From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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230 lines
9.3 KiB
230 lines
9.3 KiB
{ |
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"cells": [ |
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"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", |
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"metadata": {}, |
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"source": [ |
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"# Welcome to your first assignment!\n", |
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"\n", |
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"Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#f71;\">Just before we get to the assignment --</h2>\n", |
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" <span style=\"color:#f71;\">I thought I'd take a second to point you at this page of useful resources for the course. This includes links to all the slides.<br/>\n", |
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" <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">https://edwarddonner.com/2024/11/13/llm-engineering-resources/</a><br/>\n", |
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" Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", |
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" </span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", |
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"metadata": {}, |
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"source": [ |
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"# HOMEWORK EXERCISE ASSIGNMENT\n", |
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"\n", |
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"Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", |
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"\n", |
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"You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", |
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"\n", |
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"**Benefits:**\n", |
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"1. No API charges - open-source\n", |
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"2. Data doesn't leave your box\n", |
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"\n", |
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"**Disadvantages:**\n", |
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"1. Significantly less power than Frontier Model\n", |
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"\n", |
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"## Recap on installation of Ollama\n", |
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"\n", |
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"Simply visit [ollama.com](https://ollama.com) and install!\n", |
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"\n", |
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"Once complete, the ollama server should already be running locally. \n", |
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"If you visit: \n", |
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"[http://localhost:11434/](http://localhost:11434/)\n", |
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"\n", |
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"You should see the message `Ollama is running`. \n", |
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"\n", |
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"If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", |
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"And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", |
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"Then try [http://localhost:11434/](http://localhost:11434/) again.\n", |
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"\n", |
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"If Ollama is slow on your machine, try using `llama3.2:1b` as an alternative. Run `ollama pull llama3.2:1b` from a Terminal or Powershell, and change the code below from `MODEL = \"llama3.2\"` to `MODEL = \"llama3.2:1b\"`" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 1, |
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"id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# imports\n", |
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"\n", |
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"import requests\n", |
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"from bs4 import BeautifulSoup\n", |
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"from IPython.display import Markdown, display" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 2, |
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"id": "29ddd15d-a3c5-4f4e-a678-873f56162724", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Constants\n", |
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"\n", |
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"OLLAMA_API = \"http://localhost:11434/api/chat\"\n", |
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"HEADERS = {\"Content-Type\": \"application/json\"}\n", |
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"MODEL = \"llama3.2\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 3, |
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"id": "dac0a679-599c-441f-9bf2-ddc73d35b940", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Create a messages list using the same format that we used for OpenAI\n", |
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"\n", |
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"messages = [\n", |
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" {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", |
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"]" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 4, |
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"id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"payload = {\n", |
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" \"model\": MODEL,\n", |
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" \"messages\": messages,\n", |
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" \"stream\": False\n", |
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" }" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 5, |
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"id": "42b9f644-522d-4e05-a691-56e7658c0ea9", |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"name": "stdout", |
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"output_type": "stream", |
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"text": [ |
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"Generative AI has numerous business applications across various industries, including:\n", |
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"\n", |
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"1. **Content Creation**: Generative AI can be used to generate high-quality content such as articles, social media posts, product descriptions, and even entire books. This can help businesses save time and resources while maintaining consistency in their content.\n", |
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"2. **Image and Video Generation**: Generative AI can create realistic images and videos for various purposes, such as:\n", |
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" * Product visualization: Generate 3D models or animations to showcase products in different scenarios.\n", |
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" * Advertising: Create custom graphics, billboards, or social media ads with unique visuals.\n", |
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" * Training simulations: Develop realistic training simulations for employees, customers, or patients.\n", |
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"3. **Marketing and Advertising**: Generative AI can help businesses:\n", |
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" * Personalize ads: Generate targeted ads based on customer preferences, behavior, and demographics.\n", |
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" * Improve product recommendation engines: Create personalized recommendations using generative models.\n", |
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" * Automate social media management: Generate social media posts, engage with customers, and monitor online conversations.\n", |
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"4. **Product Design**: Generative AI can aid in:\n", |
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" * Product design: Generate 3D models, prototypes, or design concepts for new products.\n", |
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" * Fashion design: Create custom clothing, accessories, or interior designs using generative algorithms.\n", |
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"5. **Customer Service**: Generative AI can help businesses:\n", |
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" * Automate customer support chatbots: Use generative models to respond to customer inquiries and resolve issues.\n", |
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" * Generate personalized customer experiences: Develop customized content, offers, or recommendations based on customer behavior and preferences.\n", |
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"6. **Audio and Music Generation**: Generative AI can be used to create:\n", |
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" * Original music compositions: Compose unique music tracks for ads, videos, or films.\n", |
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" * Podcasts and audiobooks: Generate custom audio content for businesses or clients.\n", |
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"7. **Data Augmentation**: Generative AI can help businesses augment their datasets by generating new data points, which can improve the accuracy of machine learning models.\n", |
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"8. **Supply Chain Optimization**: Generative AI can be used to:\n", |
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" * Predict demand: Use generative models to forecast demand and optimize inventory levels.\n", |
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" * Optimize logistics: Generate routes, schedules, or shipping plans using generative algorithms.\n", |
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"9. **Financial Modeling**: Generative AI can help businesses create more accurate financial models by generating new scenarios, forecasts, or predictions based on historical data.\n", |
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"10. **Innovation**: Generative AI can facilitate innovation in various industries by:\n", |
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" * Generating new product ideas: Use generative models to come up with novel products or services.\n", |
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" * Developing new business strategies: Create customized plans and proposals for clients using generative algorithms.\n", |
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"\n", |
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"These are just a few examples of the many business applications of Generative AI. As the technology continues to evolve, we can expect to see even more innovative uses in various industries.\n" |
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] |
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} |
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], |
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"source": [ |
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"response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", |
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"print(response.json()['message']['content'])" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", |
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"metadata": {}, |
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"source": [ |
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"# Introducing the ollama package\n", |
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"\n", |
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"And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", |
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"\n", |
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"Under the hood, it's making the same call as above to the ollama server running at localhost:11434" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import ollama\n", |
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"\n", |
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"response = ollama.chat(model=MODEL, messages=messages)\n", |
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"print(response['message']['content'])" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", |
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"metadata": {}, |
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"source": [ |
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"# NOW the exercise for you\n", |
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"\n", |
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"Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI" |
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] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"file_extension": ".py", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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