From 6fd40c4bea24691ed8589216d179f0f804d4b908 Mon Sep 17 00:00:00 2001 From: KB Date: Thu, 30 Oct 2025 07:43:16 -0400 Subject: [PATCH] kwabena_bootcamp --- .../kwabena/PEFT with Llama.ipynb | 7205 +++++++++++++++++ 1 file changed, 7205 insertions(+) create mode 100644 week7/community_contributions/kwabena/PEFT with Llama.ipynb diff --git a/week7/community_contributions/kwabena/PEFT with Llama.ipynb b/week7/community_contributions/kwabena/PEFT with Llama.ipynb new file mode 100644 index 0000000..1696eff --- /dev/null +++ b/week7/community_contributions/kwabena/PEFT with Llama.ipynb @@ -0,0 +1,7205 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "GHsssBgWM_l0" + }, + "source": [ + "\n", + "\n", + "## Predict Product Prices\n", + "\n", + "### And now, to evaluate our fine-tuned open source model\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MDyR63OTNUJ6", + "outputId": "eba91112-144e-41b2-91c4-e6b2ad338948" + }, + "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": { + "id": "-yikV8pRBer9" + }, + "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": { + "id": "uuTX-xonNeOK" + }, + "outputs": [], + "source": [ + "# Constants\n", + "\n", + "BASE_MODEL = \"meta-llama/Meta-Llama-3.1-8B\"\n", + "PROJECT_NAME = \"pricer\"\n", + "HF_USER = \"ampelox\" # your HF name here! Or use mine if you just want to reproduce my results.\n", + "\n", + "# The run itself\n", + "\n", + "RUN_NAME = \"2025-10-30_09.40.59\"\n", + "PROJECT_RUN_NAME = f\"{PROJECT_NAME}-{RUN_NAME}\"\n", + "REVISION = \"dd79bbfe3922ac56eeba2b2473ca35b08beedaa4\" # 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": "markdown", + "metadata": { + "id": "8JArT3QAQAjx" + }, + "source": [ + "### Log in to HuggingFace\n", + "\n", + "If you don't already have a HuggingFace account, visit https://huggingface.co to sign up and create a token.\n", + "\n", + "Then select the Secrets for this Notebook by clicking on the key icon in the left, and add a new secret called `HF_TOKEN` with the value as your token.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "WyFPZeMcM88v" + }, + "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": 233, + "referenced_widgets": [ + "ffd4d0873de54f8facd041dcbf481105", + "4db0c65fa1ea4586b0d3c600513a6e53", + "0da2106789254bd5bc16b1fdbf86c118", + "20b77b3d83aa49c3b2b5fda58e5efc59", + "5c064fbdd5ba40d8a15bbcc1a97434b0", + "1b0c4d269617449f93fda490ca5c70e5", + "1a7c0f77fbbd40ea8ceb2674f4a6fb1e", + "1f23ebbce9e24f0981b5f7d4f8f9b507", + "bf7012b2760545faa96a239a12b4eddb", + "8298e5ae7abb480d8d136d56e443c515", + "9a9fb639510741c19f4d14be6994d92a", + "3f71cbd40e334f6da5661f8daa02945f", + "82a5916a81084df2a4616037ccdef025", + "7c3639f3d4854ae2978821f1c8bc0670", + "6b7fab4627af4a93baddac4210e2a9ea", + "7cae9ff5d132426cadc874cfbaffba06", + "2272983d621443b0ad47863156c0e0c3", + "8bffcf4ebe9642c68a707d9d992a4fee", + "eda741669314433287edb5b1e87ba81a", + "489f90672d064cb1bef28fa02ca48677", + "72009c83a3ff4a26899631fcc98a60e4", + "49fe2b8355ca4d11974129913d0b7ba9", + "ca5e0eccdb524b31a7da4b2f733b799b", + "e5e9406ac05e48589c997c63198dd0df", + "1eb8703f483649729ecb875955a9ff1a", + "455a0653a65e415eb381a113e5a99ab1", + "4f53585125bd49be9987d4501cac03d7", + "67ad8b5959b44f83aa66460d7cea6e39", + "87b6fa11864943e4a15db692c48ad449", + "6aa4155e792344a0bff9d9e5f9407515", + "421b53e43e404425a659033fa4e8c71f", + "2e210420e0c9468886b90540646d5abb", + "636c9c4257b64e6babbf26ba90b42d0b", + "9506aa9ba454461cbed217b005cea710", + "d46df04021684c2fb44fc9211b4be791", + "de34dfd1f0ce446fb41751092b3f663d", + "123fab0dbc084b54b30dd46902a960d7", + "d3eaf4e902444290be911d8232c350c4", + "25bbaf4c01ab468685ffedbba320a78b", + "0be6499e4c8c4772b75aec98c021ce04", + "a7641f0b3b3f4193bf789f09d1452c42", + "d206729f54954c54bb0ef6c375e4c463", + "c23239b8b047463ab2b9461aada0f1e9", + "763e5d6d49c14c9b8187518264fac08d", + "3d35322adec449949405136a45acb160", + "c2648e7640974418a991b7c50e0c89f6", + "eeac64b7636141f8ada469897a2aabcc", + "f23fbf922fb84fedb21adf6553fce54c", + "4b6a5400d0964092afe03300f8443531", + "673538ecc8b846ee9aaa8d72ef426082", + "917066ea6a53484ba3895e35a7a6cd4f", + "7035b98fdadf40b684023d07eb59f4b7", + "7f2b5cf2c72849acaaca98fe537c217b", + "98b67ec0e2d64a68bf9459ba8318ea5a", + "b5eb1f1846f34f70a18d325315b0ea77" + ] + }, + "id": "cvXVoJH8LS6u", + "outputId": "6ff12ecc-fa8b-4f7f-afc9-3690f52bc6d6" + }, + "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": "xb86e__Wc7j_", + "outputId": "d398aca0-3e80-46e7-e9e3-fd4a7ca0408d" + }, + "outputs": [], + "source": [ + "test[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qJWQ0a3wZ0Bw" + }, + "source": [ + "## Now load the Tokenizer and Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lAUAAcEC6ido" + }, + "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", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 594, + "referenced_widgets": [ + "9d3b4350362044e18f0cfb80763f0cfb", + "40203153a8cd4e919398458c75ff3b3b", + "c96b4c657fc3494aa44d390e6b7cf341", + "27fac4bc47664f29864614869cfbba23", + "1bf3c6e347524a028280e00496d89b13", + "e0bf0c14330c495ca2225c94e0579959", + "52fb2ed81bac45dc91a7b3fa601b184e", + "9e96a0821aba41969136a1f8b3ad0414", + "31c0ae2670eb4f8d9f9c76b79cdeedd4", + "f83b54262691436283c4f51a559e8011", + "0833ef4e585546b7b6597fd2bbc75513", + "afb4793df43442bbb19ed1a4e867714d", + "e2c782671e614d21895f6b6971f9a9b2", + "e280ff82acd64ff6b8587b299be5cc1b", + "2dc0bb364df54911a89df8cc0d90a13c", + "72e33e0b2d2947ca9be4e6807219d2ca", + "b9d6e9adf15a4571a8390b2531984285", + "84742d657466472da5fe8dbc26c983e1", + "87e52c0c7f0b459386f2e1912c6f350c", + "576b0fe56090423fb587e310f5c5a3ba", + "c0615d02f1144ddca769e95020100d29", + "99aa8378d7514ea8bbabc653ef69d063", + "953f966958c944318313c1e0dcbe40c2", + "10e96ef33cfe4c2eabbb7fc701fc871a", + "b4c18d9482654ca58c27ce4f13f76a2a", + "ba6d0785fec04368a5564bb677a4cceb", + "1017a319c45e4de9a616ea96530e68da", + "8bdb6c862bec486fb7b0c862cdcba5b7", + "23d5829c4db743cbbfaa83a38ae0dfe6", + "7c3a4188af4749bc8f96f2481f247f13", + "8cb417fe916c465aa5c43683a7c3e7a2", + "656d9086c279498fa0af88b28c192b54", + "0bb3addbdcec4a7ea4d58fa7ec41e780", + "a86198eac03f496f86590ce8ae1aa7c6", + "be8c57434a0041eeb2b5a2922d16ff0c", + "59dda051061d4a098f673b26a41358f5", + "5c911f8db7be466c97d8e7b3608fc48f", + "c4b43d7d6ab44b619bde91e0661565de", + "79ee5177277048fb808099d0aea19d78", + "d6719a3e541041baaf547b96a3b45b9c", + "7e410232374e416a9c8856dbd594620d", + 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"118cf2048a9d4a12836781fc8f87f96a", + "6a0bdf0e52634c0298ddf453a5494f2c", + "35c466359b924d93b1342a4f907e4dcd", + "bf620acb23564cb998951c3b0f080720", + "31a6e952d8ba4431b1d65dcab8ce73b3", + "86fa8183992242e58c4b349c96c94b2c", + "d0d7ab0c19f540dc9b3dcd06ac2afef8", + "68fe2f73523b47ea8f07043aab7fc75b", + "ba2682c5c1244619a6a525949fded771", + "602101eae56b42c9bdf0cba4c08b5db4", + "c805b4aa5b424daaad24aad6b9d9c86e", + "89812f3a3d194891a3f9ba2e8e5726c5", + "a1b0412c1d834f229d2a148f66556ed2", + "e4faea58abb84dd78cd3deee43621c7e", + "10d269096ef04577935d7766f7d9efac", + "8e02478b4aff41ecbebe6b0f4e695972", + "28cd142e67354eecae98beeee27e364f", + "85d7658f7d6f4d23abf3e96e61f87d20", + "aff85219c837445ab85a463d41200bf6", + "9413e5b1990f4b11aac96b4354e5edc0", + "7312bfa893064fefb8a8dd06528bbd3c", + "654b16ef5d4b46fd82851d57fd97b693", + "631dcad01f974289b6a37ba518e84dba", + "84b4e8f8394b4bfd94a58e2d6a8498e2", + "9bf8190c7ca847999c83f14233b8ac61", + "9f9fc6ea79bd4afaa8cdca949d543705", + "3de3e28ae52242ce912658409789b4cf", + "160673a274bd4915b5b7fe189fbb7363", + "7a2ff32993b24ae288c6735d1a19caf6", + "57000fc336f147328386c9496600b9f3", + "b204b85e31594d5a8730f4ff2f476f4b", + "fd1b27d6ae964c03993367082e811907", + "1f1493955f6340669236b99bbd1205e6", + "b9a90750475e49f88baddfed09c2448a", + "855db79b3e464f6dab9d8e20a2ba300e" + ] + }, + "id": "R_O04fKxMMT-", + "outputId": "2546755e-ec1f-486d-c294-4d260e3be0e0" + }, + "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/" + }, + "id": "kD-GJtbrdd5t", + "outputId": "80e7eec2-86b8-4b91-d2fe-2bed361e17df" + }, + "outputs": [], + "source": [ + "fine_tuned_model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UObo1-RqaNnT" + }, + "source": [ + "# THE MOMENT OF TRUTH!\n", + "\n", + "## Use the model in inference mode\n", + "\n", + "Remember, GPT-4o had an average error of \\$76. \n", + "Llama 3.1 base model had an average error of \\$395.72. \n", + "This human had an error of \\$127. \n", + "\n", + "## Caveat\n", + "\n", + "Keep in mind that prices of goods vary considerably; the model can't predict things like sale prices that it doesn't have any information about." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Qst1LhBVAB04" + }, + "outputs": [], + "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/" + }, + "id": "jXFBW_5UeEcp", + "outputId": "feb4a86f-4a6b-49d2-b97d-7df91e714e9c" + }, + "outputs": [], + "source": [ + "extract_price(\"Price is $a fabulous 899.99 or so\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Oj_PzpdFAIMk" + }, + "outputs": [], + "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": { + "id": "Je5dR8QEAI1d" + }, + "outputs": [], + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "lQk7jNlm1oV9" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "30lzJXBH7BcK" + }, + "outputs": [], + "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()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "W_KcLvyt6kbb", + "outputId": "bc6a2269-f02a-4007-8a60-7d9054634324" + }, + "outputs": [], + "source": [ + "Tester.test(improved_model_predict, test)" + ] + }, + { + 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