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diff --git a/.gitignore b/.gitignore
index 5323911..1bee055 100644
--- a/.gitignore
+++ b/.gitignore
@@ -240,3 +240,6 @@ cython_debug/
.idea/
*.pyc
+.sesskey
+
+*.html
diff --git a/README.md b/README.md
index 254ad96..1511cbf 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,8 @@
# Large Language Models and Generative AI for NLP
+**THIS REPOSITORY WILL EVOLVE OVER THE DURATION OF THE COURSE. WE WILL ADD CONTENT AS WE GO.**
+
+
**Time:** Fall 2024 / Period 2\
**Target group:** Master's students\
**Teachers:**
@@ -17,20 +20,26 @@ This hands-on course delves into the world of Large Language Models (LLMs) and t
## Evaluation
-* Weekly lab exercise submissions (25%)
-* Final group project submission (75%)
+The course will be evaluated based on the submission of a final report.
+
+Students will need to submit a final report that covers all the labs:
+
+What was done in each lab?
+What was the motivation behind your solutions?
+What did you learn?
+Challenges you encountered?
## Syllabus
| Week | Dates | Topic / Lecture | Format | Teacher |
|------|-----------|--------------------------------------------------------------------------|-------------------------------------------------------------------------------------|---------|
-| 1 | 29/31.10. | [Introduction to Generative AI and Large Language Models (LLM)](week-1/) | 45 min lecture and 45 min coding lab | Aarne |
-| 2 | 05/07.11. | [Using LLMs and Prompting-based approaches](week-2/) | 45 min lecture and 45 min coding lab | Aarne |
-| 3 | 12/14.11. | [Evaluating LLMs](week-3/) | 45 min lecture and 45 min coding lab | Jussi |
-| 4 | 9/21.11. | [Fine-tuning LLMs](week-4/) | 45 min lecture and 45 min coding lab | Aarne |
-| 5 | 26/28.11. | [Retrieval Augmented Generation (RAG)](week-5/) | [45 min lecture](https://www.youtube.com/watch?v=1GtBArPD-UA) and 45 min coding lab | Dmitry |
-| 6 | 03/05.12. | [Use cases and applications of LLMs](week-6/) | [45 min lecture](https://www.youtube.com/watch?v=8LkR35wNZnU) and 45 min coding lab | Dmitry |
-| 7 | 10/12.12. | [Group project presentations](week-7/) | Student project presentations | Aarne |
+| 1 | 29/31.10. | [Introduction to Generative AI and Large Language Models (LLM)](week-1/) | 90 min lecture and 90 min lab | Aarne |
+| 2 | 05/07.11. | [Using LLMs and Prompting-based approaches](week-2/) | 90 min lecture and 90 min coding lab | Aarne |
+| 3 | 12/14.11. | [Evaluating LLMs](week-3/) | 90 min lecture and 90 min coding lab | Jussi |
+| 4 | 19/21.11. | [Fine-tuning LLMs](week-4/) | 90 min lecture and 90 min coding lab | Aarne |
+| 5 | 26/28.11. | [Retrieval Augmented Generation (RAG)](week-5/) | [90 min lecture](https://www.youtube.com/watch?v=1GtBArPD-UA) and 90 min coding lab | Dmitry |
+| 6 | 03/05.12. | [Use cases and applications of LLMs](week-6/) | [90 min lecture](https://www.youtube.com/watch?v=8LkR35wNZnU) and 90 min coding lab | Dmitry |
+| 7 | 10/12.12. | [Final report preparation](week-7/) | Student work on their final report | Aarne |
### Detailed Syllabus:
@@ -38,7 +47,7 @@ This hands-on course delves into the world of Large Language Models (LLMs) and t
**Week 1: Introduction to Generative AI and Large Language Models (LLM)**
* Overview of Generative AI and its applications in NLP
* Introduction to Large Language Models (LLMs) and their architecture
-* Hands-on lab: Getting started with LLMs using popular libraries
+* Lab: Learn about tokenizers
**Week 2: Using LLMs and Prompting-based approaches**
* Understanding prompt engineering and its importance in working with LLMs
@@ -65,10 +74,10 @@ This hands-on course delves into the world of Large Language Models (LLMs) and t
* Discussing the potential impact of LLMs on different industries
* Hands-on lab: query tables and generate synthetic data
-**Week 7: Group project presentations**
-* Students present their final group projects plan, showcasing their understanding and application of LLMs in NLP.
+**Week 7: Final report preparation**
+* Students work on their final reports, showcasing their understanding of the labs and the concepts learned.
**Group Project submission**
-* Final projects are submitted by 31st December 2024
+* Final reports are submitted by 31st December 2024
-Note: This syllabus is subject to change at the discretion of the instructors. Any modifications will be communicated to the students in a timely manner.
\ No newline at end of file
+Note: This syllabus is subject to change at the discretion of the instructors. Any modifications will be communicated to the students in a timely manner.
diff --git a/week-1/LLM-Course Lecture 1.pdf b/week-1/LLM-Course Lecture 1.pdf
new file mode 100644
index 0000000..40ee8f1
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diff --git a/week-1/README.md b/week-1/README.md
index 9b5de99..1b47d7e 100644
--- a/week-1/README.md
+++ b/week-1/README.md
@@ -1 +1,16 @@
-# Introduction to Generative AI and Large Language Models (LLM)
\ No newline at end of file
+# Introduction to Generative AI and Large Language Models (LLM)
+
+Week 1 is the most theory-heavy week of the course. You can find the lecture slides here: [Week 1 Slides](https://github.com/Helsinki-NLP/LLM-course-2024/blob/main/week-1/LLM-Course%20Lecture%201.pdf).
+
+## Lab Assignment
+
+Research on Tokenizers and write a section to your final report reflecting on the following questions:
+* What are tokenizers?
+* Why are they important for language modeling and LLMs?
+* What different tokenization algorithms are there and which ones are the most popular ones and why?
+
+**Some references:**
+* Neural Machine Translation of Rare Words with Subword Units:
+[https://arxiv.org/abs/1508.07909](https://arxiv.org/abs/1508.07909)
+* SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing:
+[https://arxiv.org/abs/1808.06226](https://arxiv.org/abs/1808.06226)
diff --git a/week-2/.DS_Store b/week-2/.DS_Store
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diff --git a/week-2/LLM-Course Lecture 2.pdf b/week-2/LLM-Course Lecture 2.pdf
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diff --git a/week-2/README.md b/week-2/README.md
index 310774e..4dd170e 100644
--- a/week-2/README.md
+++ b/week-2/README.md
@@ -1 +1,34 @@
-# Using LLMs and Prompting-based approaches
\ No newline at end of file
+# Using LLMs and Prompting-based approaches
+
+## Lecture
+
+Slides can be found here: [Week 2 Slides](https://github.com/Helsinki-NLP/LLM-course-2024/blob/main/week-2/LLM-Course%20Lecture%202.pdf).
+
+
+## Preparation for the lab
+
+**Gemini API Key**
+* Use your existing google account or create a new free account
+* Go to https://aistudio.google.com/apikey
+* Get your API key and store it in a safe place
+
+**Create a HuggingFace account**
+* https://huggingface.co/join
+
+**Set up Python & Jupyter environment**
+* I use Python 3.12 for running code locally
+* For inference or fine-tuning requiring GPU, I use Google Colab: https://colab.research.google.com/
+
+
+## Lab Exercise
+
+For this exercise use the gemini-chatbot and/or prompting-notebook found in GitHub under week-2 folder.
+Select a domain (e.g. finance, sports, cooking), tone of voice, style and persona (e.g. a pirate) and a question/task you want to accomplish (e.g. write a blog post)
+* Modify the gemini-chatbot and test the different prompting approaches discussed in the lecture to achieve the task.
+* Do the same for prompting-notebook (run this in Google Colab using a T4 GPU backend)
+* Write a section to your report explaining what you did and what were your findings. Which prompting approach worked the best and why?
+
+Modify the in-context-learning notebook (you can run this locally or in Google Colab)
+* Modify the prompt to change the style of the output to be a table with strengths and weaknesses in separate columns. (Markdown printing should show the table correctly. If you have time, modify the html printing to show the updated style as a table).
+* If you have time: modify the notebook to use an open source model from Hugging Face instead of Gemini
+
diff --git a/week-2/gemini-chatbot/basic_chatbot.py b/week-2/gemini-chatbot/basic_chatbot.py
new file mode 100644
index 0000000..cac78fc
--- /dev/null
+++ b/week-2/gemini-chatbot/basic_chatbot.py
@@ -0,0 +1,61 @@
+from fasthtml.common import *
+import google.generativeai as genai
+import strip_markdown
+import configparser
+
+API_KEY = os.environ.get("GEMINI_API_KEY")
+genai.configure(api_key=API_KEY)
+LLM = "gemini-1.5-flash"
+model = genai.GenerativeModel(LLM)
+
+# Read system prompts from config file
+prompts = configparser.ConfigParser()
+prompts.read('prompts.env')
+
+# Set system prompt
+#system_prompt = prompts.get("SYSTEM_PROMPTS", "IT_HELPDESK")
+system_prompt = f'Summarize the following text about {prompts.get("TEMPLATES", "TOPIC")} in {prompts.get("TEMPLATES", "NUMBER")} bullet points:'
+
+# Set up the app, including daisyui and tailwind for the chat component
+hdrs = (picolink, Script(src="https://cdn.tailwindcss.com"),
+ Link(rel="stylesheet", href="https://cdn.jsdelivr.net/npm/daisyui@4.11.1/dist/full.min.css"))
+app = FastHTML(hdrs=hdrs, cls="p-4 max-w-lg mx-auto")
+
+# Chat message component (renders a chat bubble)
+def ChatMessage(msg, user):
+ bubble_class = "chat-bubble-primary" if user else 'chat-bubble-secondary'
+ chat_class = "chat-end" if user else 'chat-start'
+ return Div(cls=f"chat {chat_class}")(
+ Div('user' if user else 'assistant', cls="chat-header"),
+ Div(msg, cls=f"chat-bubble {bubble_class}"),
+ Hidden(msg, name="messages")
+ )
+
+# The input field for the user message.
+def ChatInput():
+ return Input(name='msg', id='msg-input', placeholder="Type a message",
+ cls="input input-bordered w-full", hx_swap_oob='true')
+
+# The main screen
+@app.get
+def index():
+ page = Form(hx_post=send, hx_target="#chatlist", hx_swap="beforeend")(
+ Div(id="chatlist", cls="chat-box h-[73vh] overflow-y-auto"),
+ Div(cls="flex space-x-2 mt-2")(
+ Group(ChatInput(), Button("Send", cls="btn btn-primary"))
+ )
+ )
+ return Titled('Simple chatbot demo', page)
+
+# Handle the form submission
+@app.post
+def send(msg:str, messages:list[str]=None):
+ if not messages: messages = [system_prompt]
+ messages.append(msg.rstrip())
+ r = model.generate_content(messages).text
+ return (ChatMessage(msg, True), # The user's message
+ ChatMessage(strip_markdown.strip_markdown(r.rstrip()), False), # The chatbot's response
+ ChatInput()) # And clear the input field
+
+serve()
+
diff --git a/week-2/gemini-chatbot/gemini_prompting.ipynb b/week-2/gemini-chatbot/gemini_prompting.ipynb
new file mode 100644
index 0000000..3bf8496
--- /dev/null
+++ b/week-2/gemini-chatbot/gemini_prompting.ipynb
@@ -0,0 +1,223 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Prompting with Gemini \n",
+ "Notebook for experimenting different promting techniques with Gemini"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Install the required dependencies with the following command."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install google-generativeai"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Import the required libraries."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import google.generativeai as genai\n",
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Insert your Gemini API key to the following command and import the Gemini model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# API_KEY = os.environ.get(\"GEMINI_API_KEY\")\n",
+ "API_KEY = \"\" # insert API key in this function call with your own API key from aistudio.google.com\n",
+ "genai.configure(api_key=API_KEY) \n",
+ "\n",
+ "LLM = \"gemini-1.5-flash\"\n",
+ "model = genai.GenerativeModel(LLM)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Modify the `system_prompt` to experiment with different prompting approaches."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "system_prompt = \"Hello! You are a helpful and concise assistant.\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We create a list of messages so that we keep history in the context. If you want to clear the messages later in this notebook, add the line `messages = []` to a new line cell in the notebook. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = []\n",
+ "messages.append(system_prompt)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Get the response from Gemini model by providing the prompt in a messages list to it."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = model.generate_content(messages).text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Print the output of the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(r)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's define a new prompt."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "user_input = \"Help me with prompting. What are the different promt engineering techniques?\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's add athe new prompt to the messages list."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages.append(user_input)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Again we generate the output with the Gemini model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "r = model.generate_content(messages).text"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "... and print the output."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(r)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "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": "3.12.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week-2/gemini-chatbot/prompts.env b/week-2/gemini-chatbot/prompts.env
new file mode 100644
index 0000000..d92ef9b
--- /dev/null
+++ b/week-2/gemini-chatbot/prompts.env
@@ -0,0 +1,7 @@
+[SYSTEM_PROMPTS]
+BASIC_PROMPT = "You are a helpful and concise assistant."
+IT_HELPDESK = "You are a overly polite IT help desk agent."
+
+[TEMPLATES]
+TOPIC = "prompt engineering"
+NUMBER = 5
\ No newline at end of file
diff --git a/week-2/gemini-chatbot/requirements.txt b/week-2/gemini-chatbot/requirements.txt
new file mode 100644
index 0000000..8b4b37f
--- /dev/null
+++ b/week-2/gemini-chatbot/requirements.txt
@@ -0,0 +1,4 @@
+python-fasthtml
+uvicorn>=0.29
+google-generativeai
+strip_markdown
\ No newline at end of file
diff --git a/week-2/in-context-learning/in-context-learning.ipynb b/week-2/in-context-learning/in-context-learning.ipynb
new file mode 100644
index 0000000..061b8d6
--- /dev/null
+++ b/week-2/in-context-learning/in-context-learning.ipynb
@@ -0,0 +1,226 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# In-Context Learning\n",
+ "\n",
+ "\n",
+ "In-context learning is a generalisation of few-shot learning where the LLM is provided a context as part of the prompt and asked to respond by utilising the information in the context.\n",
+ "\n",
+ "* Example: *\"Summarize this research article into one paragraph highlighting its strengths and weaknesses: [insert article text]”*\n",
+ "* Example: *\"Extract all the quotes from this text and organize them in alphabetical order: [insert text]”*\n",
+ "\n",
+ "A very popular technique that you will learn in week 5 called Retrieval-Augmented Generation (RAG) is a form of in-context learning, where:\n",
+ "* a search engine is used to retrieve some relevant information\n",
+ "* that information is then provided to the LLM as context\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this example we download some recent research papers from arXiv papers, extract the text from the PDF files and ask Gemini to summarize the articles as well as provide the main strengths and weaknesses of the papers. Finally we print the summaries to a local html file and as markdown."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import requests\n",
+ "from bs4 import BeautifulSoup\n",
+ "import google.generativeai as genai\n",
+ "from urllib.request import urlopen, urlretrieve\n",
+ "from IPython.display import Markdown, display\n",
+ "from pypdf import PdfReader\n",
+ "from datetime import date\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "API_KEY = os.environ.get(\"GEMINI_API_KEY\")\n",
+ "genai.configure(api_key=API_KEY)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We select those papers that have been featured in Hugging Face papers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "BASE_URL = \"https://huggingface.co/papers\"\n",
+ "page = requests.get(BASE_URL)\n",
+ "soup = BeautifulSoup(page.content, \"html.parser\")\n",
+ "h3s = soup.find_all(\"h3\")\n",
+ "\n",
+ "papers = []\n",
+ "\n",
+ "for h3 in h3s:\n",
+ " a = h3.find(\"a\")\n",
+ " title = a.text\n",
+ " link = a[\"href\"].replace('/papers', '')\n",
+ "\n",
+ " papers.append({\"title\": title, \"url\": f\"https://arxiv.org/pdf{link}\"})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Code to extract text from PDFs."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract_paper(url):\n",
+ " html = urlopen(url).read()\n",
+ " soup = BeautifulSoup(html, features=\"html.parser\")\n",
+ "\n",
+ " # kill all script and style elements\n",
+ " for script in soup([\"script\", \"style\"]):\n",
+ " script.extract() # rip it out\n",
+ "\n",
+ " # get text\n",
+ " text = soup.get_text()\n",
+ "\n",
+ " # break into lines and remove leading and trailing space on each\n",
+ " lines = (line.strip() for line in text.splitlines())\n",
+ " # break multi-headlines into a line each\n",
+ " chunks = (phrase.strip() for line in lines for phrase in line.split(\" \"))\n",
+ " # drop blank lines\n",
+ " text = '\\n'.join(chunk for chunk in chunks if chunk)\n",
+ "\n",
+ " return text\n",
+ "\n",
+ "\n",
+ "def extract_pdf(url):\n",
+ " pdf = urlretrieve(url, \"pdf_file.pdf\")\n",
+ " reader = PdfReader(\"pdf_file.pdf\")\n",
+ " text = \"\"\n",
+ " for page in reader.pages:\n",
+ " text += page.extract_text() + \"\\n\"\n",
+ " return text\n",
+ "\n",
+ "\n",
+ "def printmd(string):\n",
+ " display(Markdown(string))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "LLM = \"gemini-1.5-flash\"\n",
+ "model = genai.GenerativeModel(LLM)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use Gemini to summarize the papers."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for paper in tqdm(papers):\n",
+ " try:\n",
+ " paper[\"summary\"] = model.generate_content(\"Summarize this research article into one paragraph without formatting highlighting its strengths and weaknesses. \" + extract_pdf(paper[\"url\"])).text\n",
+ " except:\n",
+ " print(\"Generation failed\")\n",
+ " paper[\"summary\"] = \"Paper not available\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We print the results to a html file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "page = f\" Daily Dose of AI Research
{date.today()}
Summaries generated with: {LLM}\"\n",
+ "with open(\"papers.html\", \"w\") as f:\n",
+ " f.write(page)\n",
+ "for paper in papers:\n",
+ " page = f'
{paper[\"summary\"]}
'\n",
+ " with open(\"papers.html\", \"a\") as f:\n",
+ " f.write(page)\n",
+ "end = \" \"\n",
+ "with open(\"papers.html\", \"a\") as f:\n",
+ " f.write(end)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can also print the results to this notebook as markdown."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "for paper in papers:\n",
+ " printmd(\"**[{}]({})**
{}
\".format(paper[\"title\"],\n",
+ " paper[\"url\"],\n",
+ " paper[\"summary\"]))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "venv",
+ "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": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/week-2/in-context-learning/requirements.txt b/week-2/in-context-learning/requirements.txt
new file mode 100644
index 0000000..15da081
--- /dev/null
+++ b/week-2/in-context-learning/requirements.txt
@@ -0,0 +1,4 @@
+requests
+bs4
+google-generativeai
+pypdf
\ No newline at end of file
diff --git a/week-2/prompting-notebook/prompting.ipynb b/week-2/prompting-notebook/prompting.ipynb
new file mode 100644
index 0000000..eca19fd
--- /dev/null
+++ b/week-2/prompting-notebook/prompting.ipynb
@@ -0,0 +1,6574 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Prompting a Large Language Model\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is an example notebook for running an open source model from Hugging Face. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%pip install -r requirements.txt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We use the [Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) in this notebook due to its small size. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "torch.random.manual_seed(0)\n",
+ "\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " \"microsoft/Phi-3.5-mini-instruct\", \n",
+ " device_map=\"cuda\", # change to cpu is running locally\n",
+ " torch_dtype=\"auto\", \n",
+ " trust_remote_code=True, \n",
+ ")\n",
+ "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/Phi-3.5-mini-instruct\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "An example few-shot prompt:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "messages = [\n",
+ " {\"role\": \"system\", \"content\": \"You are a helpful AI assistant.\"},\n",
+ " {\"role\": \"user\", \"content\": \"Can you provide ways to eat combinations of bananas and dragonfruits?\"},\n",
+ " {\"role\": \"assistant\", \"content\": \"Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey.\"},\n",
+ " {\"role\": \"user\", \"content\": \"What about solving an 2x + 3 = 7 equation?\"},\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pipe = pipeline(\n",
+ " \"text-generation\",\n",
+ " model=model,\n",
+ " tokenizer=tokenizer,\n",
+ ")\n",
+ "\n",
+ "generation_args = {\n",
+ " \"max_new_tokens\": 500,\n",
+ " \"return_full_text\": False,\n",
+ " \"temperature\": 0.0,\n",
+ " \"do_sample\": False,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "output = pipe(messages, **generation_args)\n",
+ "print(output[0]['generated_text'])"
+ ]
+ }
+ ],
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+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
+ },
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diff --git a/week-2/prompting-notebook/requirements.txt b/week-2/prompting-notebook/requirements.txt
new file mode 100644
index 0000000..292dcb3
--- /dev/null
+++ b/week-2/prompting-notebook/requirements.txt
@@ -0,0 +1,5 @@
+transformers
+torch
+sentencepiece
+ipywidgets
+accelerate
diff --git a/week-3/README.md b/week-3/README.md
index c6a38c7..2bebc73 100644
--- a/week-3/README.md
+++ b/week-3/README.md
@@ -1 +1,23 @@
-# Evaluating LLMs
\ No newline at end of file
+# Evaluating LLMs
+
+Slides can be found [here](https://docs.google.com/presentation/d/1NB7AxwV8mIaJbh9a93yPPCN2PFskLdZ_we0psqrCIJc/edit?usp=sharing).
+
+## Lab Assignment
+
+1. Pick a topical area of some importance:
+* legal advice (wills, prenuptial agreements, e.g.)
+* medical or dietary advice
+* mental health and wellbeing
+* relationships
+* financial advice (pensions, crypto, saving for a down payment, gambling)
+* tell a joke on some theme
+
+2. Pick a contrast pair of languages, sociolects, dialects, or politeness
+
+3. Pick a pair of LLMs readily accessible to you (variants are a good idea!)
+4. Experiment to see if you are able to construct a pair of equivalent but stylistically different prompts that provoke at least one of the LLMs to deliver stylistically different but topically different responses.
+
+5. Remember this over the course of this class and retest with new models!
+
+6. This may become a baseline report for ELOQUENT!
+
diff --git a/week-4/.DS_Store b/week-4/.DS_Store
new file mode 100644
index 0000000..5008ddf
Binary files /dev/null and b/week-4/.DS_Store differ
diff --git a/week-4/LLM-Course Lecture 4.pdf b/week-4/LLM-Course Lecture 4.pdf
new file mode 100644
index 0000000..d3780ac
Binary files /dev/null and b/week-4/LLM-Course Lecture 4.pdf differ
diff --git a/week-4/README.md b/week-4/README.md
index ab338e4..9294b5d 100644
--- a/week-4/README.md
+++ b/week-4/README.md
@@ -1 +1,13 @@
-# Fine-tuning LLMs
\ No newline at end of file
+# Fine-tuning LLMs
+
+**Lecture slides:** [LLM-Course Lecture 4](https://github.com/Helsinki-NLP/LLM-course-2024/blob/main/week-4/LLM-Course%20Lecture%204.pdf)
+
+## Lab Exercise
+
+1. Run the `supervised_finetuning.ipynb` notebook in Google Colab.
+2. Change the base model used (search for small <7B parameter models in Hugging Face).
+3. Change the dataset used in fine-tuning.
+4. **Bonus challenge:**
+ * Change the fine-tuning method from supervised fine-tuning to DPO.
+ * Change the code accordingly, see: [Hugging Face DPO Trainer Documentation](https://huggingface.co/docs/trl/en/dpo_trainer)
+ * Select an appropriate DPO dataset. Search Hugging Face Datasets.
diff --git a/week-4/supervised_finetuning.ipynb b/week-4/supervised_finetuning.ipynb
index ff2df42..7d5cfd0 100644
--- a/week-4/supervised_finetuning.ipynb
+++ b/week-4/supervised_finetuning.ipynb
@@ -1,7073 +1,7077 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Fine-tuning a Large Language Model\n",
- "\n",
- "In this lecture we will be looking at how to fine-tune an existing pre-trained language model.\n",
- "\n",
- "## Learning outcomes\n",
- "* You will learn how to download a pre-trained model and a training dataset from Hugging Face.\n",
- "* You will learn how to fine-tune the downloaded model with the dataset using Hugging Face trl library and the supervised fine-tuning (SFT) method.\n",
- "* You will learn how to use the fine-tuned model to generate text based on user input / prompts.\n",
- "* You will learn how to upload the fine-tuned model to your own Hugging Face repository so that it can be used later or shared with other users.\n",
- "\n",
- "## Prerequistes\n",
- "* You will need the following free accounts: Google, Hugging Face and Weights & Biases. You may use your existing accounts or create new accounts for the purposes of this course.\n",
- "* We will use the [Hugging Face](https://huggingface.co/) libraries: transformers (for models), datasets (for datasets), trl (for training). We will also store the fine-tuned models in a Hugging Face repository.\n",
- "* Training is done using [Google Colab](https://colab.research.google.com/), which provides free access to Jupyter notebooks backed with a GPU compute required for fine-tuning.\n",
- "* For monitoring the training run we will use [Weights & Biases](https://wandb.ai/)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Fine-tuning"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's first install some pre-requisites using Python's package manager pip"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "lioAFETZWlKi",
- "outputId": "32dc1efe-d08c-435c-bd13-eb5cb362cf30"
- },
- "outputs": [],
- "source": [
- "!pip install transformers peft accelerate\n",
- "!pip install -q trl xformers wandb datasets einops sentencepiece bitsandbytes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then we need to import the required libraries"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "id": "NdjM7XroWqEx"
- },
- "outputs": [],
- "source": [
- "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, TextStreamer\n",
- "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training\n",
- "import torch, wandb\n",
- "from datasets import load_dataset\n",
- "from trl import SFTTrainer\n",
- "from huggingface_hub import notebook_login"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We will download a pre-trained large language model from Hugging Face and a dataset to train the model with. Below we assign these to variables we will use later. We will also set the name of the repository and model for the fine-tuned model."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "id": "jTq15hgUW8qB"
- },
- "outputs": [],
- "source": [
- "# Pre trained model\n",
- "model_name = \"mistralai/Mistral-7B-v0.3\"\n",
- "\n",
- "# Dataset name\n",
- "dataset_name = \"vicgalle/alpaca-gpt4\"\n",
- "\n",
- "# Hugging face repository link to save fine-tuned model(Create new repository in huggingface,copy and paste here)\n",
- "new_model = \"talman-fi/Llama2-7b-hf-finetune\""
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "To access your Hugging Face account, you need to log in. First go to your Hugging Face account, click *Settings* and select *Access Tokens*. Create a new token and copy the token. Then execute the below login command and when asked paste an access token. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 145,
- "referenced_widgets": [
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- },
- "id": "xnwXVf_dXeIp",
- "outputId": "02bd913d-2331-4968-821f-330f4df6baf5"
- },
- "outputs": [],
- "source": [
- "notebook_login()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's then download a subset of the dataset we want to use. Below we limit the dataset to the first 10,000 examples in order to save time. In real life you would probably use the full dataset."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 342,
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- },
- "id": "Bt_36Xa5Xuub",
- "outputId": "5c3b2662-6de6-4aa3-90f6-21346273dac6"
- },
- "outputs": [],
- "source": [
- "dataset = load_dataset(dataset_name, split=\"train[0:10000]\")\n",
- "dataset[\"text\"][0]"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Let's then download the model. We first create a config object for quantization of the model using bitsandbytes. Bitsandbytes enables accessible large language models via k-bit quantization for PyTorch.\n",
- "\n",
- "We also need to download the tokenizer."
- ]
- },
- {
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- },
- "id": "ecbQfx6mYpSU",
- "outputId": "91338af7-f6ab-4422-e71e-b1817e111283"
- },
- "outputs": [],
- "source": [
- "bnb_config = BitsAndBytesConfig(\n",
- " load_in_4bit= True,\n",
- " bnb_4bit_quant_type= \"nf4\",\n",
- " bnb_4bit_compute_dtype= torch.float16,\n",
- " bnb_4bit_use_double_quant= False,\n",
- ")\n",
- "model = AutoModelForCausalLM.from_pretrained(\n",
- " model_name,\n",
- " quantization_config=bnb_config,\n",
- " device_map={\"\": 0}\n",
- ")\n",
- "model = prepare_model_for_kbit_training(model)\n",
- "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
- "model.config.pretraining_tp = 1\n",
- "\n",
- "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
- "tokenizer.pad_token = tokenizer.eos_token\n",
- "tokenizer.add_eos_token = True\n",
- "tokenizer.add_bos_token, tokenizer.add_eos_token"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Below we set the access token to Waights & Biases. You should copy your access token from your account at [https://wandb.ai](https://wandb.ai)."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 208
- },
- "id": "ow0n2oSOYz6V",
- "outputId": "737f4b26-2f94-42b3-d623-2f0961130547"
- },
- "outputs": [],
- "source": [
- "#monitering login\n",
- "wandb.login(key=\"05efcdf852a97c2354d463fef34d65ba3dcbf203\")\n",
- "run = wandb.init(project='Fine tuning mistral 7B', job_type=\"training\", anonymous=\"allow\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then we'll create a configuration for the lo-rank adaptation method we will use. "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "id": "D409OdMjcc-n"
- },
- "outputs": [],
- "source": [
- "peft_config = LoraConfig(\n",
- " lora_alpha=8,\n",
- " lora_dropout=0.1,\n",
- " r=16,\n",
- " bias=\"none\",\n",
- " task_type=\"CAUSAL_LM\",\n",
- " target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\"]\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We need to set the training arguments for the training run."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "id": "n9sAp50mc4LR"
- },
- "outputs": [],
- "source": [
- "training_arguments = TrainingArguments(\n",
- " output_dir=\"./results\",\n",
- " num_train_epochs=1,\n",
- " per_device_train_batch_size=8,\n",
- " gradient_accumulation_steps=2,\n",
- " optim=\"paged_adamw_8bit\",\n",
- " save_steps=1000,\n",
- " logging_steps=30,\n",
- " learning_rate=2e-4,\n",
- " weight_decay=0.001,\n",
- " fp16=False,\n",
- " bf16=False,\n",
- " max_grad_norm=0.3,\n",
- " max_steps=-1,\n",
- " warmup_ratio=0.3,\n",
- " group_by_length=True,\n",
- " lr_scheduler_type=\"linear\",\n",
- " report_to=\"wandb\",\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Finally we create the trainer object that uses supervised fine-tuning (SFT) as the training method."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 243,
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- },
- "id": "7JdMo0sqc_SB",
- "outputId": "b5e04401-e48c-4018-f032-7f8f9e2d15a2"
- },
- "outputs": [],
- "source": [
- "# Setting sft parameters\n",
- "trainer = SFTTrainer(\n",
- " model=model,\n",
- " train_dataset=dataset,\n",
- " peft_config=peft_config,\n",
- " max_seq_length= None,\n",
- " dataset_text_field=\"text\",\n",
- " tokenizer=tokenizer,\n",
- " args=training_arguments,\n",
- " packing=False,\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Then, we can execute the training run. This will approximately 8 hours using the T4 GPU available in Colab and the dataset of 10,000 samples we downloaded."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/",
- "height": 214
- },
- "id": "pEiMWxk9dFLk",
- "outputId": "842c435d-8eee-41a6-edba-c6c88ba9617e"
- },
- "outputs": [],
- "source": [
- "# Train model\n",
- "trainer.train()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "UZg7GbvHdOUc"
- },
- "outputs": [],
- "source": [
- "# Save the fine-tuned model\n",
- "trainer.model.save_pretrained(new_model)\n",
- "wandb.finish()\n",
- "model.config.use_cache = True\n",
- "model.eval()"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "def stream(user_prompt):\n",
- " runtimeFlag = \"cuda:0\"\n",
- " system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n'\n",
- " B_INST, E_INST = \"### Instruction:\\n\", \"### Response:\\n\"\n",
- " prompt = f\"{system_prompt}{B_INST}{user_prompt.strip()}\\n\\n{E_INST}\"\n",
- " inputs = tokenizer([prompt], return_tensors=\"pt\").to(runtimeFlag)\n",
- " streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
- " _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "stream(\"what is newtons 3rd law and its formula\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "base_model = AutoModelForCausalLM.from_pretrained(\n",
- " model_name, low_cpu_mem_usage=True,\n",
- " return_dict=True,torch_dtype=torch.float16,\n",
- " device_map= {\"\": 0})\n",
- "model = PeftModel.from_pretrained(base_model, new_model)\n",
- "model = model.merge_and_unload()\n",
- "\n",
- "# Reload tokenizer\n",
- "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
- "tokenizer.pad_token = tokenizer.eos_token\n",
- "tokenizer.padding_side = \"right\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "model.push_to_hub(new_model)\n",
- "tokenizer.push_to_hub(new_model)"
- ]
- }
- ],
- "metadata": {
- "accelerator": "GPU",
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Fine-tuning a Large Language Model\n",
+ "\n",
+ "In this lecture we will be looking at how to fine-tune an existing pre-trained language model.\n",
+ "\n",
+ "## Learning outcomes\n",
+ "* You will learn how to download a pre-trained model and a training dataset from Hugging Face.\n",
+ "* You will learn how to fine-tune the downloaded model with the dataset using Hugging Face trl library and the supervised fine-tuning (SFT) method.\n",
+ "* You will learn how to use the fine-tuned model to generate text based on user input / prompts.\n",
+ "* You will learn how to upload the fine-tuned model to your own Hugging Face repository so that it can be used later or shared with other users.\n",
+ "\n",
+ "## Prerequistes\n",
+ "* You will need the following free accounts: Google, Hugging Face and Weights & Biases. You may use your existing accounts or create new accounts for the purposes of this course.\n",
+ "* We will use the [Hugging Face](https://huggingface.co/) libraries: transformers (for models), datasets (for datasets), trl (for training). We will also store the fine-tuned models in a Hugging Face repository.\n",
+ "* Training is done using [Google Colab](https://colab.research.google.com/), which provides free access to Jupyter notebooks backed with a GPU compute required for fine-tuning.\n",
+ "* For monitoring the training run we will use [Weights & Biases](https://wandb.ai/)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Fine-tuning"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's first install some pre-requisites using Python's package manager pip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
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+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "lioAFETZWlKi",
+ "outputId": "32dc1efe-d08c-435c-bd13-eb5cb362cf30"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install transformers peft accelerate\n",
+ "!pip install -U datasets\n",
+ "!pip install -q trl xformers wandb einops sentencepiece bitsandbytes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we need to import the required libraries"
+ ]
+ },
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+ "execution_count": 2,
+ "metadata": {
+ "id": "NdjM7XroWqEx"
+ },
+ "outputs": [],
+ "source": [
+ "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TrainingArguments, TextStreamer\n",
+ "from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training\n",
+ "import torch, wandb\n",
+ "from datasets import load_dataset\n",
+ "from trl import SFTTrainer\n",
+ "from huggingface_hub import notebook_login"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We will download a pre-trained large language model from Hugging Face and a dataset to train the model with. Below we assign these to variables we will use later. We will also set the name of the repository and model for the fine-tuned model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jTq15hgUW8qB"
+ },
+ "outputs": [],
+ "source": [
+ "# Pre trained model\n",
+ "model_name = \"mistralai/Mistral-7B-v0.3\"\n",
+ "\n",
+ "# Dataset name\n",
+ "dataset_name = \"vicgalle/alpaca-gpt4\"\n",
+ "\n",
+ "# Hugging face repository link to save fine-tuned model(Create new repository in huggingface,copy and paste here)\n",
+ "new_model = \"YOUR-USER-NAME/mistral-7b-hf-finetune\" "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To access your Hugging Face account, you need to log in. First go to your Hugging Face account, click *Settings* and select *Access Tokens*. Create a new token and copy the token. Then execute the below login command and when asked paste an access token. "
+ ]
+ },
+ {
+ "cell_type": "code",
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+ },
+ "id": "xnwXVf_dXeIp",
+ "outputId": "02bd913d-2331-4968-821f-330f4df6baf5"
+ },
+ "outputs": [],
+ "source": [
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's then download a subset of the dataset we want to use. Below we limit the dataset to the first 10,000 examples in order to save time. In real life you would probably use the full dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 342,
+ "referenced_widgets": [
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+ },
+ "id": "Bt_36Xa5Xuub",
+ "outputId": "5c3b2662-6de6-4aa3-90f6-21346273dac6"
+ },
+ "outputs": [],
+ "source": [
+ "dataset = load_dataset(dataset_name, split=\"train[0:100]\")\n",
+ "dataset[\"text\"][0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's then download the model. We first create a config object for quantization of the model using bitsandbytes. Bitsandbytes enables accessible large language models via k-bit quantization for PyTorch.\n",
+ "\n",
+ "We also need to download the tokenizer."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
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+ ]
+ },
+ "id": "ecbQfx6mYpSU",
+ "outputId": "91338af7-f6ab-4422-e71e-b1817e111283"
+ },
+ "outputs": [],
+ "source": [
+ "bnb_config = BitsAndBytesConfig(\n",
+ " load_in_4bit= True,\n",
+ " bnb_4bit_quant_type= \"nf4\",\n",
+ " bnb_4bit_compute_dtype= torch.float16,\n",
+ " bnb_4bit_use_double_quant= False,\n",
+ ")\n",
+ "model = AutoModelForCausalLM.from_pretrained(\n",
+ " model_name,\n",
+ " quantization_config=bnb_config,\n",
+ " device_map={\"\": 0}\n",
+ ")\n",
+ "model = prepare_model_for_kbit_training(model)\n",
+ "model.config.use_cache = False # silence the warnings. Please re-enable for inference!\n",
+ "model.config.pretraining_tp = 1\n",
+ "\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "tokenizer.add_eos_token = True\n",
+ "tokenizer.add_bos_token, tokenizer.add_eos_token"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Below we set the access token to Waights & Biases. You should copy your access token from your account at [https://wandb.ai](https://wandb.ai)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 208
+ },
+ "id": "ow0n2oSOYz6V",
+ "outputId": "737f4b26-2f94-42b3-d623-2f0961130547"
+ },
+ "outputs": [],
+ "source": [
+ "#monitering login\n",
+ "wandb.login(key=\"\") # Add your WANDB key here\n",
+ "run = wandb.init(project='Fine tuning mistral 7B', job_type=\"training\", anonymous=\"allow\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then we'll create a configuration for the lo-rank adaptation method we will use. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "id": "D409OdMjcc-n"
+ },
+ "outputs": [],
+ "source": [
+ "peft_config = LoraConfig(\n",
+ " lora_alpha=8,\n",
+ " lora_dropout=0.1,\n",
+ " r=16,\n",
+ " bias=\"none\",\n",
+ " task_type=\"CAUSAL_LM\",\n",
+ " target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\"]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We need to set the training arguments for the training run."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "id": "n9sAp50mc4LR"
+ },
+ "outputs": [],
+ "source": [
+ "training_arguments = TrainingArguments(\n",
+ " output_dir=\"./results\",\n",
+ " num_train_epochs=1,\n",
+ " per_device_train_batch_size=8,\n",
+ " gradient_accumulation_steps=2,\n",
+ " optim=\"paged_adamw_8bit\",\n",
+ " save_steps=1000,\n",
+ " logging_steps=30,\n",
+ " learning_rate=2e-4,\n",
+ " weight_decay=0.001,\n",
+ " fp16=False,\n",
+ " bf16=False,\n",
+ " max_grad_norm=0.3,\n",
+ " max_steps=-1,\n",
+ " warmup_ratio=0.3,\n",
+ " group_by_length=True,\n",
+ " lr_scheduler_type=\"linear\",\n",
+ " report_to=\"wandb\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally we create the trainer object that uses supervised fine-tuning (SFT) as the training method."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 243,
+ "referenced_widgets": [
+ "e9cf3b824a4a4f1990c29f2e61be0038",
+ "a433330ec84542de807aef472c9acafa",
+ "c8e91d29445e490aaa525ea119b0b8e3",
+ "a7bd631e98504e0296ad78639647b2ef",
+ "9151568cd1e8476ba7fb8f811cb61f52",
+ "959e16989c134a32908bd9be600947fe",
+ "1fff16dbf4bd47d5af73902fd7d8ff0e",
+ "b747915d40df437d948dfc73a8dd224c",
+ "dbdff3235e1e412d815dafdb9e0c9661",
+ "2d76618d11db4b79ad98d5248ad7982e",
+ "28339461a9714909bb53bb39b27c44d4"
+ ]
+ },
+ "id": "7JdMo0sqc_SB",
+ "outputId": "b5e04401-e48c-4018-f032-7f8f9e2d15a2"
+ },
+ "outputs": [],
+ "source": [
+ "# Setting sft parameters\n",
+ "trainer = SFTTrainer(\n",
+ " model=model,\n",
+ " train_dataset=dataset,\n",
+ " peft_config=peft_config,\n",
+ " max_seq_length= None,\n",
+ " dataset_text_field=\"text\",\n",
+ " tokenizer=tokenizer,\n",
+ " args=training_arguments,\n",
+ " packing=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Then, we can execute the training run. This will approximately 8 hours using the T4 GPU available in Colab and the dataset of 10,000 samples we downloaded."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 214
+ },
+ "id": "pEiMWxk9dFLk",
+ "outputId": "842c435d-8eee-41a6-edba-c6c88ba9617e"
+ },
+ "outputs": [],
+ "source": [
+ "# Train model\n",
+ "trainer.train()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UZg7GbvHdOUc"
+ },
+ "outputs": [],
+ "source": [
+ "# Save the fine-tuned model\n",
+ "trainer.model.save_pretrained(new_model)\n",
+ "wandb.finish()\n",
+ "model.config.use_cache = True\n",
+ "model.eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def stream(user_prompt):\n",
+ " runtimeFlag = \"cuda:0\"\n",
+ " system_prompt = 'Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n'\n",
+ " B_INST, E_INST = \"### Instruction:\\n\", \"### Response:\\n\"\n",
+ " prompt = f\"{system_prompt}{B_INST}{user_prompt.strip()}\\n\\n{E_INST}\"\n",
+ " inputs = tokenizer([prompt], return_tensors=\"pt\").to(runtimeFlag)\n",
+ " streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)\n",
+ " _ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)"
+ ]
},
- "nbformat": 4,
- "nbformat_minor": 0
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "stream(\"what is newtons 3rd law and its formula\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# This will fail due to cuda out of memeory issue. Need to add quantization\n",
+ "base_model = AutoModelForCausalLM.from_pretrained(\n",
+ " model_name, \n",
+ " low_cpu_mem_usage=True,\n",
+ " return_dict=True,\n",
+ " torch_dtype=torch.float16,\n",
+ " device_map= {\"\": 0})\n",
+ "model = PeftModel.from_pretrained(base_model, new_model)\n",
+ "model = model.merge_and_unload()\n",
+ "\n",
+ "# Reload tokenizer\n",
+ "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
+ "tokenizer.pad_token = tokenizer.eos_token\n",
+ "tokenizer.padding_side = \"right\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "model.push_to_hub(new_model)\n",
+ "tokenizer.push_to_hub(new_model)"
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "gpuType": "T4",
+ "provenance": []
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diff --git a/week-6/README.md b/week-6/README.md
index cb57ffa..26f4d9c 100644
--- a/week-6/README.md
+++ b/week-6/README.md
@@ -4,11 +4,18 @@
# Lab
1. Query tables in natural language. See [query_tables.py](query_tables.py)
+
a. Improve code by loading all sections to avoid hard-coding a section with tables
+
b. Test the capabilities of reasoning with table data: can it sum up numbers or do some other calculation?
-2. Generate synthetic query with misspellings - only task description is available you need to write the code in [synthetic_data.py](synthetic_data.py)
+3. Generate synthetic query with misspellings - only task description is available you need to write the code in [synthetic_data.py](synthetic_data.py)
+
a. Find the queries in [web_search_queries.csv](web_search_queries.csv), containing example queries from different topics and use cases, like map search, job search, travel and tourism, general knowledge and learning
+
b. Implement code, that will load one query at a time and generate up to N misspellings.
+
c. Improve robustness of the method, for example, skip known abbreviations, like JFK.
+
d. Make misspellings capture a variety of error types (phonetic, omission, transposition, repetition)?
- e. Test the resulting query variants with your favourite web search engine. Are results equal for the same for all variants of a given query? If not, why do you think this is happening?
\ No newline at end of file
+
+ e. Test the resulting query variants with your favourite web search engine. Are results equal for the same for all variants of a given query? If not, why do you think this is happening?