Reverted unintended changes to existing lab file and commit the new file in community-contributions folder
This commit is contained in:
Dimitris
511
week1/community-contributions/day2 EXERCISE_ollama_llama3.ipynb
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511
week1/community-contributions/day2 EXERCISE_ollama_llama3.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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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": null,
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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": null,
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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": null,
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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": null,
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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": null,
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"id": "479ff514-e8bd-4985-a572-2ea28bb4fa40",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# Let's just make sure the model is loaded\n",
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"\n",
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"!ollama pull 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": null,
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"id": "42b9f644-522d-4e05-a691-56e7658c0ea9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# If this doesn't work for any reason, try the 2 versions in the following cells\n",
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"# And double check the instructions in the 'Recap on installation of Ollama' at the top of this lab\n",
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"# And if none of that works - contact me!\n",
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"\n",
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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": [
|
||||
"# 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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{
|
||||
"cell_type": "code",
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"execution_count": null,
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||||
"id": "7745b9c4-57dc-4867-9180-61fa5db55eb8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"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": "a4704e10-f5fb-4c15-a935-f046c06fb13d",
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"metadata": {},
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"source": [
|
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"## Alternative approach - using OpenAI python library to connect to Ollama"
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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": "23057e00-b6fc-4678-93a9-6b31cb704bff",
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"# There's actually an alternative approach that some people might prefer\n",
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"# You can use the OpenAI client python library to call Ollama:\n",
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"\n",
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"from openai import OpenAI\n",
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"ollama_via_openai = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')\n",
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"\n",
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"response = ollama_via_openai.chat.completions.create(\n",
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" model=MODEL,\n",
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" messages=messages\n",
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")\n",
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"\n",
|
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"print(response.choices[0].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": "bc7d1de3-e2ac-46ff-a302-3b4ba38c4c90",
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||||
"metadata": {},
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"source": [
|
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"## Also trying the amazing reasoning model DeepSeek\n",
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"\n",
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"Here we use the version of DeepSeek-reasoner that's been distilled to 1.5B. \n",
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"This is actually a 1.5B variant of Qwen that has been fine-tuned using synethic data generated by Deepseek R1.\n",
|
||||
"\n",
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||||
"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!"
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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": "cf9eb44e-fe5b-47aa-b719-0bb63669ab3d",
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"jupyter": {
|
||||
"outputs_hidden": true
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!ollama pull deepseek-r1:1.5b"
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||||
]
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1d3d554b-e00d-4c08-9300-45e073950a76",
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"# This may take a few minutes to run! You should then see a fascinating \"thinking\" trace inside <think> tags, followed by some decent definitions\n",
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"\n",
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||||
"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)"
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1622d9bb-5c68-4d4e-9ca4-b492c751f898",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NOW the exercise for you\n",
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||||
"\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": "markdown",
|
||||
"id": "ffaa3470-884c-467e-b4ce-c1b8d39294da",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"This is the code from day 1 notebook. Here we create the class to extract the text from the website, using BeautifulSoup library, and the we execute it to see the the results"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8d8c9f01-ca12-4018-b7fa-698c9fa1aa93",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# A class to represent a Webpage\n",
|
||||
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
|
||||
"\n",
|
||||
"# 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",
|
||||
"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)"
|
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]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6fd198df-bac5-42c5-83a0-06c5f71fb76a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Let's try one out. Change the website and add print statements to follow along.\n",
|
||||
"\n",
|
||||
"ed = Website(\"https://edwarddonner.com\")\n",
|
||||
"print(ed.title)\n",
|
||||
"print(ed.text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "995b637d-a5db-4ad9-ac78-5980fd7ef112",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Define the system prompt, to instruct the model how we want to respond to our query. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ee810d49-e88a-4137-a4be-98812e0d0748",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Define our system prompt - you can experiment with this later, changing the last sentence to 'Respond in markdown in Spanish.\"\n",
|
||||
"\n",
|
||||
"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": null,
|
||||
"id": "482b5d4c-69ed-4332-abb5-8b0986dcf368",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# A function that writes a User Prompt that asks for summaries of websites:\n",
|
||||
"\n",
|
||||
"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": "d966cb09-3ca2-49f7-8462-f6ef26c01159",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print(user_prompt_for(ed))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2f9be84f-4cd7-4ce7-8f33-e60d16f02852",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# For test purpose\n",
|
||||
"\n",
|
||||
"messages = [\n",
|
||||
" {\"role\": \"system\", \"content\": \"You are a snarky assistant\"},\n",
|
||||
" {\"role\": \"user\", \"content\": \"What is 2 + 2?\"}\n",
|
||||
"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f5cb0e9f-eb56-4633-ba4c-76817be98856",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# To give you a preview -- calling ollama with system and user messages:\n",
|
||||
"\n",
|
||||
"import ollama\n",
|
||||
"\n",
|
||||
"response = ollama.chat(model=MODEL, messages=messages)\n",
|
||||
"print(response['message']['content'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c554903f-eb04-4a16-87fc-f1d9ff58f6d9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# See how this function creates exactly the format above\n",
|
||||
"\n",
|
||||
"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": null,
|
||||
"id": "6b64b814-123f-436d-9366-4c762ac4b89a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Try this out, and then try for a few more websites\n",
|
||||
"\n",
|
||||
"messages_for(ed)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d1ef4be2-ef3a-4b5d-8d18-f2eafa9d6a93",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### So, here let's run the summarize by using ollama and see how appears."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7c46edc5-c85d-4ad0-89fd-39c4fdc44a5d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# And now: call the ollama API. \n",
|
||||
"\n",
|
||||
"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": null,
|
||||
"id": "466c2f78-91ca-4ed2-b60b-40661d0b6f68",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"summarize(\"https://edwarddonner.com\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7ab7c9a1-70fd-421c-be06-c36eb6c9aedf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# A function to display this nicely in the Jupyter output, using markdown\n",
|
||||
"\n",
|
||||
"def display_summary(url):\n",
|
||||
" summary = summarize(url)\n",
|
||||
" display(Markdown(summary))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1cedc9d9-6a76-4225-82c1-82240da16260",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"display_summary(\"https://edwarddonner.com\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "82c48586-33c8-4797-a24f-41602c1297b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "llms",
|
||||
"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": "3.11.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in New Issue
Block a user