diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..47efcc7
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,4 @@
+# API Keys and Other Secrets
+OPENAI_API_KEY="something"
+MISTRAL_API_KEY="anoher-secret"
+HUGGINGFACE_TOKEN="and-anotherg"
diff --git a/.gitignore b/.gitignore
index 5eff2a9..691c57a 100644
--- a/.gitignore
+++ b/.gitignore
@@ -13,7 +13,7 @@ slides/
# Distribution / packaging
.Python
-env/
+.venv/
build/
develop-eggs/
dist/
diff --git a/data/docs/who-docs/Cholera-Report.pdf b/data/docs/who-docs/Cholera-Report.pdf
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index 0000000..7ecf47d
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diff --git a/data/docs/who-docs/Dengue-Global-situation.pdf b/data/docs/who-docs/Dengue-Global-situation.pdf
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index 0000000..defa68c
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new file mode 100644
index 0000000..502796a
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diff --git a/data/docs/who-docs/MidEast-COVID.pdf b/data/docs/who-docs/MidEast-COVID.pdf
new file mode 100644
index 0000000..0a76ac4
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diff --git a/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb b/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb
deleted file mode 100644
index 20c458b..0000000
--- a/notebooks/malawi-nov-24/2-document-classification-with-sklearn.ipynb
+++ /dev/null
@@ -1,575 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Building a Document Classification System\n",
- "The NumPy (Numerical Python) library used for working iwith arrays, and the Scikit-learn library is a python library built on NumPy, SciPy and matplotlib for data analytics and machine learning. The NLTK (Natural Language Toolkit) provides access to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, wrappers for industrial-strength NLP libraries."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Ensuring that you have the necessary libraries\n",
- "# !pip install nltk\n",
- "# !pip install numpy\n",
- "# !pip install scikit-learn"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import numpy as np\n",
- "import nltk\n",
- "from nltk.corpus import reuters\n",
- "from sklearn.feature_extraction.text import TfidfVectorizer\n",
- "from sklearn.model_selection import train_test_split\n",
- "from sklearn.svm import LinearSVC\n",
- "from sklearn.metrics import accuracy_score, classification_report\n",
- "\n",
- "from sklearn.feature_extraction.text import CountVectorizer\n",
- "from sklearn.naive_bayes import MultinomialNB"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 1. Load your data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "The Reuters-21578 dataset is one of the most widely used data collections for text categorization research. It is a collection of documents with news articles and the original corpus has 10,369 documents and a vocabulary of 29,930 word and has labeled categories such as \"earnings\", \"acquisitions\".. etc. You can read metadata about the dataset on [Hugging Face](https://huggingface.co/datasets/ucirvine/reuters21578)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package reuters to\n",
- "[nltk_data] /Users/dunstanmatekenya/nltk_data...\n",
- "[nltk_data] Package reuters is already up-to-date!\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# download the dataset\n",
- "nltk.download('reuters')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Load the Reuters-21578 dataset\n",
- "documents = reuters.fileids()\n",
- "train_docs = list(filter(lambda doc: doc.startswith(\"train\"), documents))\n",
- "test_docs = list(filter(lambda doc: doc.startswith(\"test\"), documents))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 2. Prepare your data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Prepare the data by extracting the raw text and category labels for both the training and testing documents. Assumption is that each document has only one category label, so we take only the first category label for each document."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Prepare the data\n",
- "train_data = [reuters.raw(doc_id) for doc_id in train_docs]\n",
- "train_labels = [reuters.categories(doc_id)[0] for doc_id in train_docs]\n",
- "test_data = [reuters.raw(doc_id) for doc_id in test_docs]\n",
- "test_labels = [reuters.categories(doc_id)[0] for doc_id in test_docs]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Question-How many different classes are in the training data?"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Explore some of the training examples"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Article content: COMPUTER TERMINAL SYSTEMS <CPML> COMPLETES SALE\n",
- " Computer Terminal Systems Inc said\n",
- " it has completed the sale of 200,000 shares of its common\n",
- " stock, and warrants to acquire an additional one mln shares, to\n",
- " <Sedio N.V.> of Lugano, Switzerland for 50,000 dlrs.\n",
- " The company said the warrants are exercisable for five\n",
- " years at a purchase price of .125 dlrs per share.\n",
- " Computer Terminal said Sedio also has the right to buy\n",
- " additional shares and increase its total holdings up to 40 pct\n",
- " of the Computer Terminal's outstanding common stock under\n",
- " certain circumstances involving change of control at the\n",
- " company.\n",
- " The company said if the conditions occur the warrants would\n",
- " be exercisable at a price equal to 75 pct of its common stock's\n",
- " market price at the time, not to exceed 1.50 dlrs per share.\n",
- " Computer Terminal also said it sold the technolgy rights to\n",
- " its Dot Matrix impact technology, including any future\n",
- " improvements, to <Woodco Inc> of Houston, Tex. for 200,000\n",
- " dlrs. But, it said it would continue to be the exclusive\n",
- " worldwide licensee of the technology for Woodco.\n",
- " The company said the moves were part of its reorganization\n",
- " plan and would help pay current operation costs and ensure\n",
- " product delivery.\n",
- " Computer Terminal makes computer generated labels, forms,\n",
- " tags and ticket printers and terminals.\n",
- " \n",
- "\n",
- " n\\, Label: acq\n"
- ]
- }
- ],
- "source": [
- "print(\"Article content: {} n\\, Label: {}\".format(train_data[1], train_labels[1]))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 3. Vectorizing the text data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "- Vectorize the text data using the TfidVectorizer from scikit-learn. TF-IDF is an abbreviation for Term Frequency Inverse Document Frequency. This is very common algorithm to transform text into a meaningful representation of numbers which is used to fit machine algorithm for prediction. \n",
- "- Its worth noting that nowadays, this vectorization approach is not commonly used. We will cover **word embeddings** tomorrow which is a better approach to represent words as numbers because **vector embeddings** can capture semantic meanings better.\n",
- "\n",
- "For the sklearn TF-IDF vectorizer, you can learn more about it [here](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Vectorize the text data\n",
- "vectorizer = TfidfVectorizer(stop_words=\"english\", max_features=1000)\n",
- "X_train = vectorizer.fit_transform(train_data)\n",
- "X_test = vectorizer.transform(test_data)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Question: What role are the ```stop words``` playing in the code above? You might have learned this from Prof. Mohamad Ali already."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### 4. Training a Linear Support Vector Machine (LinearSVC) classifier using the vectorized training data and corresponding label"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "LinearSVC() In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. Dunstan Matekenya \n",
"\n",
"**Affiliation:** DECAT, The World Bank Group \n",
"\n",
- "**Date:** May 30, 2024 \n",
+ "**Date:** November, 2024 \n",
"\n",
"\n",
"## What you will learn \n",
@@ -65,15 +56,150 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 7,
"id": "b9a13ee9-f3d9-4141-a1a2-929cdc1b5113",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "True"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"import os\n",
- "from pathlib import Path"
+ "from pathlib import Path\n",
+ "\n",
+ "\n",
+ "# =======================\n",
+ "# ENVIRONMENT HANDLING\n",
+ "# =======================\n",
+ "from dotenv import load_dotenv\n",
+ "load_dotenv()"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "51203f5f",
+ "metadata": {},
+ "source": [
+ "# Test OpenAI and HuggingFace"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "28693074",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "44942.48999999999"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "(1498083*0.15) - (1498083*0.12)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4ee60ee5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0.75"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "9/12"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "8d810eca",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "44983.889999999985"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "224919.44999999998 - 179935.56"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "9b2abf53",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from langchain_openai import ChatOpenAI\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "98bc75bc",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Result : Malawi is located in southeastern Africa, bordered by Tanzania to the north and northeast, Zambia to the west, and Mozambique to the east, south and southwest.\n"
+ ]
+ }
+ ],
+ "source": [
+ "llm = ChatOpenAI()\n",
+ "res = llm.invoke(\"Where is Malawi?\").content\n",
+ "print(f\"Result : {res}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "049080c6",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Result : I'm just a computer program, so I don't have feelings or emotions. How can I assist you today?\n"
+ ]
+ }
+ ],
+ "source": []
+ },
{
"cell_type": "markdown",
"id": "1e1dad1b-4014-48e9-b911-2095c9864a84",
@@ -521,27 +647,6 @@
"print(output)"
]
},
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "e9bda8e1-dce1-49c1-b308-82063fa53e6a",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "3544"
- ]
- },
- "execution_count": 1,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "2*1772"
- ]
- },
{
"cell_type": "markdown",
"id": "7381c4a5-5a33-405c-b82a-baacfebe6e56",
@@ -559,7 +664,7 @@
"id": "f4edd5e5-44fa-49b0-b150-64a580da8f66",
"metadata": {},
"source": [
- "### . Prompt templates\n",
+ "### Prompt templates\n",
"Prompt templates are used for creating prompts in a more modular way, so they can be reused and built on. Chains act as the glue in LangChain; bringing the other components together into workflows that pass inputs and outputs between the different components\n",
"- They are recipes for generating prompts\n",
"- Flexible and modular\n",
@@ -659,14 +764,6 @@
"llm(full_prompt)"
]
},
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "e11ea305-a3c9-439f-b135-236e26c39ac1",
- "metadata": {},
- "outputs": [],
- "source": []
- },
{
"cell_type": "markdown",
"id": "c3e8c433-8672-4ae8-9579-66d5201bc657",
@@ -1876,9 +1973,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python3.12-audio",
+ "display_name": ".venv",
"language": "python",
- "name": "audio"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
diff --git a/notebooks/malawi-nov-24/LLM_Teaching_Notebook_with_Conclusion.ipynb b/notebooks/malawi-nov-24/LLM_Teaching_Notebook_with_Conclusion.ipynb
index 3b556e5..21253b9 100644
--- a/notebooks/malawi-nov-24/LLM_Teaching_Notebook_with_Conclusion.ipynb
+++ b/notebooks/malawi-nov-24/LLM_Teaching_Notebook_with_Conclusion.ipynb
@@ -1,3915 +1,3929 @@
{
- "cells": [
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L-hCapPatC1R"
+ },
+ "source": [
+ "# Introduction to LLMs\n",
+ "Large Language Models (LLMs) are AI models that can generate human-like text based on given prompts. They work by:\n",
+ "\n",
+ "\n",
+ "* Understanding and tokenizing the input text.\n",
+ "* Predicting the most likely sequence of words (tokens) to follow.\n",
+ "* Using probabilities to generate coherent and contextually relevant outputs.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VVoiHfQdt0rp"
+ },
+ "source": [
+ "##Setting Up the Environment\n",
+ "Below is the code to set up the environment using the Hugging Face transformers library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "HvKJfKCws8pQ",
+ "outputId": "133950ce-ede3-4435-c477-17da1f1ce4d4"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "L-hCapPatC1R"
- },
- "source": [
- "# Introduction to LLMs\n",
- "Large Language Models (LLMs) are AI models that can generate human-like text based on given prompts. They work by:\n",
- "\n",
- "\n",
- "* Understanding and tokenizing the input text.\n",
- "* Predicting the most likely sequence of words (tokens) to follow.\n",
- "* Using probabilities to generate coherent and contextually relevant outputs.\n",
- "\n"
- ]
+ "ename": "ModuleNotFoundError",
+ "evalue": "No module named 'torch'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Importing necessary libraries\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoTokenizer, AutoModelForCausalLM, pipeline, set_seed\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyplot\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mplt\u001b[39;00m\n",
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'torch'"
+ ]
+ }
+ ],
+ "source": [
+ "# Importing necessary libraries\n",
+ "import torch\n",
+ "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, set_seed\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Checking if GPU is available\n",
+ "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+ "print(f\"Using device: {device}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eUs4IPMTuBof"
+ },
+ "source": [
+ "# Tokenization\n",
+ "In this section, we'll explain tokenization and demonstrate it using a sample prompt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 397,
+ "referenced_widgets": [
+ "ed52f970e5ae4c11863e8e46f9ae828f",
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+ "2ea9aa390af84712893e431108ba80d2",
+ "e5c16313f7c94eb7a390a99f5d09ef77",
+ "9e874cda300f4056807b378d386789cb",
+ "85baaaffafea4de693f7637e5e1f09c8",
+ "6377db896bed44299ffdf88704c925ff",
+ "c8b3fb7df3fa4d0f8d57efe9721d3a67",
+ "6e3b8ae4aa044e8e878c3f3ea1e76887",
+ "e3d9044e0c0f4cf8b3783d617b0e4649"
+ ]
},
+ "id": "Ea5dAG_KuDyl",
+ "outputId": "4a40cd1d-2e02-4999-aa04-398ed4f1cf6e"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "VVoiHfQdt0rp"
- },
- "source": [
- "##Setting Up the Environment\n",
- "Below is the code to set up the environment using the Hugging Face transformers library:"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
+ "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+ "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+ "You will be able to reuse this secret in all of your notebooks.\n",
+ "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+ " warnings.warn(\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "HvKJfKCws8pQ",
- "outputId": "133950ce-ede3-4435-c477-17da1f1ce4d4"
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ed52f970e5ae4c11863e8e46f9ae828f",
+ "version_major": 2,
+ "version_minor": 0
},
- "outputs": [
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Using device: cpu\n"
- ]
- }
- ],
- "source": [
- "# Importing necessary libraries\n",
- "import torch\n",
- "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, set_seed\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# Checking if GPU is available\n",
- "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
- "print(f\"Using device: {device}\")\n"
+ "text/plain": [
+ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00"
- ],
- "image/png": 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smCHJ6NKlS6brbz9fUVFRRrt27YyAgACjQIECRkBAgNGtWzfj8OHDVvsdO3bMCA0NNdzc3Ax/f3/jnXfeMSIjIzPUl93zeLcpwI8fP37H1y19Ku8///zT6NChg1GoUCHD19fXeO6554zTp09n+lqMHz/eeOSRRwwXFxer58hs6vFjx44ZnTt3NgoVKmS4u7sbTzzxhLF69WqrbdJfn2+++caqPb12y+MFAGcyGQZ3SQIAAABAOsa/AQAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALOT5L5NNS0vT6dOn5e3tLZPJ5OxyAAAAADiJYRi6fPmyAgIC7vhF13k+JJ0+fVqBgYHOLgMAAABALhEfH69SpUpluT7PhyRvb29Jt06Ej4+Pk6sBAAAA4CxJSUkKDAw0Z4Ss5PmQlH6JnY+PDyEJAAAAwF1vw2HiBgAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwkM/ZBQB4sJUd8YOzS8jVTkxu7ewSYAPez3fHexrAw4CRJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAtODUlbtmxRmzZtFBAQIJPJpBUrVpjX3bhxQ2+//bZq1qypggULKiAgQN27d9fp06edVzAAAACAPM+pISk5OVmPPfaYZs2alWHd1atXtWfPHr333nvas2ePli9frri4OLVt29YJlQIAAAB4WORzZuctW7ZUy5YtM13n6+uryMhIq7ZPP/1UTzzxhE6dOqXSpUs7okQAAAAADxmnhiRbJSYmymQyqVChQlluk5KSopSUFPNyUlKSAyoDAAAAkFc8MBM3XLt2TW+//ba6desmHx+fLLebNGmSfH19zY/AwEAHVgkAAADgQfdAhKQbN26oS5cuMgxDs2fPvuO2I0eOVGJiovkRHx/voCoBAAAA5AW5/nK79IB08uRJbdy48Y6jSJLk5uYmNzc3B1UHAAAAIK/J1SEpPSAdOXJE0dHRKlq0qLNLAgAAAJDHOTUkXblyRUePHjUvHz9+XLGxsSpSpIhKliypzp07a8+ePVq9erVSU1P1999/S5KKFCmiAgUKOKtsAAAAAHmYU0PSrl271KxZM/Py4MGDJUk9evTQ2LFjtWrVKklS7dq1rfaLjo5W06ZNHVUmAAAAgIeIU0NS06ZNZRhGluvvtA4AAAAAcsIDMbsdAAAAADgKIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAAAAALDg1JG3ZskVt2rRRQECATCaTVqxYYbXeMAyNHj1aJUuWlIeHh0JDQ3XkyBHnFAsAAADgoeDUkJScnKzHHntMs2bNynT91KlT9fHHH+uzzz7T9u3bVbBgQTVv3lzXrl1zcKUAAAAAHhb5nNl5y5Yt1bJly0zXGYahGTNmaNSoUWrXrp0kacGCBfL399eKFSv0/PPPO7JUAAAAAA+JXHtP0vHjx/X3338rNDTU3Obr66sGDRro559/znK/lJQUJSUlWT0AAAAAILvuaSQpKipKUVFRSkhIUFpamtW6L7/80i6F/f3335Ikf39/q3Z/f3/zusxMmjRJ4eHhdqkBAAAAwMPH5pGk8PBwPfPMM4qKitL58+d18eJFq4ezjRw5UomJieZHfHy8s0sCAAAA8ACxeSTps88+U0REhF566aWcqMesRIkSkqSzZ8+qZMmS5vazZ8+qdu3aWe7n5uYmNze3HK0NAAAAQN5l80jS9evXFRQUlBO1WClXrpxKlCihqKgoc1tSUpK2b9+u//znPznePwAAAICHk80hqU+fPlq0aJFdOr9y5YpiY2MVGxsr6dZkDbGxsTp16pRMJpMGDhyo999/X6tWrdKvv/6q7t27KyAgQO3bt7dL/wAAAABwO5svt7t27Zrmzp2rDRs2qFatWsqfP7/V+unTp2f7uXbt2qVmzZqZlwcPHixJ6tGjhyIiIjR8+HAlJyerb9++unTpkho2bKi1a9fK3d3d1rIBAAAAIFtsDkn79+833xP022+/Wa0zmUw2PVfTpk1lGEaW600mk8aNG6dx48bZWiYAAAAA3BObQ1J0dHRO1AEAAAAAucI9f5ns0aNHtW7dOv3777+SdMcRIQAAAAB4UNgckv755x+FhISoUqVKatWqlc6cOSNJevnllzVkyBC7FwgAAAAAjmRzSBo0aJDy58+vU6dOydPT09zetWtXrV271q7FAQAAAICj2XxP0vr167Vu3TqVKlXKqr1ixYo6efKk3QoDAAAAAGeweSQpOTnZagQp3YULF+Tm5maXogAAAADAWWwOSY0aNdKCBQvMyyaTSWlpaZo6darVdx4BAAAAwIPI5svtpk6dqpCQEO3atUvXr1/X8OHDdeDAAV24cEExMTE5USMAAAAAOIzNI0k1atTQ4cOH1bBhQ7Vr107Jycnq2LGj9u7dq/Lly+dEjQAAAADgMPf0ZbLNmjXTu+++m2HdrFmz1K9fP7sUBgAAAADOYPNIUseOHbV79+4M7TNnztTIkSPtUhQAAAAAOIvNIemDDz5Qy5Yt9fvvv5vbpk2bptGjR+uHH36wa3EAAAAA4Gg2X27Xp08fXbhwQaGhofrpp5+0dOlSTZw4UT/++KOCg4NzokYAAAAAcBibQ5IkDR8+XP/884/q1aun1NRUrVu3Tk8++aS9awMAAAAAh8tWSPr4448ztD3yyCPy9PRU48aNtWPHDu3YsUOS1L9/f/tWCAAAAAAOlK2Q9NFHH2Xa7urqqpiYGPP3I5lMJkISAAAAgAdatkLS8ePHc7oOAAAAAMgVbJ7dzpJhGDIMw161AAAAAIDT3VNIWrBggWrWrCkPDw95eHioVq1a+uqrr+xdGwAAAAA4nM2z202fPl3vvfee3nzzTfOU3z/99JNee+01nT9/XoMGDbJ7kQAAAADgKDaHpE8++USzZ89W9+7dzW1t27ZV9erVNXbsWEISAAAAgAeazZfbnTlzRkFBQRnag4KCdObMGbsUBQAAAADOYnNIqlChgpYtW5ahfenSpapYsaJdigIAAAAAZ8n25XZPPfWUli9frvDwcHXt2lVbtmwx35MUExOjqKioTMMTAAAAADxIsj2StGnTJl2/fl2dOnXS9u3bVaxYMa1YsUIrVqxQsWLFtGPHDnXo0CEnawUAAACAHGfzxA2SVLduXS1cuNDetQAAAACA09kUkg4ePKi///77jtvUqlXrvgoCAAAAAGeyKSSFhITIMIws15tMJqWmpt53UQAAAADgLDaFpO3bt8vPzy+nagEAAAAAp7MpJJUuXVrFixfPqVoAAAAAwOls/p4kAAAAAMjLsh2SmjRpogIFCuRkLQAAAADgdNm+3C46Ojon6wAAAACAXIHL7QAAAADAQq4OSampqXrvvfdUrlw5eXh4qHz58ho/fvwdpyEHAAAAgPth0+x2jjZlyhTNnj1b8+fPV/Xq1bVr1y716tVLvr6+6t+/v7PLAwAAAJAH5eqQtG3bNrVr106tW7eWJJUtW1aLFy/Wjh07nFwZAAAAgLzK5pCUmpqqiIgIRUVFKSEhQWlpaVbrN27caLfigoKCNHfuXB0+fFiVKlXSvn379NNPP2n69OlZ7pOSkqKUlBTzclJSkt3qAQAAAJD32RySBgwYoIiICLVu3Vo1atSQyWTKibokSSNGjFBSUpKqVKkiV1dXpaamasKECQoLC8tyn0mTJik8PDzHagIAAACQt9kckpYsWaJly5apVatWOVGPlWXLlunrr7/WokWLVL16dcXGxmrgwIEKCAhQjx49Mt1n5MiRGjx4sHk5KSlJgYGBOV4rAAAAgLzB5pBUoEABVahQISdqyWDYsGEaMWKEnn/+eUlSzZo1dfLkSU2aNCnLkOTm5iY3NzeH1AcAAAAg77F5CvAhQ4Zo5syZDpmG++rVq3JxsS7R1dU1w31QAAAAAGAv2RpJ6tixo9Xyxo0btWbNGlWvXl358+e3Wrd8+XK7FdemTRtNmDBBpUuXVvXq1bV3715Nnz5dvXv3tlsfAAAAAGApWyHJ19fXarlDhw45UsztPvnkE7333nt64403lJCQoICAAL366qsaPXq0Q/oHAAAA8PDJVkiaN29eTteRKW9vb82YMUMzZsxwSv8AAAAAHj4235N0/PhxHTlyJEP7kSNHdOLECXvUBAAAAABOY3NI6tmzp7Zt25ahffv27erZs6c9agIAAAAAp7E5JO3du1fBwcEZ2p988knFxsbaoyYAAAAAcBqbQ5LJZNLly5cztCcmJio1NdUuRQEAAACAs9gckho3bqxJkyZZBaLU1FRNmjRJDRs2tGtxAAAAAOBo2ZrdztKUKVPUuHFjVa5cWY0aNZIkbd26VUlJSdq4caPdCwQAAAAAR7J5JKlatWrav3+/unTpooSEBF2+fFndu3fX77//rho1auREjQAAAADgMDaPJElSQECAJk6caO9aAAAAAMDp7ikkSdLVq1d16tQpXb9+3aq9Vq1a910UAAAAADiLzSHp3Llz6tWrl9asWZPpema4AwAAAPAgs/mepIEDB+rSpUvavn27PDw8tHbtWs2fP18VK1bUqlWrcqJGAAAAAHAYm0eSNm7cqJUrV6pevXpycXFRmTJl9PTTT8vHx0eTJk1S69atc6JOAAAAAHAIm0eSkpOTVbx4cUlS4cKFde7cOUlSzZo1tWfPHvtWBwAAAAAOZnNIqly5suLi4iRJjz32mObMmaO//vpLn332mUqWLGn3AgEAAADAkWy+3G7AgAE6c+aMJGnMmDFq0aKFvv76axUoUEARERH2rg8AAAAAHMrmkPTiiy+a/123bl2dPHlSv//+u0qXLq1ixYrZtTgAAAAAcLR7/p6kdJ6enqpTp449agHspuyIH5xdQq53YjKTrAAAAGQm2/ckVatWTRcuXDAvv/HGGzp//rx5OSEhQZ6envatDgAAAAAcLNsh6ffff9fNmzfNywsXLlRSUpJ52TAMXbt2zb7VAQAAAICD2Ty7XTrDMDK0mUym+yoGAAAAAJztnkMSAAAAAORF2Q5JJpMpw0gRI0cAAAAA8ppsz25nGIZCQkKUL9+tXf7991+1adNGBQoUkCSr+5UAAAAA4EGV7ZA0ZswYq+V27dpl2KZTp073XxEAAAAAONE9hyQAAAAAyIuYuAEAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMBCtkJSkSJFdP78eUlS7969dfny5RwtCgAAAACcJVsh6fr160pKSpIkzZ8/X9euXcvRogAAAADAWbI1Bfh//vMftW/fXnXr1pVhGOrfv788PDwy3fbLL7+0a4EAAAAA4EjZCkkLFy7URx99pGPHjslkMikxMZHRJAAAAAB5UrZCkr+/vyZPnixJKleunL766isVLVo0RwsDAAAAAGeweXa748ePOzQg/fXXX3rxxRdVtGhReXh4qGbNmtq1a5fD+gcAAADwcLmnKcA3b96sNm3aqEKFCqpQoYLatm2rrVu32rs2Xbx4UcHBwcqfP7/WrFmjgwcPatq0aSpcuLDd+wIAAAAAKZuX21lauHChevXqpY4dO6p///6SpJiYGIWEhCgiIkIvvPCC3YqbMmWKAgMDNW/ePHNbuXLl7Pb8AAAAAHA7m0eSJkyYoKlTp2rp0qXq37+/+vfvr6VLl2ry5MkaP368XYtbtWqV6tWrp+eee07FixfX448/rs8///yO+6SkpCgpKcnqAQAAAADZZfNI0h9//KE2bdpkaG/btq3eeecduxRl2dfs2bM1ePBgvfPOO9q5c6f69++vAgUKqEePHpnuM2nSJIWHh9u1DgAAgAdR2RE/OLuEXO3E5NbOLgG5lM0jSYGBgYqKisrQvmHDBgUGBtqlqHRpaWmqU6eOJk6cqMcff1x9+/bVK6+8os8++yzLfUaOHKnExETzIz4+3q41AQAAAMjbbB5JGjJkiPr376/Y2FgFBQVJunVPUkREhGbOnGnX4kqWLKlq1apZtVWtWlXfffddlvu4ubnJzc3NrnUAAAAAeHjYHJJef/11lShRQtOmTdOyZcsk3QouS5cuVbt27exaXHBwsOLi4qzaDh8+rDJlyti1HwAAAABIZ3NIkqQOHTqoQ4cO9q4lg0GDBikoKEgTJ05Uly5dtGPHDs2dO1dz587N8b4BAAAAPJzu6XuSHKV+/fr6/vvvtXjxYtWoUUPjx4/XjBkzFBYW5uzSAAAAAORR9zSS5EjPPvusnn32WWeXAQAAAOAhkatHkgAAAADA0QhJAAAAAGCBkAQAAAAAFmy+Jyk1NVURERGKiopSQkKC0tLSrNZv3LjRbsUBAAAAgKPZHJIGDBigiIgItW7dWjVq1JDJZMqJugAAAADAKWwOSUuWLNGyZcvUqlWrnKgHAAAAAJzK5nuSChQooAoVKuRELQAAAADgdDaHpCFDhmjmzJkyDCMn6gEAAAAAp8rW5XYdO3a0Wt64caPWrFmj6tWrK3/+/Fbrli9fbr/qAAAAAMDBshWSfH19rZY7dOiQI8UAAAAAgLNlKyTNmzcvp+sAAAAAgFzB5nuSnnrqKV26dClDe1JSkp566il71AQAAAAATmNzSNq0aZOuX7+eof3atWvaunWrXYoCAAAAAGfJ9vck7d+/3/zvgwcP6u+//zYvp6amau3atXrkkUfsWx0AAAAAOFi2Q1Lt2rVlMplkMpkyvazOw8NDn3zyiV2LAwAAAABHy3ZIOn78uAzD0KOPPqodO3bIz8/PvK5AgQIqXry4XF1dc6RIAAAAAHCUbIekMmXKSJLS0tJyrBgAAAAAcLZsh6R0q1atyrTdZDLJ3d1dFSpUULly5e67MAAAAABwBptDUvv27WUymWQYhlV7epvJZFLDhg21YsUKFS5c2G6FAgAAAIAj2DwFeGRkpOrXr6/IyEglJiYqMTFRkZGRatCggVavXq0tW7bon3/+0dChQ3OiXgAAAADIUTaPJA0YMEBz585VUFCQuS0kJETu7u7q27evDhw4oBkzZqh37952LRQAAAAAHMHmkaRjx47Jx8cnQ7uPj4/++OMPSVLFihV1/vz5+68OAAAAABzM5pBUt25dDRs2TOfOnTO3nTt3TsOHD1f9+vUlSUeOHFFgYKD9qgQAAAAAB7H5crsvvvhC7dq1U6lSpcxBKD4+Xo8++qhWrlwpSbpy5YpGjRpl30oBAAAAwAFsDkmVK1fWwYMHtX79eh0+fNjc9vTTT8vF5dbAVPv27e1aJAAAAAA4is0hSZJcXFzUokULtWjRwt71AAAAAIBT3VNIioqKUlRUlBISEpSWlma17ssvv7RLYQAAAADgDDaHpPDwcI0bN0716tVTyZIlZTKZcqIuAAAAAHAKm0PSZ599poiICL300ks5UQ8AAAAAOJXNU4Bfv37d6otkAQAAACAvsTkk9enTR4sWLcqJWgAAAADA6Wy+3O7atWuaO3euNmzYoFq1ail//vxW66dPn2634gAAAADA0WwOSfv371ft2rUlSb/99pvVOiZxAAAAAPCgszkkRUdH50QdAAAAAJAr2HxPUrqjR49q3bp1+vfffyVJhmHYrSgAAAAAcBabQ9I///yjkJAQVapUSa1atdKZM2ckSS+//LKGDBli9wItTZ48WSaTSQMHDszRfgAAAAA8vGwOSYMGDVL+/Pl16tQpeXp6mtu7du2qtWvX2rU4Szt37tScOXNUq1atHOsDAAAAAGwOSevXr9eUKVNUqlQpq/aKFSvq5MmTdivM0pUrVxQWFqbPP/9chQsXvuO2KSkpSkpKsnoAAAAAQHbZPHFDcnKy1QhSugsXLsjNzc0uRd2uX79+at26tUJDQ/X+++/fcdtJkyYpPDw8R+oAAAD2UXbED84uIVc7Mbm1s0sAHmo2jyQ1atRICxYsMC+bTCalpaVp6tSpatasmV2Lk6QlS5Zoz549mjRpUra2HzlypBITE82P+Ph4u9cEAAAAIO+yeSRp6tSpCgkJ0a5du3T9+nUNHz5cBw4c0IULFxQTE2PX4uLj4zVgwABFRkbK3d09W/u4ubnl2IgWAAAAgLzP5pGkGjVq6PDhw2rYsKHatWun5ORkdezYUXv37lX58uXtWtzu3buVkJCgOnXqKF++fMqXL582b96sjz/+WPny5VNqaqpd+wMAAAAAm0eSJMnX11fvvvuuVduff/6pvn37au7cuXYpTJJCQkL066+/WrX16tVLVapU0dtvvy1XV1e79QUAAAAA0j2GpMz8888/+uKLL+wakry9vVWjRg2rtoIFC6po0aIZ2gEAAADAHmy+3A4AAAAA8jK7jSQ5yqZNm5xdAgAAAIA8jJEkAAAAALCQ7ZGkjh073nH9pUuX7rcWAAAAAHC6bIckX1/fu67v3r37fRcEAAAAAM6U7ZA0b968nKwDAAAAAHIF7kkCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAuEJAAAAACwQEgCAAAAAAv5nF3Aw6bsiB+cXUKudmJya2eXAORKfHbcGZ8dAAB7YiQJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAQq4OSZMmTVL9+vXl7e2t4sWLq3379oqLi3N2WQAAAADysFwdkjZv3qx+/frpl19+UWRkpG7cuKFnnnlGycnJzi4NAAAAQB6Vz9kF3MnatWutliMiIlS8eHHt3r1bjRs3dlJVAAAAAPKyXB2SbpeYmChJKlKkSJbbpKSkKCUlxbyclJSU43UBAAAAyDty9eV2ltLS0jRw4EAFBwerRo0aWW43adIk+fr6mh+BgYEOrBIAAADAg+6BCUn9+vXTb7/9piVLltxxu5EjRyoxMdH8iI+Pd1CFAAAAAPKCB+JyuzfffFOrV6/Wli1bVKpUqTtu6+bmJjc3NwdVBgAAACCvydUhyTAMvfXWW/r++++1adMmlStXztklAQAAAMjjcnVI6tevnxYtWqSVK1fK29tbf//9tyTJ19dXHh4eTq4OAAAAQF6Uq+9Jmj17thITE9W0aVOVLFnS/Fi6dKmzSwMAAACQR+XqkSTDMJxdAgAAAICHTK4eSQIAAAAARyMkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWCAkAQAAAIAFQhIAAAAAWMjn7AIAAACAB1nZET84u4Rc78Tk1s4uwSaMJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFggJAEAAACABUISAAAAAFh4IELSrFmzVLZsWbm7u6tBgwbasWOHs0sCAAAAkEfl+pC0dOlSDR48WGPGjNGePXv02GOPqXnz5kpISHB2aQAAAADyoFwfkqZPn65XXnlFvXr1UrVq1fTZZ5/J09NTX375pbNLAwAAAJAH5XN2AXdy/fp17d69WyNHjjS3ubi4KDQ0VD///HOm+6SkpCglJcW8nJiYKElKSkrK2WKzKS3lqrNLyNXs9Tpxnu+Oc+0YnGfH4Dw7DufaMTjPjsF5dpzc8rt4eh2GYdxxO5Nxty2c6PTp03rkkUe0bds2/ec//zG3Dx8+XJs3b9b27dsz7DN27FiFh4c7skwAAAAAD5D4+HiVKlUqy/W5eiTpXowcOVKDBw82L6elpenChQsqWrSoTCaTEyvLfZKSkhQYGKj4+Hj5+Pg4u5w8jXPtGJxnx+A8Ow7n2jE4z47BeXYcznXWDMPQ5cuXFRAQcMftcnVIKlasmFxdXXX27Fmr9rNnz6pEiRKZ7uPm5iY3NzertkKFCuVUiXmCj48PP0AOwrl2DM6zY3CeHYdz7RicZ8fgPDsO5zpzvr6+d90mV0/cUKBAAdWtW1dRUVHmtrS0NEVFRVldfgcAAAAA9pKrR5IkafDgwerRo4fq1aunJ554QjNmzFBycrJ69erl7NIAAAAA5EG5PiR17dpV586d0+jRo/X333+rdu3aWrt2rfz9/Z1d2gPPzc1NY8aMyXB5IuyPc+0YnGfH4Dw7DufaMTjPjsF5dhzO9f3L1bPbAQAAAICj5ep7kgAAAADA0QhJAAAAAGCBkAQAAAAAFghJwF00bdpUAwcOlCRdvXpVnTp1ko+Pj0wmky5duqSyZctqxowZ2XquiIgIm7+3y5bnz6t69uyp9u3bm5ctXxOJc+RI9/IeRvaNHTtW/v7+MplMWrFihUP7PnHihEwmk2JjYx3arzPx2ZF73f45nxleP+QkQtJD4Oeff5arq6tat26dre3Hjh2r2rVr52xRD5Dly5dr/PjxkqT58+dr69at2rZtm86cOSNfX1/t3LlTffv2zdZzde3aVYcPH87Jch1m06ZN5qCYXbeHHXux5TXA/clL7+HcFvgOHTqk8PBwzZkzR2fOnFHLli0d2n9gYKDOnDmjGjVqOLRfR8htrzWA3C/XTwGO+/fFF1/orbfe0hdffKHTp08rICAg0+0Mw1BqaqqDq8v9ihQpYv73sWPHVLVqVatfIvz8/LL9XB4eHvLw8LBrfbDtNcD9eRjfw9evX1eBAgVyvJ9jx45Jktq1ayeTyXTPz3Pjxg3lz5/f5v1cXV1VokSJe+4XsAdH/bwBd8NIUh535coVLV26VK+//rpat26tiIgI87r0kYA1a9aobt26cnNz08KFCxUeHq59+/bJZDLJZDJZ7fMwSh/yb9q0qaZNm6YtW7bIZDKpadOmkjIO91+6dEmvvvqq/P395e7urho1amj16tWSMv4189ixY2rXrp38/f3l5eWl+vXra8OGDQ48OvtJP7Z169apatWq8vLyUosWLXTmzBlJt0Yo58+fr5UrV5rfW5s2bZIkxcfHq0uXLipUqJCKFCmidu3a6cSJE9nu+/bX4Pfff1fDhg3l7u6uatWqacOGDRkuX7pbn+mjXh9++KFKliypokWLql+/frpx44Z5m5SUFL399tsKDAyUm5ubKlSooC+++MK8/rffflPLli3l5eUlf39/vfTSSzp//rxN5zW3uf09vG/fPjVr1kze3t7y8fFR3bp1tWvXLucVmE2bNm1Sr169lJiYaH4/jh07VtKt99P48ePVvXt3+fj4mEcp3377bVWqVEmenp569NFH9d5771m9H9JH4b/66iuVLVtWvr6+ev7553X58mXzNt9++61q1qwpDw8PFS1aVKGhoUpOTtbYsWPVpk0bSZKLi4s5JKWlpWncuHEqVaqU3NzczN8VmC79ErmlS5eqSZMmcnd319dff21+/06cOFH+/v4qVKiQxo0bp5s3b2rYsGEqUqSISpUqpXnz5mV4rvTL7dL/j4iKilK9evXk6empoKAgxcXFWZ3L999/X8WLF5e3t7f69OmjESNG5KqrEe70Wku3LqPu3bu3vL29Vbp0ac2dO9dq//v9fMpLDMOQn5+fvv32W3Nb7dq1VbJkSfPyTz/9JDc3N129elWSdOrUKbVr105eXl7y8fFRly5ddPbsWfP26T83//vf/1SuXDm5u7tn2ndCQoLatGkjDw8PlStXTl9//XUOHSVwCyEpj1u2bJmqVKmiypUr68UXX9SXX36p278aa8SIEZo8ebIOHTqkp59+WkOGDFH16tV15swZnTlzRl27dnVS9bnL8uXL9corr+g///mPzpw5o+XLl2fYJi0tTS1btlRMTIwWLlyogwcPavLkyXJ1dc30Oa9cuaJWrVopKipKe/fuVYsWLdSmTRudOnUqpw8nR1y9elUffvihvvrqK23ZskWnTp3S0KFDJUlDhw5Vly5dzMHpzJkzCgoK0o0bN9S8eXN5e3tr69atiomJMQes69ev21xDamqq2rdvL09PT23fvl1z587Vu+++a7VNdvuMjo7WsWPHFB0drfnz5ysiIsLqjwbdu3fX4sWL9fHHH+vQoUOaM2eOvLy8JN0Ky0899ZQef/xx7dq1S2vXrtXZs2fVpUuXezizuVdYWJhKlSqlnTt3avfu3RoxYsQ9jWI4WlBQkGbMmCEfHx/z+zH9vSpJH374oR577DHt3btX7733niTJ29tbEREROnjwoGbOnKnPP/9cH330kdXzHjt2TCtWrNDq1au1evVqbd68WZMnT5YknTlzRt26dVPv3r116NAhbdq0SR07dpRhGBo6dKg5sKTXI0kzZ87UtGnT9OGHH2r//v1q3ry52rZtqyNHjlj1O2LECA0YMECHDh1S8+bNJUkbN27U6dOntWXLFk2fPl1jxozRs88+q8KFC2v79u167bXX9Oqrr+rPP/+847l69913NW3aNO3atUv58uVT7969zeu+/vprTZgwQVOmTNHu3btVunRpzZ49+15ekhxzt9d62rRpqlevnvbu3as33nhDr7/+ujkI2vvz6UFnMpnUuHFj8x+4Ll68qEOHDunff//V77//LknavHmz6tevL09PT6Wlpaldu3a6cOGCNm/erMjISP3xxx8Zfq84evSovvvuOy1fvjzLe+J69uyp+Ph4RUdH69tvv9V///tfJSQk5OTh4mFnIE8LCgoyZsyYYRiGYdy4ccMoVqyYER0dbRiGYURHRxuSjBUrVljtM2bMGOOxxx5zcKW5V5MmTYwBAwYYhmEYAwYMMJo0aWK1vkyZMsZHH31kGIZhrFu3znBxcTHi4uIyfa558+YZvr6+d+yvevXqxieffJLp8+cm6e+fixcvGoZx69gkGUePHjVvM2vWLMPf39+83KNHD6Ndu3ZWz/PVV18ZlStXNtLS0sxtKSkphoeHh7Fu3bpM97N8TQzD+hytWbPGyJcvn3HmzBnz+sjISEOS8f3339vUZ5kyZYybN2+at3nuueeMrl27GoZhGHFxcYYkIzIyMtPzM378eOOZZ56xaouPjzckZfn+eBDc/h729vY2IiIinFfQfcjq57FMmTJG+/bt77r/Bx98YNStW9e8PGbMGMPT09NISkoytw0bNsxo0KCBYRiGsXv3bkOSceLEiUyf7/vvvzdu/285ICDAmDBhglVb/fr1jTfeeMMwDMM4fvy4Icn8OZ8u/f2bmppqbqtcubLRqFEj8/LNmzeNggULGosXL7Z6rr179xqG8f8/4xs2bDDv88MPPxiSjH///dcwDMNo0KCB0a9fP6u+g4ODc93/IXd6rV988UXzclpamlG8eHFj9uzZhmFk77PiYfPxxx8b1atXNwzDMFasWGE0aNDAaNeunfmchYaGGu+8845hGIaxfv16w9XV1Th16pR5/wMHDhiSjB07dhiGcevnJn/+/EZCQoJVP5af8+mft+n7GIZhHDp0yJCUK/9/RN7ASFIeFhcXpx07dqhbt26SpHz58qlr165WlwNJUr169ZxRXp4UGxurUqVKqVKlStna/sqVKxo6dKiqVq2qQoUKycvLS4cOHXpgR5I8PT1Vvnx583LJkiXv+pe+ffv26ejRo/L29paXl5e8vLxUpEgRXbt2zXyPhi3i4uIUGBhodW/FE088cU99Vq9e3WoU0PJ4YmNj5erqqiZNmmR5XNHR0ebn9/LyUpUqVSTpno4rtxo8eLD69Omj0NBQTZ48Oc8cW2afi0uXLlVwcLBKlCghLy8vjRo1KsPPatmyZeXt7W1etnzPPPbYYwoJCVHNmjX13HPP6fPPP9fFixezrCEpKUmnT59WcHCwVXtwcLAOHTp013qrV68uF5f//2/e399fNWvWNC+7urqqaNGid/0ZrVWrltXxSDLvExcXl+Hn6/bl3M7y+Ewmk0qUKGE+Pnt/PuUFTZo00cGDB3Xu3Dlt3rxZTZs2VdOmTbVp0ybduHFD27ZtM1+OfujQIQUGBiowMNC8f7Vq1VSoUCGr93CZMmXueG/poUOHlC9fPtWtW9fcVqVKFSbjQI5i4oY87IsvvtDNmzetJmowDENubm769NNPzW0FCxZ0Rnl5kq03tA8dOlSRkZH68MMPVaFCBXl4eKhz584P7GUct19mZTKZMlzeebsrV66obt26mV5fnlMTMmS3z8yOJy0tTdLdX+srV66oTZs2mjJlSoZ1ltfvP+jGjh2rF154QT/88IPWrFmjMWPGaMmSJerQoYOzS7svt38u/vzzzwoLC1N4eLiaN28uX19fLVmyRNOmTbPa7k7vGVdXV0VGRmrbtm1av369PvnkE7377rvavn27ypUrZ9d6s6rlTvVlxXIfy3ul8oo7nRNnfD7ldjVr1lSRIkW0efNmbd68WRMmTFCJEiU0ZcoU7dy5Uzdu3FBQUJBNz8nvIciNCEl51M2bN7VgwQJNmzZNzzzzjNW69u3ba/Hixea/at+uQIECzHJ3j2rVqqU///xThw8fztZoUkxMjHr27Gn+hfLKlSt5+obgzN5bderU0dKlS1W8eHH5+Pjcdx+VK1dWfHy8zp49K39/f0m3pgi3d581a9ZUWlqaNm/erNDQ0Azr69Spo++++05ly5ZVvnx5+6O2UqVKqlSpkgYNGqRu3bpp3rx5D0RIsuWzbtu2bSpTpozV/W0nT560uU+TyaTg4GAFBwdr9OjRKlOmjL7//nsNHjw4w7Y+Pj4KCAhQTEyM1YhlTExMrhmtqVy5snbu3Knu3bub227/ecsN7vX/NXt/PuUFJpNJjRo10sqVK3XgwAE1bNhQnp6eSklJ0Zw5c1SvXj1z6Klatari4+MVHx9vHk06ePCgLl26pGrVqmW7zypVqujmzZvavXu36tevL+nWKKYtX0EB2IrL7fKo1atX6+LFi3r55ZdVo0YNq0enTp0yXHJnqWzZsjp+/LhiY2N1/vx5paSkOLDyB1uTJk3UuHFjderUSZGRkTp+/LjWrFljNRuVpYoVK5pvVN23b59eeOGFPPUX2tuVLVtW+/fvV1xcnM6fP68bN24oLCxMxYoVU7t27bR161YdP35cmzZtUv/+/e96Q3lmnn76aZUvX149evTQ/v37FRMTo1GjRkn6/7+C26PPsmXLqkePHurdu7dWrFhhfo5ly5ZJkvr166cLFy6oW7du2rlzp44dO6Z169apV69eeeaPEP/++6/efPNNbdq0SSdPnlRMTIx27typqlWrOru0bClbtqyuXLmiqKgonT9/3jwbV2YqVqyoU6dOacmSJTp27Jg+/vhjff/99zb1t337dk2cOFG7du3SqVOntHz5cp07d+6O52vYsGGaMmWKli5dqri4OI0YMUKxsbEaMGCATX3nlPSvl5g/f76OHDmi999/X/v377+vKcxzgi2vtSV7fz7lFU2bNtXixYtVu3ZteXl5ycXFRY0bN9bXX39tFehDQ0NVs2ZNhYWFac+ePdqxY4e6d++uJk2a2HSpf+XKldWiRQu9+uqr2r59u3bv3q0+ffo8dF9HYItPP/1UISEhzi7jgUZIyqO++OILhYaGytfXN8O6Tp06adeuXdq/f3+m+3bq1EktWrRQs2bN5Ofnp8WLF+d0uXnKd999p/r166tbt26qVq2ahg8fnuUvxdOnT1fhwoUVFBSkNm3aqHnz5qpTp46DK3acV155RZUrV1a9evXk5+enmJgYeXp6asuWLSpdurQ6duyoqlWr6uWXX9a1a9fu6S+3rq6uWrFiha5cuaL69eurT58+5r/+p08ta68+Z8+erc6dO+uNN95QlSpV9Morryg5OVmSzCMAqampeuaZZ1SzZk0NHDhQhQoVsrpP5EHm6uqqf/75R927d1elSpXUpUsXtWzZUuHh4c4uLVuCgoL02muvqWvXrvLz89PUqVOz3LZt27YaNGiQ3nzzTdWuXVvbtm0zz3qXXT4+PtqyZYtatWqlSpUqadSoUZo2bdodvzS2f//+Gjx4sIYMGaKaNWtq7dq1WrVqlSpWrGhT3zklLCxMI0eO1NChQ1WnTh0dP35cPXv2zHIaZ2ex5bW2ZO/Pp7yiSZMmSk1NNd97JN0KTre3mUwmrVy5UoULF1bjxo0VGhqqRx99VEuXLrW5z3nz5ikgIEBNmjRRx44d1bdvXxUvXtwOR5M3nT9//qG9b85eTMbdbhgAgAdcTEyMGjZsqKNHj1pNLAHA/p5++mmVKFFCX331lbNLAYB7lrcvlAfwUPr+++/l5eWlihUr6ujRoxowYICCg4MJSICdXb16VZ999pmaN28uV1dXLV68WBs2bFBkZKSzSwOA+0JIApDnXL58WW+//bZOnTqlYsWKKTQ0NMMsZADun8lk0o8//qgJEybo2rVrqly5sr777rtMJzMBgAcJl9sBAAAAgIW8cfcwAAAAANgJIQkAAAAALBCSAAAAAMACIQkAAAAALBCSAAAAAMACIQkAkCecOHFCJpNJsbGxzi4FAPCAIyQBAHINk8l0x8fYsWOdXSIA4CHAl8kCAHKNM2fOmP+9dOlSjR49WnFxceY2Ly8vZ5QFAHjIMJIEAMg1SpQoYX74+vrKZDKZl4sXL67p06erVKlScnNzU+3atbV27dosnys1NVW9e/dWlSpVdOrUKUnSypUrVadOHbm7u+vRRx9VeHi4bt68ad7HZDLpf//7nzp06CBPT09VrFhRq1atMq+/ePGiwsLC5OfnJw8PD1WsWFHz5s3LuRMCAHAKQhIA4IEwc+ZMTZs2TR9++KH279+v5s2bq23btjpy5EiGbVNSUvTcc88pNjZWW7duVenSpbV161Z1795dAwYM0MGDBzVnzhxFRERowoQJVvuGh4erS5cu2r9/v1q1aqWwsDBduHBBkvTee+/p4MGDWrNmjQ4dOqTZs2erWLFiDjl+AIDjmAzDMJxdBAAAt4uIiNDAgQN16dIlSdIjjzyifv366Z133jFv88QTT6h+/fqaNWuWTpw4oXLlymnr1q0aO3asUlJStHr1avn6+kqSQkNDFRISopEjR5r3X7hwoYYPH67Tp09LujWSNGrUKI0fP16SlJycLC8vL61Zs0YtWrRQ27ZtVaxYMX355ZcOOgsAAGfgniQAQK6XlJSk06dPKzg42Ko9ODhY+/bts2rr1q2bSpUqpY0bN8rDw8Pcvm/fPsXExFiNHKWmpuratWu6evWqPD09JUm1atUyry9YsKB8fHyUkJAgSXr99dfVqVMn7dmzR88884zat2+voKAgux8vAMC5uNwOAJCntGrVSvv379fPP/9s1X7lyhWFh4crNjbW/Pj111915MgRubu7m7fLnz+/1X4mk0lpaWmSpJYtW+rkyZMaNGiQTp8+rZCQEA0dOjTnDwoA4FCEJABArufj46OAgADFxMRYtcfExKhatWpWba+//romT56stm3bavPmzeb2OnXqKC4uThUqVMjwcHHJ/n+Hfn5+6tGjhxYuXKgZM2Zo7ty593dwAIBch8vtAAAPhGHDhmnMmDEqX768ateurXnz5ik2NlZff/11hm3feustpaam6tlnn9WaNWvUsGFDjR49Ws8++6xKly6tzp07y8XFRfv27dNvv/2m999/P1s1jB49WnXr1lX16tXN9zxVrVrV3ocKAHAyQhIA4IHQv39/JSYmasiQIUpISFC1atW0atUqVaxYMdPtBw4cqLS0NLVq1Upr165V8+bNtXr1ao0bN05TpkxR/vz5VaVKFfXp0yfbNRQoUEAjR47UiRMn5OHhoUaNGmnJkiX2OkQAQC7B7HYAAAAAYIF7kgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAAiEJAAAAACwQkgAAAADAwv8BaOsTmvkHWx8AAAAASUVORK5CYII=\n"
- },
- "metadata": {}
- }
- ],
- "source": [
- "\n",
- "\n",
- "# Load a pre-trained GPT model and its tokenizer\n",
- "model_name = \"gpt2\"\n",
- "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
- "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
- "\n",
- "# Sample text for tokenization\n",
- "text = \"Artificial Intelligence is transforming the world.\"\n",
- "\n",
- "# Tokenizing the text\n",
- "tokens = tokenizer.encode(text, return_tensors=\"pt\")\n",
- "print(f\"Tokens: {tokens}\")\n",
- "\n",
- "# Decoding tokens back to text\n",
- "decoded_text = tokenizer.decode(tokens[0])\n",
- "print(f\"Decoded Text: {decoded_text}\")\n",
- "\n",
- "# Visualize tokenization\n",
- "token_texts = [tokenizer.decode([token]) for token in tokens[0]]\n",
- "plt.figure(figsize=(10, 4))\n",
- "plt.bar(range(len(token_texts)), [len(token) for token in token_texts], tick_label=token_texts)\n",
- "plt.xlabel('Tokens')\n",
- "plt.ylabel('Length of Each Token')\n",
- "plt.title('Tokenization Visualization')\n",
- "plt.show()\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Output 2: The advancements in artificial intelligence have been made possible by the development of new algorithms that are able to recognize people's faces in a variety of ways, from the way they look to how they behave.\n",
+ "\n",
+ "The first such algorithm, known as the Face\n",
+ "\n",
+ "Output 3: The advancements in artificial intelligence and robotics have made it possible to create a computer that is capable of performing complex tasks that have never been done before.\n",
+ "\n",
+ "\"We're doing things that will not be possible before,\" said Dr. Gail M.\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Generate multiple outputs for the same prompt\n",
+ "set_seed(42) # Setting a seed for reproducibility\n",
+ "for i in range(3):\n",
+ " print(f\"Output {i+1}: {generate_text(prompt)}\\n\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9H14GOeivCXC"
+ },
+ "source": [
+ " # Prompt Engineering\n",
+ "\n",
+ "This section covers how modifying prompts can change the outputs generated."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "EQTyEIbcu_TI",
+ "outputId": "1470032c-e517-49ed-9daf-2cc382fb33ed"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "d935aa2f"
- },
- "source": [
- "\n",
- "## Understanding Token Probabilities\n",
- "\n",
- "LLMs generate text by predicting the next token based on the probability distribution over the vocabulary. The model assigns a probability to each token, indicating how likely it is to come next given the input text.\n",
- "\n",
- "We'll demonstrate how the model generates probabilities for the next token using a simple prompt.\n"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 572
- },
- "id": "5504776a",
- "outputId": "4eb17d53-4afb-4658-8e85-653cc4867bf8"
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- ""
- ],
- "image/png": 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\n"
- },
- "metadata": {}
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Token: es, Probability: 0.1297\n",
- "Token: was, Probability: 0.0457\n",
- "Token: is, Probability: 0.0344\n",
- "Token: 's, Probability: 0.0332\n",
- "Token: ,, Probability: 0.0313\n"
- ]
- }
- ],
- "source": [
- "\n",
- "# Generate output with token probabilities\n",
- "input_text = \"The quick brown fox\"\n",
- "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\n",
- "output = model(input_ids)\n",
- "\n",
- "# Extract logits (raw scores) for the next token prediction\n",
- "logits = output.logits[0, -1, :]\n",
- "probs = torch.softmax(logits, dim=0)\n",
- "\n",
- "# Show the top 5 probable tokens\n",
- "top_k = 5\n",
- "top_k_indices = torch.topk(probs, top_k).indices\n",
- "top_k_probs = torch.topk(probs, top_k).values\n",
- "\n",
- "# Visualize token probabilities\n",
- "top_k_tokens = [tokenizer.decode([idx]) for idx in top_k_indices]\n",
- "plt.figure(figsize=(8, 5))\n",
- "plt.bar(top_k_tokens, top_k_probs.detach().numpy())\n",
- "plt.xlabel('Tokens')\n",
- "plt.ylabel('Probability')\n",
- "plt.title('Top-5 Token Probabilities')\n",
- "plt.show()\n",
- "\n",
- "# Display top-k tokens with their probabilities\n",
- "for i in range(top_k):\n",
- " token = tokenizer.decode([top_k_indices[i].item()])\n",
- " print(f\"Token: {token}, Probability: {top_k_probs[i].item():.4f}\")\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Prompt: Explain the concept of machine learning.\n"
+ ]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "440963c9"
- },
- "source": [
- "\n",
- "## Exploring the Stochastic Nature of LLMs\n",
- "\n",
- "The behavior of LLMs can be controlled by various parameters during text generation:\n",
- "\n",
- "- **Temperature**: Controls the randomness of predictions. A higher temperature value (e.g., 1.0) makes the model generate more random outputs, while a lower value (e.g., 0.2) makes it more focused and deterministic.\n",
- "- **Top-k Sampling**: Limits the model’s token choices to the top-k most probable tokens.\n",
- "- **Top-p Sampling (Nucleus Sampling)**: Limits token selection to those with a cumulative probability below a threshold (p).\n",
- "\n",
- "Let's experiment with these parameters to see their effects on the generated text.\n"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "9c969e28",
- "outputId": "4f9e13f5-a29d-448c-b2e4-0296737eac22"
- },
- "outputs": [
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
- "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n",
- "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
- "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Temperature 0.2: Once upon a time, the world was a place of great beauty and great danger. The world was a place of great danger. The world was a place of great danger. The world was a place of great danger. The world was a place of\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stderr",
- "text": [
- "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
- "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
- ]
- },
- {
- "output_type": "stream",
- "name": "stdout",
- "text": [
- "Temperature 0.7: Once upon a time, she saw him and she said, 'How can I say that you were a man?' He said, 'I had a son.' And she said, 'I heard you say that. How can I say that you were\n",
- "Temperature 1.0: Once upon a time, I felt at ease with the thought of being able to control the emotions. And yet it took me a little while to come to accept it as a reality. I was also afraid of feeling too proud of myself, because there\n"
- ]
- }
- ],
- "source": [
- "# Function to generate text with different parameters\n",
- "def generate_text(prompt, max_length=50, temperature=0.7, top_k=50, top_p=0.9):\n",
- " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
- " output = model.generate(\n",
- " input_ids,\n",
- " max_length=max_length,\n",
- " temperature=temperature,\n",
- " top_k=top_k,\n",
- " top_p=top_p,\n",
- " do_sample=True,\n",
- " )\n",
- " return tokenizer.decode(output[0], skip_special_tokens=True)\n",
- "\n",
- "# Testing with different temperatures\n",
- "prompt = \"Once upon a time\"\n",
- "print(\"Temperature 0.2:\", generate_text(prompt, temperature=0.2))\n",
- "print(\"Temperature 0.7:\", generate_text(prompt, temperature=0.7))\n",
- "print(\"Temperature 1.0:\", generate_text(prompt, temperature=1.0))\n"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Explain the concept of machine learning.\n",
+ "\n",
+ "This is the third article in a series of articles in Computer Vision and Machine Learning.\n",
+ "\n",
+ "The first article is from the Computer Vision and Machine Learning Institute.\n",
+ "\n",
+ "The second article is from the\n",
+ "\n",
+ "--------------------------------------------------\n",
+ "\n",
+ "Prompt: Explain the concept of machine learning in simple terms.\n"
+ ]
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "27b21512"
- },
- "source": [
- "\n",
- "## Interactive Prompt Engineering\n",
- "\n",
- "Try modifying the prompt and parameters to see how they affect the output. This is an important aspect of prompt engineering, where small changes in the input can lead to different outputs.\n"
- ]
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
+ ]
},
{
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 265,
- "referenced_widgets": [
- "ffe4d972a0b74270a604c04d5c12397c",
- "b46058236d844cc88920d694cc318850",
- "2b513016ccd141bba01b7d6f3691e680",
- "a73cd8bd4c774735abd1e0225143bb8e",
- "31866301c8f9409fa6b7d62f301fcc40",
- "6bd75cbe1ffd4d36be66dae0f32137d6",
- "fc2017e688c5476991674ec173754cfd",
- "cdad5116ad64449f9d2ac4f4bad6cd53",
- "38ac2a593cda4c119081ebed8f4707ee",
- "772c8c22072544719d31fb28004624c3",
- "75830447817a494b879ac788a760919f",
- "d457087c0d364526989a9e5cfec6fb9b",
- "1e42836ab9f74e3486436322c7736ca5",
- "3e8f509028e547049d903b4ed5fb4876",
- "afdb2987395849878185d9618f3da323",
- "e9e782dda4864e9c9010929b4ed4b1bc"
- ]
- },
- "id": "5debeaff",
- "outputId": "d59f5186-ee4f-4007-f5a2-26cc3caf0dd1"
- },
- "outputs": [
- {
- "output_type": "display_data",
- "data": {
- "text/plain": [
- "interactive(children=(Text(value='Explain the concept of deep learning.', description='Prompt:'), FloatSlider(…"
- ],
- "application/vnd.jupyter.widget-view+json": {
- "version_major": 2,
- "version_minor": 0,
- "model_id": "ffe4d972a0b74270a604c04d5c12397c"
- }
- },
- "metadata": {}
- }
- ],
- "source": [
- "\n",
- "import ipywidgets as widgets\n",
- "from IPython.display import display\n",
- "\n",
- "# Interactive function for prompt engineering\n",
- "def interactive_prompt(prompt, temperature=0.7, top_k=50, max_length=50):\n",
- " print(generate_text(prompt, temperature=temperature, top_k=top_k, max_length=max_length))\n",
- "\n",
- "prompt_input = widgets.Text(value=\"Explain the concept of deep learning.\", description='Prompt:')\n",
- "temperature_slider = widgets.FloatSlider(value=0.7, min=0.1, max=1.5, step=0.1, description='Temperature:')\n",
- "top_k_slider = widgets.IntSlider(value=50, min=1, max=100, step=1, description='Top-k:')\n",
- "length_slider = widgets.IntSlider(value=50, min=10, max=100, step=10, description='Max Length:')\n",
- "\n",
- "widgets.interactive(interactive_prompt, prompt=prompt_input, temperature=temperature_slider, top_k=top_k_slider, max_length=length_slider)\n"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Explain the concept of machine learning in simple terms.\n",
+ "\n",
+ "The machine learning is a system of learning that can be applied to any task. In particular, machine learning is a system that can be applied to any task in the world.\n",
+ "\n",
+ "\n",
+ "\n",
+ "--------------------------------------------------\n",
+ "\n",
+ "Prompt: Explain machine learning to a 10-year-old.\n",
+ "Explain machine learning to a 10-year-old.\n",
+ "\n",
+ "The next step is to find a way to teach students the basics of machine learning.\n",
+ "\n",
+ "\"We have some of the most advanced systems available,\" said Chris Binder, a\n",
+ "\n",
+ "--------------------------------------------------\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Testing prompt engineering\n",
+ "prompts = [\n",
+ " \"Explain the concept of machine learning.\",\n",
+ " \"Explain the concept of machine learning in simple terms.\",\n",
+ " \"Explain machine learning to a 10-year-old.\",\n",
+ "]\n",
+ "\n",
+ "for p in prompts:\n",
+ " print(f\"Prompt: {p}\")\n",
+ " print(generate_text(p))\n",
+ " print(\"\\n\" + \"-\"*50 + \"\\n\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2fa5476a"
+ },
+ "source": [
+ "\n",
+ "## Tokenization Explained in Detail\n",
+ "\n",
+ "In this section, we'll dive deeper into tokenization. The process of tokenization is essential for transforming raw text into tokens that the model can understand. Each token represents a word, part of a word, or even special characters.\n",
+ "\n",
+ "Let's see a practical demonstration of how a sentence is tokenized using a pre-trained GPT model. We will also visualize how text is broken down into tokens.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 444
+ },
+ "id": "a0c8b391",
+ "outputId": "6bd7567c-5647-4d80-8918-abb3e72b2fc2"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Tokens: tensor([[ 8001, 9542, 9345, 318, 25449, 262, 995, 13]])\n",
+ "Decoded Text: Artificial Intelligence is transforming the world.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "\n",
+ "# Load a pre-trained GPT model and its tokenizer\n",
+ "model_name = \"gpt2\"\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+ "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
+ "\n",
+ "# Sample text for tokenization\n",
+ "text = \"Artificial Intelligence is transforming the world.\"\n",
+ "\n",
+ "# Tokenizing the text\n",
+ "tokens = tokenizer.encode(text, return_tensors=\"pt\")\n",
+ "print(f\"Tokens: {tokens}\")\n",
+ "\n",
+ "# Decoding tokens back to text\n",
+ "decoded_text = tokenizer.decode(tokens[0])\n",
+ "print(f\"Decoded Text: {decoded_text}\")\n",
+ "\n",
+ "# Visualize tokenization\n",
+ "token_texts = [tokenizer.decode([token]) for token in tokens[0]]\n",
+ "plt.figure(figsize=(10, 4))\n",
+ "plt.bar(range(len(token_texts)), [len(token) for token in token_texts], tick_label=token_texts)\n",
+ "plt.xlabel('Tokens')\n",
+ "plt.ylabel('Length of Each Token')\n",
+ "plt.title('Tokenization Visualization')\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d935aa2f"
+ },
+ "source": [
+ "\n",
+ "## Understanding Token Probabilities\n",
+ "\n",
+ "LLMs generate text by predicting the next token based on the probability distribution over the vocabulary. The model assigns a probability to each token, indicating how likely it is to come next given the input text.\n",
+ "\n",
+ "We'll demonstrate how the model generates probabilities for the next token using a simple prompt.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 572
},
+ "id": "5504776a",
+ "outputId": "4eb17d53-4afb-4658-8e85-653cc4867bf8"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "70a78d74"
- },
- "source": [
- "\n",
- "# **Conclusion**\n",
- "\n",
- "In this notebook, we explored how Large Language Models (LLMs) like GPT work, focusing on key concepts such as:\n",
- "1. **Tokenization**: Transforming text into tokens that the model can understand.\n",
- "2. **Token Probabilities**: Understanding how the model assigns probabilities to different tokens when generating text.\n",
- "3. **Text Generation**: Using different techniques like temperature, top-k, and top-p sampling to control text generation.\n",
- "4. **Stochastic Nature**: Demonstrating how randomness plays a role in generating diverse outputs even with the same prompt.\n",
- "5. **Prompt Engineering**: Highlighting how small changes in the prompt can significantly impact the output.\n",
- "\n",
- "These concepts are crucial for understanding how LLMs generate human-like text and how to leverage them effectively in various applications.\n",
- "\n",
- "## **Next Steps**\n",
- "\n",
- "If you'd like to explore further, consider the following:\n",
- "- Experiment with different prompts to see how the model behaves.\n",
- "- Try adjusting the temperature, top-k, and top-p parameters to observe how they change the output.\n",
- "- Explore other pre-trained models available in the Hugging Face model hub.\n",
- "- Apply these techniques to a real-world task, such as text summarization, chatbot development, or content generation.\n",
- "\n",
- "### **Additional Resources**\n",
- "- [Hugging Face Transformers Documentation](https://huggingface.co/transformers/)\n",
- "- [OpenAI GPT-3 Documentation](https://beta.openai.com/docs/)\n",
- "- [NLP with Python](https://www.nltk.org/book/)\n",
- "\n",
- "We hope this notebook has given you a foundational understanding of how LLMs work. Happy experimenting!\n"
+ "data": {
+ "image/png": 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+ },
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+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Token: es, Probability: 0.1297\n",
+ "Token: was, Probability: 0.0457\n",
+ "Token: is, Probability: 0.0344\n",
+ "Token: 's, Probability: 0.0332\n",
+ "Token: ,, Probability: 0.0313\n"
+ ]
}
- ],
- "metadata": {
+ ],
+ "source": [
+ "\n",
+ "# Generate output with token probabilities\n",
+ "input_text = \"The quick brown fox\"\n",
+ "input_ids = tokenizer.encode(input_text, return_tensors=\"pt\")\n",
+ "output = model(input_ids)\n",
+ "\n",
+ "# Extract logits (raw scores) for the next token prediction\n",
+ "logits = output.logits[0, -1, :]\n",
+ "probs = torch.softmax(logits, dim=0)\n",
+ "\n",
+ "# Show the top 5 probable tokens\n",
+ "top_k = 5\n",
+ "top_k_indices = torch.topk(probs, top_k).indices\n",
+ "top_k_probs = torch.topk(probs, top_k).values\n",
+ "\n",
+ "# Visualize token probabilities\n",
+ "top_k_tokens = [tokenizer.decode([idx]) for idx in top_k_indices]\n",
+ "plt.figure(figsize=(8, 5))\n",
+ "plt.bar(top_k_tokens, top_k_probs.detach().numpy())\n",
+ "plt.xlabel('Tokens')\n",
+ "plt.ylabel('Probability')\n",
+ "plt.title('Top-5 Token Probabilities')\n",
+ "plt.show()\n",
+ "\n",
+ "# Display top-k tokens with their probabilities\n",
+ "for i in range(top_k):\n",
+ " token = tokenizer.decode([top_k_indices[i].item()])\n",
+ " print(f\"Token: {token}, Probability: {top_k_probs[i].item():.4f}\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "440963c9"
+ },
+ "source": [
+ "\n",
+ "## Exploring the Stochastic Nature of LLMs\n",
+ "\n",
+ "The behavior of LLMs can be controlled by various parameters during text generation:\n",
+ "\n",
+ "- **Temperature**: Controls the randomness of predictions. A higher temperature value (e.g., 1.0) makes the model generate more random outputs, while a lower value (e.g., 0.2) makes it more focused and deterministic.\n",
+ "- **Top-k Sampling**: Limits the model’s token choices to the top-k most probable tokens.\n",
+ "- **Top-p Sampling (Nucleus Sampling)**: Limits token selection to those with a cumulative probability below a threshold (p).\n",
+ "\n",
+ "Let's experiment with these parameters to see their effects on the generated text.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
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+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9c969e28",
+ "outputId": "4f9e13f5-a29d-448c-b2e4-0296737eac22"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n",
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Temperature 0.2: Once upon a time, the world was a place of great beauty and great danger. The world was a place of great danger. The world was a place of great danger. The world was a place of great danger. The world was a place of\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+ "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Temperature 0.7: Once upon a time, she saw him and she said, 'How can I say that you were a man?' He said, 'I had a son.' And she said, 'I heard you say that. How can I say that you were\n",
+ "Temperature 1.0: Once upon a time, I felt at ease with the thought of being able to control the emotions. And yet it took me a little while to come to accept it as a reality. I was also afraid of feeling too proud of myself, because there\n"
+ ]
}
+ ],
+ "source": [
+ "# Function to generate text with different parameters\n",
+ "def generate_text(prompt, max_length=50, temperature=0.7, top_k=50, top_p=0.9):\n",
+ " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n",
+ " output = model.generate(\n",
+ " input_ids,\n",
+ " max_length=max_length,\n",
+ " temperature=temperature,\n",
+ " top_k=top_k,\n",
+ " top_p=top_p,\n",
+ " do_sample=True,\n",
+ " )\n",
+ " return tokenizer.decode(output[0], skip_special_tokens=True)\n",
+ "\n",
+ "# Testing with different temperatures\n",
+ "prompt = \"Once upon a time\"\n",
+ "print(\"Temperature 0.2:\", generate_text(prompt, temperature=0.2))\n",
+ "print(\"Temperature 0.7:\", generate_text(prompt, temperature=0.7))\n",
+ "print(\"Temperature 1.0:\", generate_text(prompt, temperature=1.0))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "27b21512"
+ },
+ "source": [
+ "\n",
+ "## Interactive Prompt Engineering\n",
+ "\n",
+ "Try modifying the prompt and parameters to see how they affect the output. This is an important aspect of prompt engineering, where small changes in the input can lead to different outputs.\n"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 0
-}
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+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "ffe4d972a0b74270a604c04d5c12397c",
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+ "version_minor": 0
+ },
+ "text/plain": [
+ "interactive(children=(Text(value='Explain the concept of deep learning.', description='Prompt:'), FloatSlider(…"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "\n",
+ "import ipywidgets as widgets\n",
+ "from IPython.display import display\n",
+ "\n",
+ "# Interactive function for prompt engineering\n",
+ "def interactive_prompt(prompt, temperature=0.7, top_k=50, max_length=50):\n",
+ " print(generate_text(prompt, temperature=temperature, top_k=top_k, max_length=max_length))\n",
+ "\n",
+ "prompt_input = widgets.Text(value=\"Explain the concept of deep learning.\", description='Prompt:')\n",
+ "temperature_slider = widgets.FloatSlider(value=0.7, min=0.1, max=1.5, step=0.1, description='Temperature:')\n",
+ "top_k_slider = widgets.IntSlider(value=50, min=1, max=100, step=1, description='Top-k:')\n",
+ "length_slider = widgets.IntSlider(value=50, min=10, max=100, step=10, description='Max Length:')\n",
+ "\n",
+ "widgets.interactive(interactive_prompt, prompt=prompt_input, temperature=temperature_slider, top_k=top_k_slider, max_length=length_slider)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "70a78d74"
+ },
+ "source": [
+ "\n",
+ "# **Conclusion**\n",
+ "\n",
+ "In this notebook, we explored how Large Language Models (LLMs) like GPT work, focusing on key concepts such as:\n",
+ "1. **Tokenization**: Transforming text into tokens that the model can understand.\n",
+ "2. **Token Probabilities**: Understanding how the model assigns probabilities to different tokens when generating text.\n",
+ "3. **Text Generation**: Using different techniques like temperature, top-k, and top-p sampling to control text generation.\n",
+ "4. **Stochastic Nature**: Demonstrating how randomness plays a role in generating diverse outputs even with the same prompt.\n",
+ "5. **Prompt Engineering**: Highlighting how small changes in the prompt can significantly impact the output.\n",
+ "\n",
+ "These concepts are crucial for understanding how LLMs generate human-like text and how to leverage them effectively in various applications.\n",
+ "\n",
+ "## **Next Steps**\n",
+ "\n",
+ "If you'd like to explore further, consider the following:\n",
+ "- Experiment with different prompts to see how the model behaves.\n",
+ "- Try adjusting the temperature, top-k, and top-p parameters to observe how they change the output.\n",
+ "- Explore other pre-trained models available in the Hugging Face model hub.\n",
+ "- Apply these techniques to a real-world task, such as text summarization, chatbot development, or content generation.\n",
+ "\n",
+ "### **Additional Resources**\n",
+ "- [Hugging Face Transformers Documentation](https://huggingface.co/transformers/)\n",
+ "- [OpenAI GPT-3 Documentation](https://beta.openai.com/docs/)\n",
+ "- [NLP with Python](https://www.nltk.org/book/)\n",
+ "\n",
+ "We hope this notebook has given you a foundational understanding of how LLMs work. Happy experimenting!\n"
+ ]
+ }
+ ],
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+ "Explain the concept of deep learning.\n",
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diff --git a/requirements.txt b/requirements.txt
index 69c9097..8345618 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,4 @@
ace_tools==0.0
-acled_conflict_analysis @ git+https://github.com/datapartnership/acled_conflict_analysis.git@5f3f089a3a0662379b034a13f8ecc67a58f03609
aiohappyeyeballs==2.4.3
aiohttp==3.10.10
aiosignal==1.3.1