diff --git a/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb b/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb new file mode 100644 index 0000000..4e72ea9 --- /dev/null +++ b/week7/community_contributions/hopeogbons/week7 EXERCISE.ipynb @@ -0,0 +1,4722 @@ +{ + "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 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