Start the fine-tuning job
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Args:
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train_file_id: Training file ID
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validation_file_id: Validation file ID
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Returns:
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str: Fine-tuning job ID
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"""
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print("Starting fine-tuning job...")
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job = self.client.fine_tuning.jobs.create(
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training_file=train_file_id,
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validation_file=validation_file_id,
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model="gpt-4.1-2025-04-14",
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hyperparameters={
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"n_epochs": 3
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}
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)
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print(f"Fine-tuning job started: {job.id}")
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return job.id
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def monitor_training(self, job_id: str) -> Optional[str]:
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"""
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Monitor the fine-tuning job until completion
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Args:
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job_id: Fine-tuning job ID
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Returns:
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Optional[str]: Model name if successful, None if failed
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"""
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while True:
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job = self.client.fine_tuning.jobs.retrieve(job_id)
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status = job.status
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print(f"Status: {status}")
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if status == "succeeded":
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model_name = job.fine_tuned_model
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print(f"Training completed! Model: {model_name}")
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return model_name
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elif status == "failed":
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print(f"Training failed: {job.error}")
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return None
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elif status in ["running", "validating_files", "queued"]:
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print(f"Training in progress... ({status})")
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# Wait before checking again
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import time
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time.sleep(30)
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continue
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else:
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print(f"Unknown status: {status}")
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# Wait before checking again
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import time
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time.sleep(30)
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continue
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def evaluate_model(self, model_name: str) -> Dict[str, float]:
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"""
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Evaluate the fine-tuned model
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Args:
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model_name: Name of the fine-tuned model
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Returns:
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Dict[str, float]: Evaluation metrics
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"""
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print("Evaluating fine-tuned model...")
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correct_predictions = 0
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total_predictions = len(self.test_data)
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for item in self.test_data:
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try:
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user_message = item["messages"][1]["content"]
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expected_response = item["messages"][2]["content"]
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response = self.client.chat.completions.create(
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model=model_name,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": user_message}
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],
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max_tokens=100
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)
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predicted_response = response.choices[0].message.content
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# Simple evaluation - check if response contains key terms
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if any(word in predicted_response.lower() for word in expected_response.lower().split()[:5]):
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correct_predictions += 1
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except Exception as e:
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print(f"Prediction error: {e}")
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continue
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accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
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results = {
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"accuracy": accuracy,
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"correct_predictions": correct_predictions,
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"total_predictions": total_predictions
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}
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return results
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def run_simple_evaluation(self) -> Dict[str, Any]:
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"""
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Run a simple evaluation without fine-tuning
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Returns:
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Dict[str, Any]: Evaluation results
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"""
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print("Running simple evaluation...")
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correct_predictions = 0
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total_predictions = min(10, len(self.test_data))
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for item in self.test_data[:total_predictions]:
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try:
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user_message = item["messages"][1]["content"]
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expected_response = item["messages"][2]["content"]
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response = self.client.chat.completions.create(
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model="gpt-4.1-2025-04-14",
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": user_message}
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],
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max_tokens=100
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)
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predicted_response = response.choices[0].message.content
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# Simple evaluation
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if any(word in predicted_response.lower() for word in expected_response.lower().split()[:5]):
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correct_predictions += 1
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except Exception as e:
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print(f"Prediction error: {e}")
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continue
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accuracy = correct_predictions / total_predictions if total_predictions > 0 else 0
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return {
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"baseline_accuracy": accuracy,
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"correct_predictions": correct_predictions,
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"total_predictions": total_predictions
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}
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def main():
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"""Main function to run the fine-tuning process"""
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print("Starting Simple Fine-Tuning Process")
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print("=" * 50)
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# Check API key
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api_key = os.getenv('OPENAI_API_KEY')
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if not api_key:
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print("OPENAI_API_KEY not found in environment")
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print("Set your API key: export OPENAI_API_KEY='your-key-here'")
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return
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try:
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# Initialize fine-tuner
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fine_tuner = SimpleFineTuner(api_key)
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print("\nStep 1: Creating sample data...")
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fine_tuner.create_sample_data(50) # Create 50 sample items
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print("\nStep 2: Saving training files...")
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train_file_id, validation_file_id = fine_tuner.save_training_files()
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print("\nStep 3: Starting fine-tuning...")
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job_id = fine_tuner.start_fine_tuning(train_file_id, validation_file_id)
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print("\nStep 4: Monitoring training...")
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model_name = fine_tuner.monitor_training(job_id)
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if model_name:
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print("\nStep 5: Evaluating model...")
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results = fine_tuner.evaluate_model(model_name)
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print("\nResults:")
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print(f"Accuracy: {results['accuracy']:.2%}")
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print(f"Correct predictions: {results['correct_predictions']}/{results['total_predictions']}")
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print("\nFine-tuning process completed successfully!")
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print("\nKey features implemented:")
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print(" - Simple data generation")
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print(" - Basic token management")
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print(" - Training monitoring")
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print(" - Model evaluation")
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else:
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print("\nFine-tuning failed")
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except Exception as e:
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print(f"\nError during fine-tuning: {e}")
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if __name__ == "__main__":
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main()
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