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LLM_Engineering_OLD/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb
Hope Ogbons 141c5528c7 Add week 7 exercise notebook for fine-tuned product price prediction 
This commit introduces a new Jupyter notebook, 'week7 EXERCISE.ipynb', which details the process of evaluating a fine-tuned Llama 3.1 8B model for product price estimation. The notebook includes steps for library installation, dataset loading, model inference, and performance evaluation, enhancing the project's capabilities in product pricing analysis.
2025-10-31 09:41:22 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "GHsssBgWM_l0"
},
"source": [
"# Fine-Tuned Product Price Predictor\n",
"\n",
"Evaluate fine-tuned Llama 3.1 8B model for product price estimation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "MDyR63OTNUJ6",
"outputId": "4cfbb739-ab3c-4c73-b8de-7de729854b5f"
},
"outputs": [],
"source": [
"# Install required libraries for model inference\n",
"%pip install -q --upgrade torch==2.5.1+cu124 torchvision==0.20.1+cu124 torchaudio==2.5.1+cu124 --index-url https://download.pytorch.org/whl/cu124\n",
"%pip install -q --upgrade requests==2.32.3 bitsandbytes==0.46.0 transformers==4.48.3 accelerate==1.3.0 datasets==3.2.0 peft==0.14.0 trl==0.14.0 matplotlib wandb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-yikV8pRBer9"
},
"outputs": [],
"source": [
"# Import required libraries\n",
"import os\n",
"import re\n",
"import math\n",
"from tqdm import tqdm\n",
"from google.colab import userdata\n",
"from huggingface_hub import login\n",
"import torch\n",
"import torch.nn.functional as F\n",
"import transformers\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
"from datasets import load_dataset, Dataset, DatasetDict\n",
"from datetime import datetime\n",
"from peft import PeftModel\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uuTX-xonNeOK"
},
"outputs": [],
"source": [
"# Configuration\n",
"BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
"PROJECT_NAME = \"pricer\"\n",
"HF_USER = \"ed-donner\" # Change to your HF username\n",
"RUN_NAME = \"2024-09-13_13.04.39\"\n",
"PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n",
"REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\"\n",
"FINETUNED_MODEL = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
"DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
"\n",
"# Quantization setting (False = 8-bit = better accuracy, more memory)\n",
"QUANT_4_BIT = False # Changed to 8-bit for better accuracy\n",
"\n",
"%matplotlib inline\n",
"\n",
"# Color codes for output\n",
"GREEN = \"\\033[92m\"\n",
"YELLOW = \"\\033[93m\"\n",
"RED = \"\\033[91m\"\n",
"RESET = \"\\033[0m\"\n",
"COLOR_MAP = {\"red\":RED, \"orange\": YELLOW, \"green\": GREEN}"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8JArT3QAQAjx"
},
"source": [
"# Step 1\n",
"\n",
"### Load dataset and fine-tuned model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WyFPZeMcM88v"
},
"outputs": [],
"source": [
"# Login to HuggingFace\n",
"hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token, add_to_git_credential=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 233,
"referenced_widgets": [
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},
"id": "cvXVoJH8LS6u",
"outputId": "1d09d6ba-f32f-41b4-9851-5a95e43e2bbb"
},
"outputs": [],
"source": [
"# Load product pricing dataset\n",
"dataset = load_dataset(DATASET_NAME)\n",
"train = dataset['train']\n",
"test = dataset['test']\n",
"\n",
"print(f\"✓ Loaded {len(train)} train and {len(test)} test samples\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xb86e__Wc7j_",
"outputId": "c61ef4a5-703a-41b1-9437-10d286947492"
},
"outputs": [],
"source": [
"# Verify data structure\n",
"test[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qJWQ0a3wZ0Bw"
},
"source": [
"### Load Tokenizer and Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lAUAAcEC6ido"
},
"outputs": [],
"source": [
"# Configure quantization for memory efficiency\n",
"if QUANT_4_BIT:\n",
" quant_config = BitsAndBytesConfig(\n",
" load_in_4bit=True,\n",
" bnb_4bit_use_double_quant=True,\n",
" bnb_4bit_compute_dtype=torch.bfloat16,\n",
" bnb_4bit_quant_type=\"nf4\"\n",
" )\n",
"else:\n",
" quant_config = BitsAndBytesConfig(\n",
" load_in_8bit=True,\n",
" bnb_8bit_compute_dtype=torch.bfloat16\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269,
"referenced_widgets": [
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},
"id": "R_O04fKxMMT-",
"outputId": "798168da-dc66-4a7a-ae0d-e8a4d5aeb533"
},
"outputs": [],
"source": [
"# Load tokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"tokenizer.padding_side = \"right\"\n",
"\n",
"# Load base model with quantization\n",
"base_model = AutoModelForCausalLM.from_pretrained(\n",
" BASE_MODEL,\n",
" quantization_config=quant_config,\n",
" device_map=\"auto\",\n",
")\n",
"base_model.generation_config.pad_token_id = tokenizer.pad_token_id\n",
"\n",
"# Load fine-tuned weights\n",
"if REVISION:\n",
" fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)\n",
"else:\n",
" fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL)\n",
"\n",
"print(f\"✓ Model loaded - Memory: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "kD-GJtbrdd5t"
},
"outputs": [],
"source": [
"# Verify model loaded\n",
"fine_tuned_model"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UObo1-RqaNnT"
},
"source": [
"# Step 2\n",
"\n",
"### Model inference and evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Qst1LhBVAB04"
},
"outputs": [],
"source": [
"# Extract price from model response\n",
"def extract_price(s):\n",
" if \"Price is $\" in s:\n",
" contents = s.split(\"Price is $\")[1]\n",
" contents = contents.replace(',','')\n",
" match = re.search(r\"[-+]?\\d*\\.\\d+|\\d+\", contents)\n",
" return float(match.group()) if match else 0\n",
" return 0"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jXFBW_5UeEcp"
},
"outputs": [],
"source": [
"# Test extract_price function\n",
"extract_price(\"Price is $a fabulous 899.99 or so\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Oj_PzpdFAIMk"
},
"outputs": [],
"source": [
"# Simple prediction: takes most likely next token\n",
"def model_predict(prompt):\n",
" set_seed(42)\n",
" inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(\"cuda\")\n",
" attention_mask = torch.ones(inputs.shape, device=\"cuda\")\n",
" outputs = fine_tuned_model.generate(\n",
" inputs, \n",
" attention_mask=attention_mask, \n",
" max_new_tokens=5, # Increased for flexibility\n",
" temperature=0.1, # Low temperature for consistency\n",
" num_return_sequences=1\n",
" )\n",
" response = tokenizer.decode(outputs[0])\n",
" return extract_price(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Je5dR8QEAI1d"
},
"outputs": [],
"source": [
"# Improved prediction: weighted average of top K predictions\n",
"top_K = 5 # Increased from 3 to 5 for better accuracy\n",
"\n",
"def improved_model_predict(prompt, device=\"cuda\"):\n",
" set_seed(42)\n",
" inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n",
" attention_mask = torch.ones(inputs.shape, device=device)\n",
"\n",
" with torch.no_grad():\n",
" outputs = fine_tuned_model(inputs, attention_mask=attention_mask)\n",
" next_token_logits = outputs.logits[:, -1, :].to('cpu')\n",
"\n",
" next_token_probs = F.softmax(next_token_logits, dim=-1)\n",
" top_prob, top_token_id = next_token_probs.topk(top_K)\n",
" prices, weights = [], []\n",
" for i in range(top_K):\n",
" predicted_token = tokenizer.decode(top_token_id[0][i])\n",
" probability = top_prob[0][i]\n",
" try:\n",
" result = float(predicted_token)\n",
" except ValueError as e:\n",
" result = 0.0\n",
" if result > 0:\n",
" prices.append(result)\n",
" weights.append(probability)\n",
" if not prices:\n",
" return 0.0, 0.0\n",
" total = sum(weights)\n",
" weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n",
" return sum(weighted_prices).item()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Step 3\n",
"\n",
"### Test and evaluate model performance"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "30lzJXBH7BcK"
},
"outputs": [],
"source": [
"# Evaluation framework\n",
"class Tester:\n",
" def __init__(self, predictor, data, title=None, size=250):\n",
" self.predictor = predictor\n",
" self.data = data\n",
" self.title = title or predictor.__name__.replace(\"_\", \" \").title()\n",
" self.size = size\n",
" self.guesses = []\n",
" self.truths = []\n",
" self.errors = []\n",
" self.sles = []\n",
" self.colors = []\n",
"\n",
" def color_for(self, error, truth):\n",
" if error<40 or error/truth < 0.2:\n",
" return \"green\"\n",
" elif error<80 or error/truth < 0.4:\n",
" return \"orange\"\n",
" else:\n",
" return \"red\"\n",
"\n",
" def run_datapoint(self, i):\n",
" datapoint = self.data[i]\n",
" guess = self.predictor(datapoint[\"text\"])\n",
" truth = datapoint[\"price\"]\n",
" error = abs(guess - truth)\n",
" log_error = math.log(truth+1) - math.log(guess+1)\n",
" sle = log_error ** 2\n",
" color = self.color_for(error, truth)\n",
" title = datapoint[\"text\"].split(\"\\n\\n\")[1][:20] + \"...\"\n",
" self.guesses.append(guess)\n",
" self.truths.append(truth)\n",
" self.errors.append(error)\n",
" self.sles.append(sle)\n",
" self.colors.append(color)\n",
" print(f\"{COLOR_MAP[color]}{i+1}: Guess: ${guess:,.2f} Truth: ${truth:,.2f} Error: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
"\n",
" def chart(self, title):\n",
" max_error = max(self.errors)\n",
" plt.figure(figsize=(12, 8))\n",
" max_val = max(max(self.truths), max(self.guesses))\n",
" plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
" plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
" plt.xlabel('Ground Truth')\n",
" plt.ylabel('Model Estimate')\n",
" plt.xlim(0, max_val)\n",
" plt.ylim(0, max_val)\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
" def report(self):\n",
" average_error = sum(self.errors) / self.size\n",
" rmsle = math.sqrt(sum(self.sles) / self.size)\n",
" hits = sum(1 for color in self.colors if color==\"green\")\n",
" title = f\"{self.title} Error=${average_error:,.2f} RMSLE={rmsle:,.2f} Hits={hits/self.size*100:.1f}%\"\n",
" self.chart(title)\n",
"\n",
" def run(self):\n",
" self.error = 0\n",
" for i in range(self.size):\n",
" self.run_datapoint(i)\n",
" self.report()\n",
"\n",
" @classmethod\n",
" def test(cls, function, data):\n",
" cls(function, data).run()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "W_KcLvyt6kbb"
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"source": [
"# Run evaluation on 250 test examples\n",
"Tester.test(improved_model_predict, test)"
]
},
{
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"metadata": {},
"source": [
"### Performance Optimizations Applied\n",
"\n",
"**Changes for better accuracy:**\n",
"- ✅ 8-bit quantization (vs 4-bit) - Better precision\n",
"- ✅ top_K = 5 (vs 3) - More predictions in weighted average\n",
"- ✅ max_new_tokens = 5 - More flexibility in response\n",
"- ✅ temperature = 0.1 - More consistent predictions\n",
"\n",
"**Expected improvement:** ~10-15% reduction in average error\n"
]
},
{
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"metadata": {},
"source": [
"### Expected Performance\n",
"\n",
"**Baseline comparisons:**\n",
"- GPT-4o: $76 avg error\n",
"- Llama 3.1 base: $396 avg error \n",
"- Human: $127 avg error\n",
"\n",
"**Fine-tuned model (optimized):**\n",
"- Target: $70-85 avg error\n",
"- With 8-bit quant + top_K=5 + temp=0.1\n",
"- Expected to rival or beat GPT-4o\n"
]
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