Add Week 6 finetuning solution: Clean implementation with pickle data

- Added Week6_Product_Pricer_Clean.ipynb with complete fine-tuning pipeline
- Added enhanced_items.py and testing.py modules for Windows compatibility
- Added train.pkl, test.pkl, validation.pkl data files (250 items total)
- Implements OpenAI fine-tuning with enhanced prompts
- Includes comprehensive evaluation and comparison framework
- Ready for submission and grading

week6/community-contributions/finetuning-joshua/enhanced_items.py

@@ -0,0 +1,149 @@
+from typing import Optional
+from transformers import AutoTokenizer
+import re
+import os
+
+# Try multiple model sources in order of preference
+BASE_MODEL_OPTIONS = [
+    "/root/.llama/checkpoints/Llama3.1-8B",  # Local llama-stack download
+    "microsoft/DialoGPT-medium",  # Accessible alternative
+    "gpt2"  # Fallback
+]
+
+BASE_MODEL = None
+
+MIN_TOKENS = 150  # Any less than this, and we don't have enough useful content
+MAX_TOKENS = 160  # Truncate after this many tokens. Then after adding in prompt text, we will get to around 180 tokens
+
+MIN_CHARS = 300
+CEILING_CHARS = MAX_TOKENS * 7
+
+class Item:
+    """
+    An Item is a cleaned, curated datapoint of a Product with a Price
+    Enhanced version with better error handling and alternative tokenizer
+    """
+    
+    # Initialize tokenizer with fallback options
+    tokenizer = None
+    for model_path in BASE_MODEL_OPTIONS:
+        try:
+            if model_path.startswith("/") and not os.path.exists(model_path):
+                continue  # Skip local paths that don't exist
+            tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+            BASE_MODEL = model_path
+            print(f"✅ Successfully loaded tokenizer from: {model_path}")
+            break
+        except Exception as e:
+            print(f"⚠️  Failed to load {model_path}: {e}")
+            continue
+    
+    if tokenizer is None:
+        print("❌ All tokenizer options failed. Using character-based fallback.")
+        # Create a dummy tokenizer for fallback
+        class DummyTokenizer:
+            def encode(self, text, add_special_tokens=False):
+                # Rough approximation: 1 token ≈ 4 characters
+                return list(range(len(text) // 4))
+            def decode(self, tokens):
+                return "dummy text"
+        tokenizer = DummyTokenizer()
+        BASE_MODEL = "fallback"
+    
+    PREFIX = "Price is $"
+    QUESTION = "How much does this cost to the nearest dollar?"
+    REMOVALS = [
+        '"Batteries Included?": "No"', 
+        '"Batteries Included?": "Yes"', 
+        '"Batteries Required?": "No"', 
+        '"Batteries Required?": "Yes"', 
+        "By Manufacturer", 
+        "Item", 
+        "Date First", 
+        "Package", 
+        ":", 
+        "Number of", 
+        "Best Sellers", 
+        "Number", 
+        "Product "
+    ]
+
+    title: str
+    price: float
+    category: str
+    token_count: int = 0
+    details: Optional[str]
+    prompt: Optional[str] = None
+    include = False
+
+    def __init__(self, data, price):
+        self.title = data['title']
+        self.price = price
+        self.parse(data)
+
+    def scrub_details(self):
+        """
+        Clean up the details string by removing common text that doesn't add value
+        """
+        details = self.details
+        for remove in self.REMOVALS:
+            details = details.replace(remove, "")
+        return details
+
+    def scrub(self, stuff):
+        """
+        Clean up the provided text by removing unnecessary characters and whitespace
+        Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers
+        """
+        stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip()
+        stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",")
+        words = stuff.split(' ')
+        select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)]
+        return " ".join(select)
+    
+    def parse(self, data):
+        """
+        Parse this datapoint and if it fits within the allowed Token range,
+        then set include to True
+        """
+        contents = '\n'.join(data['description'])
+        if contents:
+            contents += '\n'
+        features = '\n'.join(data['features'])
+        if features:
+            contents += features + '\n'
+        self.details = data['details']
+        if self.details:
+            contents += self.scrub_details() + '\n'
+        if len(contents) > MIN_CHARS:
+            contents = contents[:CEILING_CHARS]
+            text = f"{self.scrub(self.title)}\n{self.scrub(contents)}"
+            tokens = self.tokenizer.encode(text, add_special_tokens=False)
+            if len(tokens) > MIN_TOKENS:
+                tokens = tokens[:MAX_TOKENS]
+                text = self.tokenizer.decode(tokens)
+                self.make_prompt(text)
+                self.include = True
+
+    def make_prompt(self, text):
+        """
+        Set the prompt instance variable to be a prompt appropriate for training
+        """
+        self.prompt = f"{self.QUESTION}\n\n{text}\n\n"
+        self.prompt += f"{self.PREFIX}{str(round(self.price))}.00"
+        self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False))
+
+    def test_prompt(self):
+        """
+        Return a prompt suitable for testing, with the actual price removed
+        """
+        return self.prompt.split(self.PREFIX)[0] + self.PREFIX
+
+    def __repr__(self):
+        """
+        Return a String version of this Item
+        """
+        return f"<{self.title} = ${self.price}>"
+
+
+