From ca180abc9d2f910566b43cee5032dfbdfbc68b22 Mon Sep 17 00:00:00 2001 From: Hope Ogbons Date: Tue, 4 Nov 2025 04:19:38 +0100 Subject: [PATCH] Remove week 7 exercise notebook with 4k7 lines of code --- .../hopeogbons/week7 EXERCISE.ipynb | 4722 ----------------- 1 file changed, 4722 deletions(-) delete mode 100644 week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb diff --git a/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb b/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb deleted file mode 100644 index 4e72ea9..0000000 --- a/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb +++ /dev/null @@ -1,4722 +0,0 @@ -{ - "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": [ - "07510562863e4ccd9bfc6e0e8e8d0cc7", - "7ba3df960cd1441faa3929d35282436a", - "39d6e3eb51344d17b802d722fc8e907f", - "3296341b5cbb4afdb5c4c122c35641e7", - "60b39b5c7cd348a8862585d4a2758882", - "6bcb8d76ee1d40adb9ae1295a1335ce6", - "ba2da0338a374d14bc113f7e51424c95", - "b28a24a1354b4482a4d81a264653cc34", - "0147ec6e0aed432a9deb5a2919d26461", - "eaec063caaef41f3b0ad844ae83c39e6", - "acbe8ffde00741a380ab7c9c3024c13f", - "b0aa68af34254537a1ac985cc81360bb", - "8fc3c65719bd4cf087c2a65ce257b0fa", - "accd101b0d9f4ad2bb5e82cbdcd6a2b0", - "a28f5a81306944b89c807c396d1ead40", - "004988bb9a47475088b32ecd848e1bb9", - "a0a9721c28544a669472d132245360ae", - "919449534447415d89f5d2d4da04b626", - "2683537bff4e4909934679627cb912d4", - "a752bf3b92844163988bd35cf638c1ca", - "9b49efee6132497b9a7e9d789a2ce7f0", - "2221864985b24580be722d31597f84fb", - "7a42b1dc6faf4dc6aa0358adff83b954", - "b1ae08d48b764ab6b994dce41b28dc90", - "beffadbb38c7441289a0b4fe86e64f50", - "06070ef16fba4dd59ba5e4503ff0f2c2", - "51522bdb65c049ad8a5386ba4ab71d4f", - "ba63379a98584bbf842eea0f078a63ff", - "1afa66d2cd7b4872b457c4dbbbf1c8bb", - "361114e64bbb49c98080656f8495c9b4", - "5b88ff1c80f241d6be2dbdfb621b5785", - "287ff3894798460e8bffca31d64cc8af", - "8764302e5e96446fb6e832466a6b9556", - "51c0f52a3d634504be310a357c0f8012", - "f4394499258348d7992bbec909929945", - "2fe372b94a6d4c4d818fddae187d129d", - "e6be1b2f426e43e6a5935731b8d5a6d4", - "8b32e96633c1426285f3da81b0ad9a30", - "3e534a5302124dd2a6ad31ce90c86dc1", - "d20dc830ca1342ecb155a601cc4b347e", - "f0b6140c0f3c40ac8665183d6c9557b3", - "313da770c7a14ace9ed9c90d1395160f", - "3dfb2aeed50f406b93db2c3223f792bf", - "b95fc74b56ea41cb82ac7cead5dae409", - "db002f333a0240ffaeeed36b78f896bc", - "9141c4c50eb442e58d64564dfd63c282", - "167444cb15f4459f977326e15d8666c2", - "7a49b2fb1c644219b51e3393ca424317", - "2d234d40fdf245dba5ddbdd93cb03fef", - "2e191cb83f9749c38a761677a61bfe95", - "7928e8207e3d421cb5498e0c184c3893", - "623182cc0976492bad91b2ab8484b697", - "843da7afb2b24ecaa3c3acc58f3b12f7", - "860d45070ea145d6b81c8ee226b6c3be", - "32efcdc2927d4d2987e5e36298a86cdb" - ] - }, - "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": [ - "6186404f971248879b170152fa2646d9", - "eab0e5b68fb14b14adb58a454037e354", - "28e53342fa3640658b49a19115b61342", - "0e0a1c5e782345b592b7528e384bf5a1", - "c1fa6529b9e94efab5d850d0c7e70f57", - "9e342643ee1b4466818006592feec188", - "66c8a769d8fc4685a2bd0ec843c0eb74", - "06186348ef4f4c11997d6502aa57776c", - "a4e14741a64d4b82a509daf4f70e17e4", - "b5c2450c91624cc18bd763bbd6002b49", - "b8198c2eafa141cdacc7955194787925", - "2a47b72104e14771a2192848add9faa7", - "75c4e81482574966a526fa76ba6eef53", - "0eae3d740a854ad38d56a406955171d1", - "931d5f114b5d49aea7e8cf71556f1d5c", - "81c8e3f132bc4b1fb4ee51efa3ae35c6", - "2c9cbea6313741d5b66cd29c262dab0d", - "31e9f633af8a473db51eb30605bddf41", - "278d31ba28ab46008513da762561d2db", - "5b4cec8c83124ae4a826622b885e1f0c", - "ead384472ffd46e1a62134753067546d", - "a0871b07a3254ae7a7796618d9c4773e", - "788a849789824b16a832f912f10c15cd", - "8171a97703044bf392b2e5d38ae1f5e6", - "73d9ee184c8d48df9cce2a69cd02e129", - "6a5e16d83cfe4d77bd8bb378b86732f9", - "7b3143e176c744df8135d48cdc6d7fa2", - "e4ebe05911e848ff96d2e1e71321264b", - "bea4d0a1b32b4beb99c24e5e85ff9f43", - "efc09d50d1734066aad77b3496ccbae7", - "629aefaebf3a4a998a662cb03571b09f", - "837efe0f9f964dcb9121028015abf0c5", - "cf82e39052fd4d9db83c5d0b66f39830", - "5fe709b088614ca6b9a9459b0fb19c4a", - "d7df60d9484840bc8f020d4d3f640c5b", - "d668a30f6fe5450d8603877a8c361315", - "01bb3481b4ac40818e4e9797129f52a0", - "df2a116de3ba4da1a1ce8f197b4bae3c", - "654a359bf9f34ebcbfe3f9ed57a161e5", - "54b26e79a2774352abc6dd833cc78825", - "9af673531da74295ac96360fd4a0e6bf", - "13ae9360a5a54208a160432fee141276", - "10a3dea6b2154b9891676bd4628d6648", - "12e2006edb7c4168811da98a3089b452", - "c743b87018be478784c0b8957c6c9324", - "ebf10ba30dc4414b9a31064caeab16fd", - "76002f5260914f3b9865388e59853d8c", - "46cb877532d44227b057340bafdcd119", - "a427d45295b84f8890c8e9a27a72a240", - "947dabaa331643c2b02bcb3be121ca27", - "dbc0a0c8fb6e434eba85fc88e9e34f16", - "973f476e820d4c52a69416078a5ec93e", - "10019572c2034d5ab886918d650b4577", - "6f342dfb24094db593ec0b28d2466944", - "40c40db0e9bb41d38102d3d32df613cc", - "76e4b082ce6744f694fa6518b26ac5e3", - "f0691e0181b946158b7a4385979e7e45", - "9df127a7e91e474386b4ea4fc4185a41", - "d01c2ad68999427dac089a3c33ee4a2e", - "550c526d48fb44828b14ec9ac484a707", - "d8fac6bad88544e39b9c80ba58e1bdad", - "cb831d4d7b044d1bb161357b346c9041", - "b813ef444b7b4c8f909f7bc037cc3d6c", - "e1dc5ca40e2e4b66834a56378071d762", - "f7e8d5edb96b45908ac8f44e356e331a", - "4ce5bb59f9eb448bbc3c500d937a715c", - "dd2eb889c4f94627bf081796a89377c2", - "45e7b4ca18a048d98b73820e4f36d004", - "5e1ccb5c69ea49b594da6b19909ad381", - "1d228273c34e439dad6d3e63aeccd8cf", - "b440d7fc628844e89fbf6c9fe864ef50", - "c5d06dcdda354dac87406720fae4f308", - "ec6b5d741aa1480cb197cf3902e906ed", - "34bc2ff5443e43e59e5b6d1a3e40280e", - "9f6b80990d224675ae501f941cb1b2c2", - "1272fc0298ff486fbe5215b7d93028c0", - "ded6c69382554ded9d1e4c894c1fea11" - ] - }, - "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" - }, - "outputs": [], - "source": [ - "# Run evaluation on 250 test examples\n", - "Tester.test(improved_model_predict, test)" - ] - }, - { - "cell_type": "markdown", - "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" - ] - }, - { - "cell_type": "markdown", - "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" - ] - } - ], - "metadata": { - "accelerator": "GPU", - 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