diff --git a/week7/community_contributions/lisekarimi/09_part5_llama31_8b_quant.ipynb b/week7/community_contributions/lisekarimi/09_part5_llama31_8b_quant.ipynb
new file mode 100644
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--- /dev/null
+++ b/week7/community_contributions/lisekarimi/09_part5_llama31_8b_quant.ipynb
@@ -0,0 +1,612 @@
+{
+ "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
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+    },
+    "id": "5PVXACKHQhh4",
+    "outputId": "80dc4772-ea31-4752-8f97-573efaa43917"
+   },
+   "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": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
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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": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lSfPbebIq2Ml"
+   },
+   "source": [
+    "![image.png](attachment:image.png)\n",
+    "\n",
+    "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)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/week7/community_contributions/lisekarimi/09_part6_ft_llama_qlora.ipynb b/week7/community_contributions/lisekarimi/09_part6_ft_llama_qlora.ipynb
new file mode 100644
index 0000000..af4b5e7
--- /dev/null
+++ b/week7/community_contributions/lisekarimi/09_part6_ft_llama_qlora.ipynb
@@ -0,0 +1,907 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 6)\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 6: Fine-Tuning LLaMA 3.1 with QLoRA\n",
+    "\n",
+    "- 🧑‍💻 Skill Level: Advanced\n",
+    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab (A100)\n",
+    "- 🛠️ Requirements: 🔑 HF Token, wandb API Key ([Weights & Biases](https://wandb.ai))\n",
+    "- Tasks:\n",
+    "    - Load and split dataset (Train/validation); set up [Weights & Biases](https://wandb.ai) logging\n",
+    "    - Load quantized LLaMA 3.1 8B and tokenizer\n",
+    "    - Prepare data with a collator for fine-tuning\n",
+    "    - Configure QLoRA (LoRAConfig), training settings (SFTConfig), and tune key hyperparameters\n",
+    "    - Fine-tune and push best model to Hugging Face Hub\n",
+    "\n",
+    "⚠️ I attempted to fine-tune the model on the full 400K dataset using an A100 on Google Colab, but it consistently crashed. So for now, I’m training on a 20K subset to understand the process, play with hyperparameters, track progress in Weights & Biases, and push the best checkpoint to the Hub.\n",
+    "\n",
+    "⏱️ Training on 20,000 examples took over 2 hours.\n",
+    "\n",
+    "The full model fine-tuned on the complete 400K dataset is available thanks to our instructor, [Ed](https://www.linkedin.com/in/eddonner) — much appreciated!  \n",
+    "We’ll dive into that model in the next notebook — **stay tuned** 😉\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "MDyR63OTNUJ6",
+    "outputId": "525372ce-f614-44f1-b894-80e289958197"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required packages in Google Colab\n",
+    "%pip install -q datasets transformers torch peft bitsandbytes trl accelerate wandb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-yikV8pRBer9"
+   },
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import os\n",
+    "import torch\n",
+    "import wandb\n",
+    "from google.colab import userdata\n",
+    "from datetime import datetime\n",
+    "from datasets import load_dataset\n",
+    "from huggingface_hub import login\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, EarlyStoppingCallback\n",
+    "from peft import LoraConfig\n",
+    "from trl import SFTTrainer, SFTConfig, DataCollatorForCompletionOnlyLM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "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')\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B48QsPsvUs_x"
+   },
+   "source": [
+    "## 🔀 Load Dataset from HF and Split into Train/Validation"
+   ],
+   "outputs": []
+  },
+  {
+   "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 (for Google Colab)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
+     "referenced_widgets": [
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+     ]
+    },
+    "id": "XEE1FrSIh-EF",
+    "outputId": "8cd19745-2f6f-41e0-96dd-5a2f72ac3a63"
+   },
+   "outputs": [],
+   "source": [
+    "HF_USER = \"lisekarimi\" # your HF name here!\n",
+    "\n",
+    "DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
+    "dataset = load_dataset(DATASET_NAME)\n",
+    "train = dataset['train']\n",
+    "test = dataset['test']\n",
+    "split_ratio = 0.1  # 10% for validation\n",
+    "\n",
+    "##############################################################################\n",
+    "# Optional: limit training dataset to TRAIN_SIZE for testing/debugging\n",
+    "# Comment the two lines below to use the full dataset\n",
+    "TRAIN_SIZE = 20000\n",
+    "train = train.select(range(TRAIN_SIZE))\n",
+    "##############################################################################\n",
+    "\n",
+    "total_size = len(train)\n",
+    "val_size = int(total_size * split_ratio)\n",
+    "\n",
+    "val_data = train.select(range(val_size))\n",
+    "train_data = train.select(range(val_size, total_size))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "lUPNqb2Bse21",
+    "outputId": "a3d09c8f-ce5a-46b0-e1b0-b4471a659f69"
+   },
+   "outputs": [],
+   "source": [
+    "print(f\"Train data size     : {len(train_data)}\")\n",
+    "print(f\"Validation data size: {len(val_data)}\")\n",
+    "print(f\"Test data size      : {len(test)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wixbM-VeVfsR"
+   },
+   "source": [
+    "## 🛠️ Hugging Face Configuration"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 35
+    },
+    "id": "OixVUG06VmZk",
+    "outputId": "3cb523e0-fd03-4a18-913b-c22fa90e3bdd"
+   },
+   "outputs": [],
+   "source": [
+    "PROJECT_NAME = \"llama3-pricer\"\n",
+    "\n",
+    "# Run name for saving the model in the hub\n",
+    "\n",
+    "RUN_NAME =  f\"{datetime.now():%Y-%m-%d_%H.%M.%S}-size{total_size}\"\n",
+    "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n",
+    "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
+    "HUB_MODEL_NAME"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1-t1nGgnVTU4"
+   },
+   "source": [
+    "## 🛠️ wandb Configuration"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load from Colab's secure storage\n",
+    "wandb_api_key = userdata.get('WANDB_API_KEY')\n",
+    "\n",
+    "# Load from environment variables (.env file) if running Locally (GPU setup)\n",
+    "# wandb_api_key = os.getenv('WANDB_API_KEY')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"WANDB_API_KEY\"] = wandb_api_key\n",
+    "wandb.login()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 156
+    },
+    "id": "yJNOv3cVvJ68",
+    "outputId": "0c03623e-6887-49e3-8989-bbe45dfc5d35"
+   },
+   "outputs": [],
+   "source": [
+    "# Configure Weights & Biases to record against our project\n",
+    "\n",
+    "LOG_TO_WANDB = True\n",
+    "\n",
+    "os.environ[\"WANDB_PROJECT\"] = PROJECT_NAME\n",
+    "os.environ[\"WANDB_LOG_MODEL\"] = \"checkpoint\" if LOG_TO_WANDB else \"end\"\n",
+    "os.environ[\"WANDB_WATCH\"] = \"gradients\"\n",
+    "\n",
+    "if LOG_TO_WANDB:\n",
+    "  wandb.init(project=PROJECT_NAME, name=RUN_NAME)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qJWQ0a3wZ0Bw"
+   },
+   "source": [
+    "## 📥 Load the Tokenizer and Model"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
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+     ]
+    },
+    "id": "R_O04fKxMMT-",
+    "outputId": "29aa1cf7-2a2e-492e-adc9-cd0a5bfb123e"
+   },
+   "outputs": [],
+   "source": [
+    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
+    "\n",
+    "quant_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,  # Reduce the precision to 4 bits\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() / 1e6:.1f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SrCE2Le7RBRj"
+   },
+   "source": [
+    "## ⚙️ Fine-tune our LLaMA 3 8B (4-bit quantized) model with QLoRA\n",
+    "- 1. Prepare the Data with a Data Collator\n",
+    "- 2. Define the QLoRA Configuration (LoraConfig)\n",
+    "- 3. Set the Training Parameters (SFTConfig)\n",
+    "- 4. Initialize the Fine-Tuning Trainer (SFTTrainer)\n",
+    "- 5. Run Fine-Tuning and Push to Hub"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9BYO0If4uWys"
+   },
+   "source": [
+    "### 🔄 1. Prepare the Data with a Data Collator\n",
+    "\n",
+    "We only want the model to learn the price, not the product description. Everything before \"Price is $\" is context, not training target. HuggingFace’s DataCollatorForCompletionOnlyLM handles this masking automatically:\n",
+    "\n",
+    "1. Tokenizes the response_template (\"Price is $\")\n",
+    "2. Finds its token position in each input\n",
+    "3. Masks all tokens before it (context)\n",
+    "4. Trains the model only on tokens after it (the price)\n",
+    "\n",
+    "\n",
+    "Example:\n",
+    "\n",
+    "Input: \"Product: Red T-shirt. Price is $12.99\"\n",
+    "\n",
+    "Masked: \"Product: Red T-shirt. Price is $\" → masked (no loss)\n",
+    "\n",
+    "\"12.99\" → not masked (model is trained to predict this)\n",
+    "\n",
+    "So the model learns to generate 12.99 given the context, but isn’t trained to repeat or memorize the description."
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2omVEaPIVJZa"
+   },
+   "outputs": [],
+   "source": [
+    "response_template = \"Price is $\"\n",
+    "collator = DataCollatorForCompletionOnlyLM(response_template, tokenizer=tokenizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4DaOeBhyy9eS"
+   },
+   "source": [
+    "### 🧠 2. Define the QLoRA Configuration (LoraConfig)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0HKuVS_XR3cw"
+   },
+   "outputs": [],
+   "source": [
+    "LORA_R = 32\n",
+    "LORA_ALPHA = 64\n",
+    "TARGET_MODULES = [\"q_proj\", \"v_proj\", \"k_proj\", \"o_proj\"]\n",
+    "LORA_DROPOUT = 0.1\n",
+    "\n",
+    "lora_parameters = LoraConfig(\n",
+    "    r=LORA_R,\n",
+    "    lora_alpha=LORA_ALPHA,\n",
+    "    target_modules=TARGET_MODULES,\n",
+    "    lora_dropout=LORA_DROPOUT,\n",
+    "    bias=\"none\",\n",
+    "    task_type=\"CAUSAL_LM\", # Specifies we're doing causal language modeling\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "uLfFsfNQSBAm"
+   },
+   "source": [
+    "### ⚙️ 3. Set the Training Parameters (SFTConfig)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7PKXdhPXSJot"
+   },
+   "outputs": [],
+   "source": [
+    "# 📦 Training Setup:\n",
+    "EPOCHS = 1\n",
+    "BATCH_SIZE = 16                     # A100 GPU can go up to 16\n",
+    "GRADIENT_ACCUMULATION_STEPS = 2\n",
+    "MAX_SEQUENCE_LENGTH = 182          # Max token length per input\n",
+    "\n",
+    "# ⚙️ Optimization:\n",
+    "LEARNING_RATE = 1e-4\n",
+    "LR_SCHEDULER_TYPE = 'cosine'\n",
+    "WARMUP_RATIO = 0.03\n",
+    "OPTIMIZER = \"paged_adamw_32bit\"\n",
+    "\n",
+    "# 💾 Checkpointing & Logging:\n",
+    "SAVE_STEPS = 200        # Checkpoint\n",
+    "STEPS = 20              # Log every 20 steps\n",
+    "save_total_limit = 10   # Keep latest 10 only\n",
+    "\n",
+    "\n",
+    "LOG_TO_WANDB = True\n",
+    "\n",
+    "HUB_MODEL_NAME = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
+    "\n",
+    "train_parameters = SFTConfig(\n",
+    "    # Output & Run\n",
+    "    output_dir=PROJECT_RUN_NAME,\n",
+    "    run_name=RUN_NAME,\n",
+    "    dataset_text_field=\"text\",\n",
+    "    max_seq_length=MAX_SEQUENCE_LENGTH,\n",
+    "\n",
+    "    # Training\n",
+    "    num_train_epochs=EPOCHS,\n",
+    "    per_device_train_batch_size=BATCH_SIZE,\n",
+    "    gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,\n",
+    "    max_steps=-1,\n",
+    "    group_by_length=True,\n",
+    "\n",
+    "    # Evaluation\n",
+    "    eval_strategy=\"steps\",\n",
+    "    eval_steps=STEPS,\n",
+    "    per_device_eval_batch_size=1,\n",
+    "\n",
+    "    # Optimization\n",
+    "    learning_rate=LEARNING_RATE,\n",
+    "    lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
+    "    warmup_ratio=WARMUP_RATIO,\n",
+    "    optim=OPTIMIZER,\n",
+    "    weight_decay=0.001,\n",
+    "    max_grad_norm=0.3,\n",
+    "\n",
+    "    # Precision\n",
+    "    fp16=False,\n",
+    "    bf16=True,\n",
+    "\n",
+    "    # Logging & Saving\n",
+    "    logging_steps=STEPS,            # See loss after each {STEP} batches\n",
+    "    save_strategy=\"steps\",\n",
+    "    save_steps=SAVE_STEPS,          # Model Checkpointed locally\n",
+    "    save_total_limit=save_total_limit,\n",
+    "    report_to=\"wandb\" if LOG_TO_WANDB else None,\n",
+    "\n",
+    "    # Hub\n",
+    "    push_to_hub=True,\n",
+    "    hub_strategy=\"end\",  # Only push once, at the end\n",
+    "    load_best_model_at_end=True, # Loads the best eval_loss checkpoint\n",
+    "    metric_for_best_model=\"eval_loss\", # Monitors eval_loss\n",
+    "    greater_is_better=False, # Lower eval_loss = better model\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1q-a3LHDSoxQ"
+   },
+   "source": [
+    "### 🧩 4. Initialize the Fine-Tuning Trainer (SFTTrainer)\n",
+    "Combining everything"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 290,
+     "referenced_widgets": [
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+      "a8a3e5973ee5441087d10dfb17bfa1d6",
+      "64c3b3c02e844df6bfd3acf1ee23d765",
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+      "07cb9756d1814a7ba7fb49cccb2763cb",
+      "492454ad524742bd8bb3f5c3d5b37feb",
+      "e98053f6b7f045da812088d1e76d3a31",
+      "f2aeb3ae99cc4b7ca97fb959df1150ad",
+      "f92e18b6ab0147b1b428724f5155ca61",
+      "14356b2447e349ee8478478eb231fa81",
+      "f244a7e331d941f5a99712dcbc5550ea"
+     ]
+    },
+    "id": "fCwmDmkSATvj",
+    "outputId": "2b4adc75-e0db-4e0b-c90b-9f9ff2dfd3c6"
+   },
+   "outputs": [],
+   "source": [
+    "# The latest version of trl is showing a warning about labels - please ignore this warning\n",
+    "fine_tuning = SFTTrainer(\n",
+    "    model=base_model,\n",
+    "    train_dataset=train_data,\n",
+    "    eval_dataset=val_data,\n",
+    "    peft_config=lora_parameters,    # QLoRA config\n",
+    "    args=train_parameters,          # SFTConfig\n",
+    "    data_collator=collator,\n",
+    "    callbacks=[EarlyStoppingCallback(early_stopping_patience=5)] # Early stop if no val improvement for 5 steps\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vHz6JA5_XJ07"
+   },
+   "source": [
+    "### 🚀 5. Run Fine-Tuning and Push to Hub"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "GfvAxnXPvB7w",
+    "outputId": "d351d89a-b3d7-4e2b-fee2-5ba2e929837e"
+   },
+   "outputs": [],
+   "source": [
+    "fine_tuning.train()\n",
+    "print(f\"✅ Best model pushed to HF Hub: {HUB_MODEL_NAME}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_train_eval_loss_steps.png?raw=true)\n",
+    "\n",
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_train_eval_loss_wandb.png?raw=true)\n",
+    "\n",
+    "This chart shows training loss vs evaluation loss over steps during fine-tuning of Llama 31 8B 4-Bit FT (20K Samples).\n",
+    "\n",
+    "- Blue line (train/loss): Decreasing overall, with some noise. Final value: 1.8596.\n",
+    "- Orange line (eval/loss): Smoother and consistently lower than training loss. Final value: 1.8103.\n",
+    "\n",
+    "- No overfitting: Eval loss < train loss throughout — a good sign.\n",
+    "- Stable convergence: Both curves flatten around step 500, suggesting the model is reaching training stability.\n",
+    "- Final eval loss is low, indicating decent generalization to unseen data.\n",
+    "\n",
+    "This fine-tuning run looks healthy. We can likely push further with more data - 400K run."
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 938
+    },
+    "id": "32vvrYRVAUNg",
+    "outputId": "bb4ab0f6-c390-48f3-a71c-2d259bb0ec0b"
+   },
+   "outputs": [],
+   "source": [
+    "if LOG_TO_WANDB:\n",
+    "  wandb.finish()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](https://github.com/lisek75/nlp_llms_notebook/blob/main/assets/09_run_summary_qlora_llama.png?raw=true)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IyKZ0r38IfT3"
+   },
+   "source": [
+    "Now that our best model is pushed to Hugging Face, let’s put it to the test.\n",
+    "\n",
+    "🔜 See you in the [next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part7_eval_llama_qlora.ipynb)"
+   ],
+   "outputs": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "A100",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/week7/community_contributions/lisekarimi/09_part7_eval_llama_qlora.ipynb b/week7/community_contributions/lisekarimi/09_part7_eval_llama_qlora.ipynb
new file mode 100644
index 0000000..bfe78d1
--- /dev/null
+++ b/week7/community_contributions/lisekarimi/09_part7_eval_llama_qlora.ipynb
@@ -0,0 +1,739 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GHsssBgWM_l0"
+   },
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 7)\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 7: Evaluating the Fine-Tuned LLaMA 3.1 8B (Quantized)\n",
+    "\n",
+    "- 🧑‍💻 Skill Level: Advanced\n",
+    "- ⚙️ Hardware: ⚠️ GPU required - use Google Colab\n",
+    "- 🛠️ Requirements: 🔑 HF Token\n",
+    "- Tasks:\n",
+    "    - Load the tokenizer and fine-tuned base model\n",
+    "    - Load the PEFT adapter for the fine-tuned weights\n",
+    "    - Run evaluation — the moment of truth!\n",
+    "\n",
+    "🔔 **Reminder:**  \n",
+    "As mentioned in Part 6, I fine-tuned the model on only 20K samples.  \n",
+    "In this notebook, we’ll evaluate both this model and the full 400K-sample version fine-tuned by our instructor.\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "MDyR63OTNUJ6"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required packages in Google Colab\n",
+    "%pip install -q datasets transformers torch peft bitsandbytes matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-yikV8pRBer9"
+   },
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "\n",
+    "import math\n",
+    "import torch\n",
+    "from huggingface_hub import login\n",
+    "import torch.nn.functional as F\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
+    "from datasets import load_dataset\n",
+    "from peft import PeftModel\n",
+    "import matplotlib.pyplot as plt\n",
+    "from google.colab import userdata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WyFPZeMcM88v"
+   },
+   "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')\n",
+    "login(hf_token, add_to_git_credential=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "30lzJXBH7BcK"
+   },
+   "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=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: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
+    "\n",
+    "    def chart(self, title):\n",
+    "        # max_error = max(self.errors)\n",
+    "        plt.figure(figsize=(12, 8))\n",
+    "        max_val = max(max(self.truths), max(self.guesses))\n",
+    "        plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
+    "        plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
+    "        plt.xlabel('Ground Truth')\n",
+    "        plt.ylabel('Model Estimate')\n",
+    "        plt.xlim(0, max_val)\n",
+    "        plt.ylim(0, max_val)\n",
+    "        plt.title(title)\n",
+    "\n",
+    "        # Add color legend\n",
+    "        from matplotlib.lines import Line2D\n",
+    "        legend_elements = [\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Accurate (green)', markerfacecolor='green', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='Medium error (orange)', markerfacecolor='orange', markersize=8),\n",
+    "            Line2D([0], [0], marker='o', color='w', label='High error (red)', markerfacecolor='red', markersize=8)\n",
+    "        ]\n",
+    "        plt.legend(handles=legend_elements, loc='upper right')\n",
+    "\n",
+    "        plt.show()\n",
+    "\n",
+    "\n",
+    "    def report(self):\n",
+    "        average_error = sum(self.errors) / self.size\n",
+    "        rmsle = math.sqrt(sum(self.sles) / self.size)\n",
+    "        hits = sum(1 for color in self.colors if color==\"green\")\n",
+    "        title = f\"{self.title} Error=${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": "markdown",
+   "metadata": {},
+   "source": [
+    "# 📥 Load Dataset"
+   ],
+   "outputs": []
+  },
+  {
+   "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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 177,
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+      "8280e432938b4e9794c95e47bb9c02fa",
+      "abdd2ff8028b432091434805f81c455c"
+     ]
+    },
+    "id": "cvXVoJH8LS6u",
+    "outputId": "6308b124-a922-4e82-fb6a-5933d3c324e0"
+   },
+   "outputs": [],
+   "source": [
+    "DATASET_NAME = \"lisekarimi/pricer-data\"\n",
+    "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": "xb86e__Wc7j_",
+    "outputId": "8b699099-7414-4663-fab1-d069d3ec3d35"
+   },
+   "outputs": [],
+   "source": [
+    "test[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "qJWQ0a3wZ0Bw"
+   },
+   "source": [
+    "## 📥 Load Tokenizer and Model\n",
+    "The fine-tuned model (PeftModel) only holds the LoRA adapters, so it requires the base model to apply them correctly."
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 401,
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+     ]
+    },
+    "id": "lAUAAcEC6ido",
+    "outputId": "b2983922-5036-4083-8cba-0cb3f51fbc51"
+   },
+   "outputs": [],
+   "source": [
+    "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
+    "\n",
+    "quant_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,  # Reduce the precision to 4 bits\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
+    "    bnb_4bit_quant_type=\"nf4\"\n",
+    ")\n",
+    "\n",
+    "# Load the Tokenizer and the Model\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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2RJ0G-WRJGMK"
+   },
+   "source": [
+    "## 🧪 Load and Evaluate the Fine-Tuned Model with PEFT Adapters"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 20K Sample Fine-Tuned Model"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
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+     ]
+    },
+    "id": "R_O04fKxMMT-",
+    "outputId": "06fc64f8-3407-460b-e093-0293e958915e"
+   },
+   "outputs": [],
+   "source": [
+    "# Load lisekarimi model (trained on 20K datapoints)\n",
+    "\n",
+    "FINETUNED_MODEL = \"lisekarimi/llama3-pricer-2025-04-08_18.44.04-size20000\"\n",
+    "fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL)\n",
+    "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")\n",
+    "fine_tuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Je5dR8QEAI1d"
+   },
+   "outputs": [],
+   "source": [
+    "# Gets top 3 predicted tokens from the model\n",
+    "# Filters valid numeric outputs (prices)\n",
+    "# Returns a weighted average based on token probabilities\n",
+    "\n",
+    "# This code would be more complex if we couldn't take advantage of the fact\n",
+    "# That Llama generates 1 token for any 3 digit number\n",
+    "\n",
+    "top_K = 3\n",
+    "\n",
+    "def improved_model_predict(prompt, device=\"cuda\"):\n",
+    "    set_seed(42) # Reproducibility : same prompt = same o/p every time\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(): # Do not track gradients during inference\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",
+    "\n",
+    "    prices, weights = [], [] # weights = corresponding probabilities\n",
+    "\n",
+    "    for i in range(top_K):\n",
+    "      predicted_token = tokenizer.decode(top_token_id[0][i])\n",
+    "      probability = top_prob[0][i]\n",
+    "\n",
+    "      try:\n",
+    "        result = float(predicted_token)\n",
+    "      except ValueError as e:\n",
+    "        result = 0.0\n",
+    "\n",
+    "      if result > 0:\n",
+    "        prices.append(result)\n",
+    "        weights.append(probability)\n",
+    "\n",
+    "    if not prices:\n",
+    "      return 0.0, 0.0\n",
+    "\n",
+    "    total = sum(weights)\n",
+    "\n",
+    "    weighted_prices = [price * weight / total for price, weight in zip(prices, weights)]\n",
+    "\n",
+    "    return sum(weighted_prices).item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t_GHfTwHXD5f",
+    "outputId": "056b0fc2-5632-4be8-ee24-b6bcefe14ab9"
+   },
+   "outputs": [],
+   "source": [
+    "improved_model_predict(test[0][\"text\"], device=\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "W_KcLvyt6kbb",
+    "outputId": "fba4200d-b911-467b-ab3c-17b78aa3b408"
+   },
+   "outputs": [],
+   "source": [
+    "Tester.test(improved_model_predict, test)"
+   ]
+  },
+  {
+   "attachments": {
+    "0dcb25a7-83fa-4313-a94f-d3a56a0f07bc.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:0dcb25a7-83fa-4313-a94f-d3a56a0f07bc.png)"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 400K Sample Fine-Tuned Model"
+   ],
+   "outputs": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000,
+     "referenced_widgets": [
+      "dd1b57e03f2641d3b702f2cc66942b8f",
+      "e1d477dccbfc44a8a6da301486180e82",
+      "c312a5111a284c3db88f22290869c023",
+      "ce118d8b8146497f9c7fdd3b38188e72",
+      "bc46c271637341bb82d6b87df22ab2af",
+      "602adf3242f54731938b68d3cf68465e",
+      "39fae5e74834421795729a259a046fb8",
+      "0618d8626e2e46cb9a17f86444de3c48",
+      "1cd43b5b2fe445088c84e19773ad861e",
+      "f70a29870ab34f34a1900b2df2bf177e",
+      "41a96c5e35a44b898b872c189f531d3a",
+      "0a524a73d5d6478db81256371bf2bc9b",
+      "275f6179dc624bceaa5d0639fe0b1b00",
+      "79c41b26746344bc9a220f2376360110",
+      "287a6430766c44e5a71dda1048fa2a2c",
+      "3bbe1a454a854747a96fe83e91d6cb3c",
+      "8a93759afe21414fb0d6684f0a591d60",
+      "a3d76b3ce67a495db861bac80cfc0864",
+      "8fc794262ed14fc785c8f06e734c57d4",
+      "7dc967baa0e7427bb66cf3e26849d508",
+      "2d7a6dbd15304347a37dbfb6e5ec7203",
+      "288393e05947444bad11034071015baf"
+     ]
+    },
+    "id": "Kl6n_0sAbU0g",
+    "outputId": "2fb53efb-da22-4c29-a594-c2cf5a079388"
+   },
+   "outputs": [],
+   "source": [
+    "FINETUNED_MODEL = \"ed-donner/pricer-2024-09-13_13.04.39\"\n",
+    "REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\"\n",
+    "fine_tuned_model = PeftModel.from_pretrained(base_model, FINETUNED_MODEL, revision=REVISION)\n",
+    "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")\n",
+    "fine_tuned_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "R0YlorBhbeSE",
+    "outputId": "f42de9bf-d45a-4d2d-c218-fe000d716e54"
+   },
+   "outputs": [],
+   "source": [
+    "Tester.test(improved_model_predict, test)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "🎉  And there it is — the open-source, quantized, and fine-tuned model outperforms the rest. 🙌 \n",
+    "\n",
+    "📘 We'll continue in [the next notebook](https://github.com/lisekarimi/lexo/blob/main/09_part8_summary.ipynb) with a final wrap-up and summary of key insights.\n"
+   ],
+   "outputs": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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\ No newline at end of file
diff --git a/week7/community_contributions/lisekarimi/09_part8_summary.ipynb b/week7/community_contributions/lisekarimi/09_part8_summary.ipynb
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@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GHsssBgWM_l0"
+   },
+   "source": [
+    "# 🔍 Predicting Item Prices from Descriptions (Part 8)\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 8: Summary & Leaderboard\n",
+    "\n",
+    "![](https://github.com/lisekarimi/lexo/blob/main/assets/09_ft_leaderboard.png?raw=true)\n",
+    "\n",
+    "# 🥇 The winner is the LLaMA 3.1 8B (4-bit) fine-tuned on 400K samples \n",
+    "\n",
+    "LLaMA 3.1 8B (4-bit) fine-tuned on 400K samples is outperforming even the big guy GPT-4o  — with the lowest error and highest accuracy (75.6%).\n",
+    "\n",
+    "RAG + GPT-4o Mini also did well, proving that retrieval adds real value.\n",
+    "\n",
+    "On the other hand, traditional ML models and even human guesses, gave weaker results and fell behind the top models.\n",
+    "\n",
+    "💡 As we’ve seen, a **well-tuned open-source small model** can do amazing things on a focused task — sometimes even better than large, closed models.\n",
+    "It’s not about size — it’s about fit, focus, and fine-tuning.\n",
+    "\n",
+    "# ✨ Conclusion\n",
+    "What a journey! From classic ML to state-of-the-art LLMs, from embeddings to retrieval and fine-tuning — we explored it all to answer: who predicts prices best?\n",
+    "\n",
+    "Thanks for following along — see you in the next challenge! 🚀\n",
+    "\n",
+    "---\n",
+    "📢 Find more LLM notebooks on my [GitHub repository](https://github.com/lisekarimi/lexo)"
+   ],
+   "outputs": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
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\ No newline at end of file