LLM_Engineering_OLD/week7/community-contributions/09_part5_llama31_8b_quant.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4WDyBU0Vm0Zl"
   },
   "source": [
    "# 🔍 Predicting Item Prices from Descriptions (Part 5)\n",
    "---\n",
    "-  Data Curation & Preprocessing\n",
    "- Model Benchmarking – Traditional ML vs LLMs\n",
    "- E5 Embeddings & RAG\n",
    "- Fine-Tuning GPT-4o Mini\n",
    "- ➡️ Evaluating LLaMA 3.1 8B Quantized\n",
    "- Fine-Tuning LLaMA 3.1 with QLoRA\n",
    "- Evaluating Fine-Tuned LLaMA\n",
    "- Summary & Leaderboard\n",
    "\n",
    "---\n",
    "\n",
    "# 🦙 Part 5: Evaluating LLaMA 3.1 8B Quantized\n",
    "\n",
    "- 🧑‍💻 Skill Level: Advanced\n",
    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab\n",
    "- 🛠️ Requirements: 🔑 HF Token\n",
    "- Tasks:\n",
    "    - Quantize LLaMA 3.1 8B to 4-bit\n",
    "    - Define prediction function\n",
    "    - Evaluate with Tester\n",
    "\n",
    "We know LLaMA 3.1 won’t beat frontier models — but how far behind is it without any tuning?\n",
    "\n",
    "---\n",
    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MDyR63OTNUJ6",
    "outputId": "7e9e5b6b-d11c-45df-d774-2da5f6455d51"
   },
   "outputs": [],
   "source": [
    "# Install required packages in Google Colab\n",
    "%pip install -q datasets torch transformers bitsandbytes accelerate matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-yikV8pRBer9"
   },
   "outputs": [],
   "source": [
    "#  imports\n",
    "\n",
    "import re\n",
    "import math\n",
    "import torch\n",
    "from huggingface_hub import login\n",
    "from datasets import load_dataset\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
    "from google.colab import userdata\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "uuTX-xonNeOK"
   },
   "outputs": [],
   "source": [
    "# Google Colab User Data\n",
    "# Ensure you have set the following in your Google Colab environment:\n",
    "hf_token = userdata.get('HF_TOKEN')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "\n",
    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
    "HF_USER = \"lisekarimi\"\n",
    "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
    "\n",
    "login(hf_token, add_to_git_credential=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DTMo_1msQb9X"
   },
   "source": [
    "## 📥 Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #If you face NotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported run:\n",
    "# %pip install -U datasets"
   ]
  },
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    "id": "5PVXACKHQhh4",
    "outputId": "80dc4772-ea31-4752-8f97-573efaa43917"
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   "outputs": [],
   "source": [
    "dataset = load_dataset(DATASET_NAME)\n",
    "train = dataset['train']\n",
    "test = dataset['test']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pGJR24lbQlRS",
    "outputId": "a1bb5e66-1aa9-40b7-c361-562eafae5d8c"
   },
   "outputs": [],
   "source": [
    "test[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vrxH6h00P9qc"
   },
   "source": [
    "## 🦙 Load Tokenizer and Quantized LLaMA Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G4veOcy5dNmG"
   },
   "source": [
    "A quick reminder of how padding for batch processing works:\n",
    "\n",
    "`texts = [\"I love AI\", \"AI is\"]`\n",
    "\n",
    "If you tokenize them without padding, you get:\n",
    "```\n",
    "['I', 'love', 'AI'] → length = 3\n",
    "['AI', 'is'] → length = 2\n",
    "```\n",
    "❗ Models require equal-length inputs for batching.\n",
    "\n",
    "Set padding like this:\n",
    "```\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"right\"\n",
    "tokens = tokenizer(texts, padding=True, return_tensors=\"pt\")\n",
    "```\n",
    "\n",
    "Result:\n",
    "```\n",
    "['I', 'love', 'AI'] → [Token1, Token2, Token3]\n",
    "['AI', 'is', '<eos>'] → [Token1, Token2, PAD]\n",
    "```\n",
    "Now both sequences are the same length (3) → batch-ready.\n"
   ]
  },
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    },
    "id": "TAit9IzsQLcc",
    "outputId": "176a77ad-0245-4a3d-b9f3-e139de359da7"
   },
   "outputs": [],
   "source": [
    "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",
    "\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",
    "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",
    "print(f\"Memory footprint: {base_model.get_memory_footprint() / 1e9:.1f} GB\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🤖 Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1FfMJ2JbzEr3"
   },
   "outputs": [],
   "source": [
    "def extract_price(s):\n",
    "    if \"Price is $\" in s:\n",
    "      contents = s.split(\"Price is $\")[1]\n",
    "      contents = contents.replace(',','').replace('$','')\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": "CgN8eRttRAZx"
   },
   "outputs": [],
   "source": [
    "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 = base_model.generate(inputs, max_new_tokens=4, attention_mask=attention_mask, num_return_sequences=1)\n",
    "    response = tokenizer.decode(outputs[0])\n",
    "    return extract_price(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "hpdEk2-FW6aT",
    "outputId": "f8913c56-1a8f-4a13-9084-21acfdb64ceb"
   },
   "outputs": [],
   "source": [
    "model_predict(test[0]['text']), test[0]['price']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "auFzPUJKTLln"
   },
   "source": [
    "## 🧪 Run Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "jnoI1EWGTUau"
   },
   "outputs": [],
   "source": [
    "# Helper class for evaluating model predictions\n",
    "\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}\n",
    "\n",
    "class Tester:\n",
    "\n",
    "    def __init__(self, predictor, data, title=None, size=100):\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: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
    "\n",
    "    def chart(self, title):\n",
    "        # max_error = max(self.errors)\n",
    "        plt.figure(figsize=(12, 8))\n",
    "        max_val = max(max(self.truths), max(self.guesses))\n",
    "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
    "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
    "        plt.xlabel('Ground Truth')\n",
    "        plt.ylabel('Model Estimate')\n",
    "        plt.xlim(0, max_val)\n",
    "        plt.ylim(0, max_val)\n",
    "        plt.title(title)\n",
    "\n",
    "        # Add color legend\n",
    "        from matplotlib.lines import Line2D\n",
    "        legend_elements = [\n",
    "            Line2D([0], [0], marker='o', color='w', label='Accurate (green)', markerfacecolor='green', markersize=8),\n",
    "            Line2D([0], [0], marker='o', color='w', label='Medium error (orange)', markerfacecolor='orange', markersize=8),\n",
    "            Line2D([0], [0], marker='o', color='w', label='High error (red)', markerfacecolor='red', markersize=8)\n",
    "        ]\n",
    "        plt.legend(handles=legend_elements, loc='upper right')\n",
    "\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "    def report(self):\n",
    "        average_error = sum(self.errors) / self.size\n",
    "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
    "        hits = sum(1 for color in self.colors if color==\"green\")\n",
    "        title = f\"{self.title} Error=${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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 692
    },
    "id": "1wA5uVgpTWLC",
    "outputId": "5a597437-50c8-419c-c1da-af0166dabe0f"
   },
   "outputs": [],
   "source": [
    "Tester.test(model_predict, test)"
   ]
  },
  {
   "attachments": {
    "99d1317e-d872-46f7-bf2c-cc73c825993a.png": {
     "image/png": 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    "![image.png](attachment:99d1317e-d872-46f7-bf2c-cc73c825993a.png)"
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    "Alright — now that we know where things stand, it’s time to shake things up.\n",
    "\n",
    "Can QLoRA fine-tuning unlock the true power of LLaMA 3.1?\n",
    "\n",
    "👀 Let’s find out... in the [next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part6_ft_llama_qlora.ipynb)"
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