Turn my blog feed into a QA dataset to fine-tune a LLM

This project converts blog feed content into a structured Question-Answer dataset using LLaMA 3.2 (via Ollama) for local processing. The generated dataset follows a conversational format and can be automatically pushed to Hugging Face.

The open-source code is available here.

I was looking to fine-tune an open-source LLM with content that I have produced to see how advanced such LLMs are and how close I could get to getting a model running locally to "output tokens" the same way I would.

According to Daniel Kahneman and his book Thinking, Fast and Slow, humans have two modes of thought:

• System 1: Fast, instinctive, and emotional. An example of this is my posts on X.

Multiple libraries are out there to scrape data from X. One that I used recently, and liked (without requiring an X API key) was Twitter scraper finetune from ElizaOS.

• System 2: Slower, more deliberative, and more logical. My blog is an example of this. Some posts take me several hours to write, and I need to sleep on the topic before pushing.

For this, I didn't find any good out-of-the-box library that allowed me to convert my posts into a QA dataset to fine-tune a model.

So this is what I ended up building.

Getting Started

To do this you will need:

• Python 3.11

• Poetry (for python dependencies)

• Ollama (to run Llama 3.2)

• Hugging Face account (for dataset upload)

And your blog in a JSON feed like https://didierlopes.com/blog/feed.json.

1. Install dependencies

poetry install
poetry run python -m spacy download en_core_web_sm

2. Install Ollama and pull Llama 3.2

Follow instructions to install Ollama: https://ollama.com/

Select a model to run locally using https://ollama.com/search.

In this case, we want to run llama3.2:latest (https://ollama.com/library/llama3.2).

ollama pull llama3.2:latest

Then, we can check that the model has been downloaded with:

ollama list

Finally, we can test that it works with:

ollama run llama3.2:latest

3. Configure Hugging Face

• Create a write-enabled token at Hugging Face

• Create a .env file:

HF_TOKEN=your_token_here

Usage

1. Update the blog feed URL in this notebook.

Below you can see the feed structure being used - which is the default coming from Docusaurus, which is the framework I'm using to auto-generate the feed for my blog.

url = "https://didierlopes.com/blog/feed.json"

JSON Feed Structure

{
  "version": "https://jsonfeed.org/version/1",
  "title": "Didier Lopes Blog", 
  "home_page_url": "https://didierlopes.com/blog",
  "description": "Didier Lopes Blog",
  "items": [
    {
      "id": "URL of the post",
      "content_html": "HTML content of the post", 
      "url": "URL of the post",
      "title": "Title of the post",
      "summary": "Brief summary of the post",
      "date_modified": "ISO 8601 date format",
      "tags": [
        "array",
        "of", 
        "tags"
      ]
    },
    // ... more items
  ]
}

2. Set your Hugging Face dataset repository name:

dataset_repo = "didierlopes/my-blog-qa-dataset"

This is what the dataset will look like in HuggingFace: https://huggingface.co/datasets/didierlopes/my-blog-qa-dataset/viewer.

3. Run the notebook cells sequentially.

The notebook contains detailed explanations throughout to guide you through the process step-by-step.

Dataset Format

The generated dataset includes:

• title: Blog post title

• conversation: Array of Q&A pairs in role-based format

• context: Original cleaned blog content

• url: Source blog post URL

• date: Publication date

Note: This is the format of the conversation field:

conversation = [
    {
        "role": "user", 
        "content": (
            "You mentioned that when ChatGPT launched, everyone rushed to build "
            "financial chatbots. What were some of the fundamental truths that "
            "those who built these chatbots missed?"
        )
    },
    {
        "role": "assistant",
        "content": (
            "Those building financial chatbots missed two fundamental truths:"
            "1. AI models are useless without access to your data."
            "2. Access to data isn't enough - AI needs to handle complete "
            "workflows, not just conversations."
            "These limitations led to chatbots that can't access proprietary "
            "data, can't handle complex workflows and restrict analysts to an"
            "unnatural chat interface."
        )
    },
    # ... more Q&A pairs following the same pattern
]

Summary of how it works

• Fetches blog content from JSON feed

• Cleans HTML to markdown format

• Analyzes sentence count to determine Q&A pair quantity

• Generates contextual questions using LLaMA 3.2 running locally

• Creates corresponding answers

• Filters and removes duplicate Q&A pairs

• Formats data for Hugging Face

• Pushes to Hugging Face Hub