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Dmitry Kisselev
2025-10-26 00:34:38 -07:00
parent 61d8281cf7
commit 907748e560
2 changed files with 144 additions and 7949 deletions
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Week_7_Exercise.ipynb
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Week_7_Excersise_fine_tuned_model.ipynb → week7/community_contributions/dkisselev-zz/Week_7_Excersise_fine_tuned_model.ipynb
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@@ -1,27 +1,10 @@
{ {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [ "cells": [
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": { "metadata": {
"id": "view-in-github", "colab_type": "text",
"colab_type": "text" "id": "view-in-github"
}, },
"source": [ "source": [
"<a href=\"https://colab.research.google.com/github/dkisselev-zz/llm_engineering/blob/wk7/Week_7_Excersise_fine_tuned_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" "<a href=\"https://colab.research.google.com/github/dkisselev-zz/llm_engineering/blob/wk7/Week_7_Excersise_fine_tuned_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
@@ -29,65 +12,67 @@
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"id": "GHsssBgWM_l0"
},
"source": [ "source": [
"# Predict Product Prices\n", "# Predict Product Prices\n",
"\n", "\n",
"Model evaluation and inference tuning\n", "Model evaluation and inference tuning\n",
"\n" "\n"
], ]
"metadata": {
"id": "GHsssBgWM_l0"
}
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [
"## Libraries and configuration"
],
"metadata": { "metadata": {
"id": "HnwMdAP3IHad" "id": "HnwMdAP3IHad"
} },
"source": [
"## Libraries and configuration"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "execution_count": null,
"!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"
],
"metadata": { "metadata": {
"id": "MDyR63OTNUJ6" "id": "MDyR63OTNUJ6"
}, },
"execution_count": null, "outputs": [],
"outputs": [] "source": [
"!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", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-yikV8pRBer9"
},
"outputs": [],
"source": [ "source": [
"import os\n", "import os\n",
"import re\n", "import re\n",
"import math\n", "import math\n",
"import numpy as np\n", "import numpy as np\n",
"from tqdm import tqdm\n",
"from google.colab import userdata\n", "from google.colab import userdata\n",
"from huggingface_hub import login\n", "from huggingface_hub import login\n",
"import wandb\n", "import wandb\n",
"import torch\n", "import torch\n",
"import torch.nn.functional as F\n", "import torch.nn.functional as F\n",
"import transformers\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, set_seed\n",
"from datasets import load_dataset, Dataset, DatasetDict\n", "from datasets import load_dataset\n",
"from datetime import datetime\n",
"from peft import PeftModel\n", "from peft import PeftModel\n",
"import matplotlib.pyplot as plt" "import matplotlib.pyplot as plt"
], ]
"metadata": {
"id": "-yikV8pRBer9"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "uuTX-xonNeOK"
},
"outputs": [],
"source": [ "source": [
"# Models\n", "# Models\n",
"\n", "\n",
@@ -140,61 +125,61 @@
"BLUE = \"\\033[94m\"\n", "BLUE = \"\\033[94m\"\n",
"RESET = \"\\033[0m\"\n", "RESET = \"\\033[0m\"\n",
"COLOR_MAP = {\"red\":RED, \"orange\": BLUE, \"green\": GREEN}" "COLOR_MAP = {\"red\":RED, \"orange\": BLUE, \"green\": GREEN}"
], ]
"metadata": {
"id": "uuTX-xonNeOK"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {
"id": "8JArT3QAQAjx"
},
"source": [ "source": [
"### Load Data\n", "### Load Data\n",
"\n", "\n",
"Data is loaded from Huggin Face\n" "Data is loaded from Huggin Face\n"
], ]
"metadata": {
"id": "8JArT3QAQAjx"
}
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "WyFPZeMcM88v"
},
"outputs": [],
"source": [ "source": [
"# Log in to HuggingFace\n", "# Log in to HuggingFace\n",
"hf_token = userdata.get('HF_TOKEN')\n", "hf_token = userdata.get('HF_TOKEN')\n",
"login(hf_token)" "login(hf_token)"
], ]
"metadata": {
"id": "WyFPZeMcM88v"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "cvXVoJH8LS6u"
},
"outputs": [],
"source": [ "source": [
"dataset = load_dataset(DATASET_NAME)\n", "dataset = load_dataset(DATASET_NAME)\n",
"train = dataset['train']\n", "train = dataset['train']\n",
"test = dataset['test']" "test = dataset['test']"
], ]
"metadata": {
"id": "cvXVoJH8LS6u"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [
"## Load Tokenizer and Model"
],
"metadata": { "metadata": {
"id": "qJWQ0a3wZ0Bw" "id": "qJWQ0a3wZ0Bw"
} },
"source": [
"## Load Tokenizer and Model"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lAUAAcEC6ido"
},
"outputs": [],
"source": [ "source": [
"# 4 or 8 but quantization\n", "# 4 or 8 but quantization\n",
"if QUANT_4_BIT:\n", "if QUANT_4_BIT:\n",
@@ -208,29 +193,29 @@
" quant_config = BitsAndBytesConfig(\n", " quant_config = BitsAndBytesConfig(\n",
" load_in_8bit=True\n", " load_in_8bit=True\n",
" )" " )"
], ]
"metadata": {
"id": "lAUAAcEC6ido"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "OQy4pCk-dutf"
},
"outputs": [],
"source": [ "source": [
"# Load model from w&b\n", "# Load model from w&b\n",
"if ARTIFCAT_LOCATTION==\"WB\":\n", "if ARTIFCAT_LOCATTION==\"WB\":\n",
" artifact = wandb.Api().artifact(WANDB_ARTIFACT_PATH, type='model')\n", " artifact = wandb.Api().artifact(WANDB_ARTIFACT_PATH, type='model')\n",
" artifact_dir = artifact.download() # Downloads to a local cache dir" " artifact_dir = artifact.download() # Downloads to a local cache dir"
], ]
"metadata": {
"id": "OQy4pCk-dutf"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "R_O04fKxMMT-"
},
"outputs": [],
"source": [ "source": [
"# Load the Tokenizer and the Model\n", "# Load the Tokenizer and the Model\n",
"\n", "\n",
@@ -256,24 +241,24 @@
" fine_tuned_model = PeftModel.from_pretrained(base_model, artifact_dir)\n", " fine_tuned_model = PeftModel.from_pretrained(base_model, artifact_dir)\n",
"\n", "\n",
"print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")" "print(f\"Memory footprint: {fine_tuned_model.get_memory_footprint() / 1e6:.1f} MB\")"
], ]
"metadata": {
"id": "R_O04fKxMMT-"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [
"## Hyperparameter helpers"
],
"metadata": { "metadata": {
"id": "UObo1-RqaNnT" "id": "UObo1-RqaNnT"
} },
"source": [
"## Hyperparameter helpers"
]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "n4u27kbwlekE"
},
"outputs": [],
"source": [ "source": [
"def calculate_weighted_price(prices, probabilities):\n", "def calculate_weighted_price(prices, probabilities):\n",
" \"\"\"\n", " \"\"\"\n",
@@ -303,15 +288,15 @@
" weighted_price = np.average(prices_array, weights=probs_array)\n", " weighted_price = np.average(prices_array, weights=probs_array)\n",
"\n", "\n",
" return weighted_price" " return weighted_price"
], ]
"metadata": {
"id": "n4u27kbwlekE"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ROjIbGuH0FWS"
},
"outputs": [],
"source": [ "source": [
"def get_top_k_predictions(prompt, device=\"cuda\"):\n", "def get_top_k_predictions(prompt, device=\"cuda\"):\n",
" \"\"\"\n", " \"\"\"\n",
@@ -351,15 +336,15 @@
" return [], []\n", " return [], []\n",
"\n", "\n",
" return prices, probabilities" " return prices, probabilities"
], ]
"metadata": {
"id": "ROjIbGuH0FWS"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tnmTAiEG32xK"
},
"outputs": [],
"source": [ "source": [
"def make_prompt(text):\n", "def make_prompt(text):\n",
" if ARTIFCAT_LOCATTION==\"HF\":\n", " if ARTIFCAT_LOCATTION==\"HF\":\n",
@@ -373,15 +358,15 @@
" # prompt = p_array[0] + \"\\n\\n\\n\" + p_title + \"\\n\\n\" + p_descr + \"\\n\\n\" + p_price\n", " # prompt = p_array[0] + \"\\n\\n\\n\" + p_title + \"\\n\\n\" + p_descr + \"\\n\\n\" + p_price\n",
" # return text\n", " # return text\n",
" return prompt" " return prompt"
], ]
"metadata": {
"id": "tnmTAiEG32xK"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "VNAEw5Eg4ABk"
},
"outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"\n", "\n",
@@ -457,15 +442,15 @@
" @classmethod\n", " @classmethod\n",
" def test(cls, function, data):\n", " def test(cls, function, data):\n",
" cls(function, data).run()" " cls(function, data).run()"
], ]
"metadata": {
"id": "VNAEw5Eg4ABk"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "dbWS1DPV4TPQ"
},
"outputs": [],
"source": [ "source": [
"class Search_K:\n", "class Search_K:\n",
" \"\"\"\n", " \"\"\"\n",
@@ -710,28 +695,28 @@
" @classmethod\n", " @classmethod\n",
" def test(cls, function, data):\n", " def test(cls, function, data):\n",
" cls(function, data).run()" " cls(function, data).run()"
], ]
"metadata": {
"id": "dbWS1DPV4TPQ"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Vtt13OuVE-t7"
},
"outputs": [],
"source": [ "source": [
"# Search best K\n", "# Search best K\n",
"search_k = Search_K(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n", "search_k = Search_K(get_top_k_predictions, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n",
"best_k = search_k.run()" "best_k = search_k.run()"
], ]
"metadata": {
"id": "Vtt13OuVE-t7"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "tuwYu1NYljIv"
},
"outputs": [],
"source": [ "source": [
"top_K = best_k\n", "top_K = best_k\n",
"\n", "\n",
@@ -785,39 +770,51 @@
" final_price = np.average(prices_np, weights=probs_np)\n", " final_price = np.average(prices_np, weights=probs_np)\n",
"\n", "\n",
" return float(final_price) # Return as a standard python float" " return float(final_price) # Return as a standard python float"
], ]
"metadata": {
"id": "tuwYu1NYljIv"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3SxpLBJH70E-"
},
"outputs": [],
"source": [ "source": [
"prompt=make_prompt(test[80]['text'])\n", "prompt=make_prompt(test[80]['text'])\n",
"print(prompt)\n", "print(prompt)\n",
"\n", "\n",
"improved_model_predict(prompt)" "improved_model_predict(prompt)"
], ]
"metadata": {
"id": "3SxpLBJH70E-"
},
"execution_count": null,
"outputs": []
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null,
"metadata": {
"id": "W_KcLvyt6kbb"
},
"outputs": [],
"source": [ "source": [
"# Run Estimate vs Ground Truth\n", "# Run Estimate vs Ground Truth\n",
"tester = Tester(improved_model_predict, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n", "tester = Tester(improved_model_predict, test, title=f\"{MODEL_ARTIFACT_NAME}:{REVISION_TAG}\" if ARTIFCAT_LOCATTION==\"WB\" else None)\n",
"tester.run()" "tester.run()"
], ]
"metadata": {
"id": "W_KcLvyt6kbb"
},
"execution_count": null,
"outputs": []
} }
] ],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"include_colab_link": true,
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} }