{ "cells": [ { "cell_type": "markdown", "id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9", "metadata": {}, "source": [ "# Welcome to your first assignment!\n", "\n", "Instructions are below. Please give this a try, and look in the solutions folder if you get stuck (or feel free to ask me!)" ] }, { "cell_type": "markdown", "id": "ada885d9-4d42-4d9b-97f0-74fbbbfe93a9", "metadata": {}, "source": [ "\n", " \n", " \n", " \n", " \n", "
\n", " \n", " \n", "

Just before we get to the assignment --

\n", " 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.
\n", " https://edwarddonner.com/2024/11/13/llm-engineering-resources/
\n", " Please keep this bookmarked, and I'll continue to add more useful links there over time.\n", "
\n", "
" ] }, { "cell_type": "markdown", "id": "6e9fa1fc-eac5-4d1d-9be4-541b3f2b3458", "metadata": {}, "source": [ "# HOMEWORK EXERCISE ASSIGNMENT\n", "\n", "Upgrade the day 1 project to summarize a webpage to use an Open Source model running locally via Ollama rather than OpenAI\n", "\n", "You'll be able to use this technique for all subsequent projects if you'd prefer not to use paid APIs.\n", "\n", "**Benefits:**\n", "1. No API charges - open-source\n", "2. Data doesn't leave your box\n", "\n", "**Disadvantages:**\n", "1. Significantly less power than Frontier Model\n", "\n", "## Recap on installation of Ollama\n", "\n", "Simply visit [ollama.com](https://ollama.com) and install!\n", "\n", "Once complete, the ollama server should already be running locally. \n", "If you visit: \n", "[http://localhost:11434/](http://localhost:11434/)\n", "\n", "You should see the message `Ollama is running`. \n", "\n", "If not, bring up a new Terminal (Mac) or Powershell (Windows) and enter `ollama serve` \n", "And in another Terminal (Mac) or Powershell (Windows), enter `ollama pull llama3.2` \n", "Then try [http://localhost:11434/](http://localhost:11434/) again.\n", "\n", "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\"`" ] }, { "cell_type": "code", "execution_count": 1, "id": "4e2a9393-7767-488e-a8bf-27c12dca35bd", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import requests\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display" ] }, { "cell_type": "code", "execution_count": 2, "id": "29ddd15d-a3c5-4f4e-a678-873f56162724", "metadata": {}, "outputs": [], "source": [ "# Constants\n", "\n", "OLLAMA_API = \"http://localhost:11434/api/chat\"\n", "HEADERS = {\"Content-Type\": \"application/json\"}\n", "MODEL = \"llama3.2\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "dac0a679-599c-441f-9bf2-ddc73d35b940", "metadata": {}, "outputs": [], "source": [ "# Create a messages list using the same format that we used for OpenAI\n", "\n", "messages = [\n", " {\"role\": \"user\", \"content\": \"Describe some of the business applications of Generative AI\"}\n", "]" ] }, { "cell_type": "code", "execution_count": 4, "id": "7bb9c624-14f0-4945-a719-8ddb64f66f47", "metadata": {}, "outputs": [], "source": [ "payload = {\n", " \"model\": MODEL,\n", " \"messages\": messages,\n", " \"stream\": False\n", " }" ] }, { "cell_type": "code", "execution_count": null, "id": "479ff514-e8bd-4985-a572-2ea28bb4fa40", "metadata": {}, "outputs": [], "source": [ "# Let's just make sure the model is loaded\n", "\n", "!ollama pull llama3.2" ] }, { "cell_type": "code", "execution_count": 5, "id": "42b9f644-522d-4e05-a691-56e7658c0ea9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generative AI has numerous business applications across various industries, including:\n", "\n", "1. **Content Creation**: Generative AI can be used to generate high-quality content such as articles, social media posts, product descriptions, and more.\n", "2. **Virtual Assistants**: Generative AI-powered virtual assistants can help businesses automate customer service, provide personalized recommendations, and offer expert advice.\n", "3. **Product Design**: Generative AI can aid in the design of new products by generating 2D and 3D models, prototypes, and even entire product lines.\n", "4. **Marketing and Advertising**: Generative AI can create personalized marketing campaigns, generate ad copy, and optimize advertising content for better engagement.\n", "5. **Data Analysis**: Generative AI can help analyze large datasets, identify patterns, and provide insights to inform business decisions.\n", "6. **Predictive Maintenance**: Generative AI-powered predictive maintenance can help businesses predict equipment failures, reduce downtime, and improve overall efficiency.\n", "7. **Personalized Recommendations**: Generative AI can be used to generate personalized product recommendations for customers based on their behavior, preferences, and demographics.\n", "8. **Language Translation**: Generative AI-powered language translation tools can help businesses communicate with customers in multiple languages, improving global reach and customer engagement.\n", "9. **Chatbots and Conversational Interfaces**: Generative AI can create conversational interfaces that are more human-like and personalized, providing better customer experiences.\n", "10. **Innovation and R&D**: Generative AI can aid in the discovery of new products, materials, and processes by generating ideas and exploring different design spaces.\n", "\n", "Some specific business applications include:\n", "\n", "* **Automated content generation for blogs and websites**\n", "* **Personalized product recommendations for e-commerce platforms**\n", "* **Predictive maintenance for industrial equipment**\n", "* **Chatbots and conversational interfaces for customer service**\n", "* **Language translation for global marketing campaigns**\n", "* **AI-powered design tools for product development**\n", "* **Data analysis and insights for business decision-making**\n", "\n", "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 across various industries.\n" ] } ], "source": [ "# If this doesn't work for any reason, try the 2 versions in the following cells\n", "# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n", "# And if none of that works - contact me!\n", "\n", "response = requests.post(OLLAMA_API, json=payload, headers=HEADERS)\n", "print(response.json()['message']['content'])" ] }, { "cell_type": "markdown", "id": "6a021f13-d6a1-4b96-8e18-4eae49d876fe", "metadata": {}, "source": [ "# Introducing the ollama package\n", "\n", "And now we'll do the same thing, but using the elegant ollama python package instead of a direct HTTP call.\n", "\n", "Under the hood, it's making the same call as above to the ollama server running at localhost:11434" ] }, { "cell_type": "code", "execution_count": 6, "id": "7745b9c4-57dc-4867-9180-61fa5db55eb8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generative AI has numerous business applications across various industries. Here are some examples:\n", "\n", "1. **Content Generation**: Generative AI can create high-quality content, such as articles, social media posts, product descriptions, and more. This can help businesses streamline their content creation process, reduce costs, and increase productivity.\n", "2. **Image and Video Generation**: Generative AI can generate images and videos that are indistinguishable from those created by humans. This has applications in fields like advertising, marketing, and entertainment, where visual content is crucial.\n", "3. **Chatbots and Virtual Assistants**: Generative AI can power chatbots and virtual assistants, enabling businesses to provide 24/7 customer support, answer frequently asked questions, and route complex inquiries to human representatives.\n", "4. **Predictive Analytics and Forecasting**: Generative AI can analyze large datasets and generate predictions about future trends and patterns. This helps businesses make informed decisions, optimize operations, and predict market demands.\n", "5. **Sales and Marketing Automation**: Generative AI can automate sales and marketing processes, such as lead generation, personalized email campaigns, and customized product recommendations.\n", "6. **Customer Service and Support**: Generative AI-powered chatbots can help businesses provide personalized customer support, resolve issues quickly, and reduce response times.\n", "7. **Product Design and Engineering**: Generative AI can aid in the design and development of new products by generating 3D models, prototypes, and simulations.\n", "8. **Financial Analysis and Risk Assessment**: Generative AI can analyze financial data to identify trends, predict market fluctuations, and assess risk levels for businesses and investors.\n", "9. **Supply Chain Optimization**: Generative AI can optimize supply chain operations, such as demand forecasting, inventory management, and logistics planning.\n", "10. **Research and Development**: Generative AI can assist researchers in generating new ideas, simulating experiments, and analyzing large datasets to discover patterns and insights.\n", "\n", "Some specific industries that are leveraging Generative AI include:\n", "\n", "1. **E-commerce**: Using generative AI to create product descriptions, images, and videos for online stores.\n", "2. **Marketing**: Leveraging generative AI to generate personalized content, ads, and social media posts.\n", "3. **Finance**: Applying generative AI to analyze financial data, predict market trends, and optimize investment portfolios.\n", "4. **Healthcare**: Using generative AI to analyze medical images, develop personalized treatment plans, and create synthetic patient data.\n", "5. **Education**: Leveraging generative AI to create customized learning materials, adaptive assessments, and virtual teaching assistants.\n", "\n", "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" ] } ], "source": [ "import ollama\n", "\n", "response = ollama.chat(model=MODEL, messages=messages)\n", "print(response['message']['content'])" ] }, { "cell_type": "markdown", "id": "a4704e10-f5fb-4c15-a935-f046c06fb13d", "metadata": {}, "source": [ "## Alternative approach - using OpenAI python library to connect to Ollama" ] }, { "cell_type": "code", "execution_count": 7, "id": "23057e00-b6fc-4678-93a9-6b31cb704bff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generative AI has numerous business applications across various industries, including:\n", "\n", "1. **Content Creation**: Generate high-quality content such as articles, social media posts, product descriptions, and even entire books. This can save time and resources for content teams.\n", "2. **Marketing Automation**: Use generative AI to create personalized marketing campaigns, emails, and advertisements tailored to individual customers' preferences.\n", "3. **Product Development**: Employ generative AI to design new products, optimize existing designs, and create prototypes. This can streamline the product development process and reduce costs.\n", "4. **Customer Service Chatbots**: Develop chatbots that use generative AI to provide 24/7 customer support, respond to frequently asked questions, and offer personalized recommendations.\n", "5. **Image and Video Generation**: Use generative AI to create high-quality visual content, such as product images, marketing materials, and promotional videos.\n", "6. **Predictive Analytics**: Employ generative AI algorithms to analyze large datasets and make predictions about future trends, customer behavior, or market conditions.\n", "7. **Sales Enablement**: Generate personalized sales materials, such as emails, presentations, and proposals, that help sales teams connect with customers more effectively.\n", "8. **Financial Services**: Use generative AI to generate financial reports, forecasts, and analyses; provide credit risk assessments; and automate tasks for accountants and analysts.\n", "9. **Healthcare**: Develop generative AI models to analyze medical images, diagnose diseases, and optimize treatment plans.\n", "10. **Virtual Assistants**: Create virtual assistants that use generative AI to schedule appointments, manage workflows, and perform administrative tasks.\n", "\n", "Some specific examples of business applications include:\n", "\n", "* IBM's AI-powered content generation platform, which can create articles and social media posts in minutes.\n", "* Microsoft's Generative Model, which can generate high-quality images, videos, and text based on user inputs.\n", "* Google's AutoML (Automated Machine Learning) tool, which uses generative AI to help users build machine learning models without extensive expertise.\n", "* Amazon's Generative AI model, which can create personalized customer recommendations and product descriptions.\n", "\n", "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 use cases across various industries.\n" ] } ], "source": [ "# There's actually an alternative approach that some people might prefer\n", "# You can use the OpenAI client python library to call Ollama:\n", "\n", "from openai import OpenAI\n", "ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n", "\n", "response = ollama_via_openai.chat.completions.create(\n", " model=MODEL,\n", " messages=messages\n", ")\n", "\n", "print(response.choices[0].message.content)" ] }, { "cell_type": "markdown", "id": "9f9e22da-b891-41f6-9ac9-bd0c0a5f4f44", "metadata": {}, "source": [ "## Are you confused about why that works?\n", "\n", "It seems strange, right? We just used OpenAI code to call Ollama?? What's going on?!\n", "\n", "Here's the scoop:\n", "\n", "The python class `OpenAI` is simply code written by OpenAI engineers that makes calls over the internet to an endpoint. \n", "\n", "When you call `openai.chat.completions.create()`, this python code just makes a web request to the following url: \"https://api.openai.com/v1/chat/completions\"\n", "\n", "Code like this is known as a \"client library\" - it's just wrapper code that runs on your machine to make web requests. The actual power of GPT is running on OpenAI's cloud behind this API, not on your computer!\n", "\n", "OpenAI was so popular, that lots of other AI providers provided identical web endpoints, so you could use the same approach.\n", "\n", "So Ollama has an endpoint running on your local box at http://localhost:11434/v1/chat/completions \n", "And in week 2 we'll discover that lots of other providers do this too, including Gemini and DeepSeek.\n", "\n", "And then the team at OpenAI had a great idea: they can extend their client library so you can specify a different 'base url', and use their library to call any compatible API.\n", "\n", "That's it!\n", "\n", "So when you say: `ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')` \n", "Then this will make the same endpoint calls, but to Ollama instead of OpenAI." ] }, { "cell_type": "markdown", "id": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90", "metadata": {}, "source": [ "## Also trying the amazing reasoning model DeepSeek\n", "\n", "Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n", "This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n", "\n", "Other sizes of DeepSeek are [here](https://ollama.com/library/deepseek-r1) all the way up to the full 671B parameter version, which would use up 404GB of your drive and is far too large for most!" ] }, { "cell_type": "code", "execution_count": 8, "id": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠋ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠙ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠹ \u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[1Gpulling manifest ⠸ 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7.4 MB/s 2m28s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 2% ▕ ▏ 23 MB/1.1 GB 7.4 MB/s 2m28s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 2% ▕ ▏ 25 MB/1.1 GB 7.4 MB/s 2m28s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 2% ▕ ▏ 25 MB/1.1 GB 7.4 MB/s 2m28s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 2% ▕ ▏ 27 MB/1.1 GB 7.4 MB/s 2m27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 29 MB/1.1 GB 7.4 MB/s 2m27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 32 MB/1.1 GB 7.4 MB/s 2m27s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 33 MB/1.1 GB 11 MB/s 1m38s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 33 MB/1.1 GB 11 MB/s 1m38s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 36 MB/1.1 GB 11 MB/s 1m38s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 3% ▕ ▏ 37 MB/1.1 GB 11 MB/s 1m38s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 4% ▕ ▏ 40 MB/1.1 GB 11 MB/s 1m37s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 4% ▕ ▏ 46 MB/1.1 GB 11 MB/s 1m37s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 4% ▕ ▏ 49 MB/1.1 GB 11 MB/s 1m37s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 5% ▕ ▏ 54 MB/1.1 GB 11 MB/s 1m36s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 5% ▕ ▏ 55 MB/1.1 GB 11 MB/s 1m36s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 5% ▕ ▏ 59 MB/1.1 GB 11 MB/s 1m36s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 6% ▕ ▏ 68 MB/1.1 GB 17 MB/s 1m1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 6% ▕ ▏ 68 MB/1.1 GB 17 MB/s 1m1s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 7% ▕█ ▏ 73 MB/1.1 GB 17 MB/s 1m0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 7% ▕█ ▏ 74 MB/1.1 GB 17 MB/s 1m0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 7% ▕█ ▏ 77 MB/1.1 GB 17 MB/s 1m0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 8% ▕█ ▏ 84 MB/1.1 GB 17 MB/s 1m0s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 8% ▕█ ▏ 92 MB/1.1 GB 17 MB/s 59s\u001b[K\u001b[?25h\u001b[?2026l\u001b[?2026h\u001b[?25l\u001b[A\u001b[1Gpulling manifest \u001b[K\n", "pulling aabd4debf0c8... 8% ▕█ ▏ 92 MB/1.1 GB 17 MB/s 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"pulling a85fe2a2e58e... 100% ▕████████████████▏ 487 B \u001b[K\n", "verifying sha256 digest \u001b[K\n", "writing manifest \u001b[K\n", "success \u001b[K\u001b[?25h\u001b[?2026l\n" ] } ], "source": [ "!ollama pull deepseek-r1:1.5b" ] }, { "cell_type": "code", "execution_count": 9, "id": "1d3d554b-e00d-4c08-9300-45e073950a76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Okay, so I need to understand what LLMs are based on. From what I've heard before, they're these models that can answer questions and generate text, right? But I'm not exactly sure how they work under the hood. The user asked me to define a few core concepts: neural networks, attention, and transformers. Let me think through each of these.\n", "\n", "Starting with neural networks. From what I remember, neural networks are inspired by how the human brain works. They have layers of interconnected nodes, or neurons. Each neuron receives input, processes it, applies an activation function to produce an output, and then passes that output on to the next layer. The key aspect here is deep learning, which involves feeding data through multiple levels of these networks to make predictions. So for LLMs, which are similar, they probably use something akin to a deep neural network, structured into layers with shared weights to capture patterns across different data points.\n", "\n", "Next up is attention. I think attention is about the model focusing on specific parts of the input rather than processing everything linearly. Something called \"attention mechanism\" in RNNs has led researchers back to traditional sequence modeling. So maybe LLMs use some form where each part of the input interacts with others, depending on its relevance or position. I'm not entirely sure how this fits into a neural network framework though.\n", "\n", "The other key term is transformers. From what I've studied, transformers are models that process sequential data by using attention mechanisms internally. They don't have fixed layers like image networks; they use self-attention to model long-range dependencies in sequences. The attention weights determine which parts of the input affect each other and output a new context vector, allowing the model to focus on important features without being constrained by fixed layer structures.\n", "\n", "Thinking about how these might combine into an LLM: If the core is a neural network (which I imagine has multiple layers and parameters), the attention mechanism could be implemented as part of it, potentially separating parameters for different interactions. Maybe with attention, each position in the input can attend to others, enabling complex dependencies. The transformer itself would handle learning these representations through its self-Attention layer.\n", "\n", "But wait, how does a transformer-based model work exactly? In one resource I saw, it mentioned that transformers don't have explicit hidden or text layers but instead compute both the representation of each token and their attention. The self-attention mechanism processes each token to produce key-value pairs, then combines these with other tokens' outputs. This allows for context-dependent interactions without fixed layers. \n", "\n", "So putting this together, the neural network layer likely provides forward pass through a series of layers, while the attention mechanism adds inter-task interaction based on relevance, perhaps in each position. The decoder part might use self-attention within its own processing.\n", "\n", "Overall, I think LLMs build upon these components by integrating a deep structure (neural network) with attention and transformers, enabling them to process sequences comprehensively and focus on relevant parts. But where do all these pieces fit together exactly? Maybe the decoder in a transformer uses the encoding's contextual representations through attention. Each step processes sequences in a way that leverages self-attention across different positions.\n", "\n", "I should also consider if there are specific models within LLMs, like BERT or ChatGPT. For example, WordPiece is often used to handle sparse tokens, and decoder-only models might not use traditional hidden layers but instead learn representations through the transformer's attention. In contrast, decoder networks process tokens sequentially, using attention on previous steps.\n", "\n", "I should also think about computational requirements. Neural networks are computationally intensive, especially with large models. Transformers have their own efficient architectures, like sparse subword embeddings to speed things up. But both approaches seem foundational for LLMs.\n", "\n", "Are there potential limitations or areas of research? Maybe comparing the advantages and shortcomings between attention and transformer-based approaches in LLMs could be an interesting topic. Or how attention mechanisms affect computational efficiency could also be a point of study.\n", "\n", "I think I have a better grasp now. A neural net has layers with parameters, attention allows looking at data interactively based on relevance, and transformers use self-attention to model dependencies without fixed layers. They combine to enable processing sequences effectively.\n", "\n", "\n", "A Large Language Model (LLM) is built upon several key concepts integrated within its computational framework:\n", "\n", "1. **Neural Network Core**: \n", " - Neural networks are composed of interconnected nodes (neurons) with layers that process inputs through transformations. Each neuron applies an activation function, enabling complex feature learning and prediction. An LLM's neural network likely processes input data through multiple layers, capturing sequential patterns.\n", "\n", "2. **Attention Mechanism**:\n", " - Attention, as formalized in RNNs, involves each node interacting dynamically with others based on their relevance or position, allowing focused context-aware processing. This mechanism is integrated into a deep structure, enhancing the model's ability to attend to important information without fixed layers.\n", "\n", "3. **Transformers Core**:\n", " - Transformers use self-attention internally, processing sequences without predefined hidden layers. Each token computes representations through key-value pairs from others, enabling contextual dependencies and long-range interactions. This self-attention mechanism allows for flexible, dynamic processing of sequential data.\n", "\n", "The integration of these components in an LLM combines the deep structure (neural network) with attention to focus on relevant parts, using transformers to model hidden dependencies efficiently. Each part works together in a forward pass within the decoder or via subsequent layers and attention mechanisms, enabling comprehensive sequence modeling.\n" ] } ], "source": [ "# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside tags, followed by some decent definitions\n", "\n", "response = ollama_via_openai.chat.completions.create(\n", " model=\"deepseek-r1:1.5b\",\n", " messages=[{\"role\": \"user\", \"content\": \"Please give definitions of some core concepts behind LLMs: a neural network, attention and the transformer\"}]\n", ")\n", "\n", "print(response.choices[0].message.content)" ] }, { "cell_type": "markdown", "id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898", "metadata": {}, "source": [ "# NOW the exercise for you\n", "\n", "Take the code from day1 and incorporate it here, to build a website summarizer that uses Llama 3.2 running locally instead of OpenAI; use either of the above approaches." ] }, { "cell_type": "code", "execution_count": 10, "id": "6de38216-6d1c-48c4-877b-86d403f4e0f8", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import requests\n", "from dotenv import load_dotenv\n", "from bs4 import BeautifulSoup\n", "from IPython.display import Markdown, display\n", "from openai import OpenAI" ] }, { "cell_type": "code", "execution_count": 11, "id": "0c2029fd-acfd-406f-b12c-5fc9b458c273", "metadata": {}, "outputs": [], "source": [ "# Some websites need you to use proper headers when fetching them:\n", "headers = {\n", " \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n", "}\n", "\n", "# Constants\n", "OLLAMA_API = \"http://localhost:11434/api/chat\"\n", "MODEL = \"llama3.2\"" ] }, { "cell_type": "code", "execution_count": 12, "id": "57c33a88-a908-40a6-9d04-f74a7811f66d", "metadata": {}, "outputs": [], "source": [ "class Website:\n", "\n", " def __init__(self, url):\n", " \"\"\"\n", " Create this Website object from the given url using the BeautifulSoup library\n", " \"\"\"\n", " self.url = url\n", " response = requests.get(url, headers=headers)\n", " soup = BeautifulSoup(response.content, 'html.parser')\n", " self.title = soup.title.string if soup.title else \"No title found\"\n", " for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n", " irrelevant.decompose()\n", " self.text = soup.body.get_text(separator=\"\\n\", strip=True)" ] }, { "cell_type": "code", "execution_count": 14, "id": "85e9922e-ef60-4595-a49e-80d86e502572", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening up the Design System for Everyone — The Federated Model | by Anirudh Ramamohan | Bootcamp | Medium\n" ] } ], "source": [ "medium = Website(\"https://medium.com/design-bootcamp/opening-up-the-design-system-for-everyone-11b9b92f3f75\")\n", "print(medium.title)\n", "# print(medium.text)" ] }, { "cell_type": "code", "execution_count": 15, "id": "29a84eeb-0a21-4991-9b63-69cb4c76d2d2", "metadata": {}, "outputs": [], "source": [ "system_prompt = \"You are an assistant that analyzes the contents of a website \\\n", "and provides a short summary, ignoring text that might be navigation related. \\\n", "Respond in markdown.\"" ] }, { "cell_type": "code", "execution_count": 16, "id": "3fef5c9e-7e3f-41f6-b0a2-29ef56d0e851", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for(website):\n", " user_prompt = f\"You are looking at a website titled {website.title}\"\n", " user_prompt += \"\\nThe contents of this website is as follows; \\\n", "please provide a short summary of this website in markdown. \\\n", "If it includes news or announcements, then summarize these too.\\n\\n\"\n", " user_prompt += website.text\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "d1fbbc02-eacb-4e5d-a353-e4088abf9285", "metadata": { "scrolled": true }, "outputs": [], "source": [ "print(user_prompt_for(medium))" ] }, { "cell_type": "code", "execution_count": 18, "id": "34ba1f4d-c0bb-4eb5-8ad0-9675bab07133", "metadata": {}, "outputs": [], "source": [ "def messages_for(website):\n", " return [\n", " {\"role\": \"system\", \"content\": system_prompt},\n", " {\"role\": \"user\", \"content\": user_prompt_for(website)}\n", " ]" ] }, { "cell_type": "code", "execution_count": 26, "id": "f787b71d-8637-4e73-9ec5-09bc99c696d2", "metadata": {}, "outputs": [], "source": [ "def summarize(url):\n", " website = Website(url)\n", " response = ollama.chat(\n", " model = MODEL,\n", " messages = messages_for(website),\n", " )\n", " return response['message']['content']" ] }, { "cell_type": "code", "execution_count": 27, "id": "33515ef3-5465-484c-9986-b8f2adc32fd3", "metadata": {}, "outputs": [], "source": [ "def display_summary(url):\n", " summary = summarize(url)\n", " display(Markdown(summary))" ] }, { "cell_type": "code", "execution_count": 28, "id": "aa878178-7a14-4614-8e5f-aa48b1e60030", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**Summary of the Website**\n", "==========================\n", "\n", "### Introduction\n", "\n", "The article discusses how to open up a design system for everyone in an organization. The author, Anirudh Ramamohan, shares his experience with creating a process for proposing and implementing changes to the design system.\n", "\n", "### Key Points\n", "\n", "* Establishing a clear and easy-to-follow process for proposing and implementing changes\n", "* Empowering team members to suggest changes and gather feedback\n", "* Collaboration between designers from different disciplines during the consolidation process\n", "* Alignment with core values of collaboration, clarity, and trust\n", "\n", "### Process Overview\n", "\n", "1. Prepare a component case using a Figma playground file template\n", "2. Present on a consolidation call to critique and strengthen the proposal\n", "3. Implement changes by renewing the components page in Figma\n", "4. Communicate changes to the business and update relevant documents\n", "\n", "### Conclusion\n", "\n", "By following this process, everyone has an opportunity to contribute to the design system's evolution while aligning with its core values.\n", "\n", "**Author Information**\n", "--------------------\n", "\n", "* Name: Anirudh Ramamohan\n", "* Location: Barcelona\n", "* Bio: Turning messy ideas into beautiful products or posting blogs on them!" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_summary(\"https://medium.com/design-bootcamp/opening-up-the-design-system-for-everyone-11b9b92f3f75\")" ] }, { "cell_type": "code", "execution_count": null, "id": "16ec41bb-fd61-4ba8-b491-e75c6137be81", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.12" } }, "nbformat": 4, "nbformat_minor": 5 }