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LLM_Engineering_OLD/week7/community_contributions/kachaje-andela-genai-bootcamp/week7-exercise.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "xhF_8Gy3BkkJ"
},
"source": [
"## Predict Product Prices"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 268402,
"status": "ok",
"timestamp": 1761625271540,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "VB1OA8NQBen3",
"outputId": "17d24790-e768-418c-b0c6-ea4b2d2b6371"
},
"outputs": [],
"source": [
"# pip installs\n",
"\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": {
"executionInfo": {
"elapsed": 23322,
"status": "ok",
"timestamp": 1761625303538,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "3czhPr1DCAmh"
},
"outputs": [],
"source": [
"# imports\n",
"\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": {
"executionInfo": {
"elapsed": 16,
"status": "ok",
"timestamp": 1761625310250,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "7148hFC8CMLK"
},
"outputs": [],
"source": [
"# Constants\n",
"\n",
"BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n",
"PROJECT_NAME = \"pricer\"\n",
"HF_USER = \"ed-donner\" # your HF name here! Or use mine if you just want to reproduce my results.\n",
"\n",
"# The run itself\n",
"\n",
"RUN_NAME = \"2024-09-13_13.04.39\"\n",
"PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n",
"REVISION = \"e8d637df551603dc86cd7a1598a8f44af4d7ae36\" # or REVISION = None\n",
"FINETUNED_MODEL = f\"{HF_USER}/{PROJECT_RUN_NAME}\"\n",
"\n",
"# Uncomment this line if you wish to use my model\n",
"# FINETUNED_MODEL = f\"ed-donner/{PROJECT_RUN_NAME}\"\n",
"\n",
"# Data\n",
"\n",
"DATASET_NAME = f\"{HF_USER}/pricer-data\"\n",
"# Or just use the one I've uploaded\n",
"# DATASET_NAME = \"ed-donner/pricer-data\"\n",
"\n",
"# Hyperparameters for QLoRA\n",
"\n",
"QUANT_4_BIT = True\n",
"\n",
"%matplotlib inline\n",
"\n",
"# Used for writing to output in color\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}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 1218,
"status": "ok",
"timestamp": 1761625315056,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "sFFKEgn9Cbh_"
},
"outputs": [],
"source": [
"# Log in to HuggingFace\n",
"\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": 177,
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},
"executionInfo": {
"elapsed": 11900,
"status": "ok",
"timestamp": 1761625329031,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "8ykpbUcoCfcg",
"outputId": "2cf8df60-dc4c-4030-b0f1-bd271e728ca0"
},
"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/",
"height": 144
},
"executionInfo": {
"elapsed": 57,
"status": "error",
"timestamp": 1761630593442,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "wJzJ2xxQCnFq",
"outputId": "6515252d-4310-4d87-81ea-74decff164b6"
},
"outputs": [],
"source": [
"test[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 13,
"status": "ok",
"timestamp": 1761625337005,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "bV_Ns-b1CxVb"
},
"outputs": [],
"source": [
"# pick the right quantization (thank you Robert M. for spotting the bug with the 8 bit version!)\n",
"\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",
" )"
]
},
{
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"base_uri": "https://localhost:8080/",
"height": 574,
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},
"executionInfo": {
"elapsed": 1468968,
"status": "ok",
"timestamp": 1761626809118,
"user": {
"displayName": "Chimwemwe Kachaje",
"userId": "01022304238626387479"
},
"user_tz": -120
},
"id": "YYZMohOuCySf",
"outputId": "d0c304b8-39f0-40a3-9e38-feb157a02056"
},
"outputs": [],
"source": [
"# 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",
"\n",
"# Load the fine-tuned model with PEFT\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",
"\n",
"print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 18,
"status": "ok",
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"fine_tuned_model"
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"cell_type": "code",
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"metadata": {
"executionInfo": {
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"source": [
"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": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"executionInfo": {
"elapsed": 3,
"status": "ok",
"timestamp": 1761626809151,
"user": {
"displayName": "Chimwemwe Kachaje",
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"id": "uDk8XkaCC-pp",
"outputId": "74f196a1-69af-4d60-d399-d3468601d9a2"
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"outputs": [],
"source": [
"extract_price(\"Price is $a fabulous 899.99 or so\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 1,
"status": "ok",
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"displayName": "Chimwemwe Kachaje",
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"source": [
"# Original prediction function takes the most likely next token\n",
"\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(inputs, attention_mask=attention_mask, max_new_tokens=3, num_return_sequences=1)\n",
" response = tokenizer.decode(outputs[0])\n",
" return extract_price(response)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"executionInfo": {
"elapsed": 2,
"status": "ok",
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"displayName": "Chimwemwe Kachaje",
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"source": [
"# An improved prediction function takes a weighted average of the top 3 choices\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)\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()"
]
},
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"cell_type": "code",
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"executionInfo": {
"elapsed": 2,
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"displayName": "Chimwemwe Kachaje",
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"source": [
"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: ${error:,.2f} SLE: {sle:,.2f} Item: {title}{RESET}\")\n",
"\n",
" def chart(self, title):\n",
" max_error = max(self.errors)\n",
" plt.figure(figsize=(12, 8))\n",
" max_val = max(max(self.truths), max(self.guesses))\n",
" plt.plot([0, max_val], [0, max_val], color='deepskyblue', lw=2, alpha=0.6)\n",
" plt.scatter(self.truths, self.guesses, s=3, c=self.colors)\n",
" plt.xlabel('Ground Truth')\n",
" plt.ylabel('Model Estimate')\n",
" plt.xlim(0, max_val)\n",
" plt.ylim(0, max_val)\n",
" plt.title(title)\n",
" plt.show()\n",
"\n",
" def report(self):\n",
" average_error = sum(self.errors) / self.size\n",
" rmsle = math.sqrt(sum(self.sles) / self.size)\n",
" hits = sum(1 for color in self.colors if color==\"green\")\n",
" title = f\"{self.title} Error=${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()"
]
},
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