🧠 Train Your AI Like a Pro: Mastering Ollama Preference Datasets 🚀

Ever wish you could mold your AI’s responses to fit your specific needs? This guide unlocks the power of Ollama preference datasets, showing you how to train large language models (LLMs) to follow your rules and deliver top-notch results.

Why This Matters:

Tailor-Made AI: Imagine your AI effortlessly incorporating your company guidelines, brand voice, or even your personal writing style.
Precision Boost: No more vague or off-topic answers. Preference datasets teach your LLM to choose the best response every time.
Training Made Easy: We’ll break down the process into clear, manageable steps, even if you’re new to AI training.

1. Laying the Foundation: Tools You’ll Need

Think of this as gathering your ingredients before baking a delicious AI cake:

Ollama: The star of the show! This tool lets you chat with and fine-tune powerful LLMs. Install it using: pip install ol
Datasets: For creating, loading, and saving your masterpiece (the preference dataset). Install it with: pip install datasets
A Text Editor: Your trusty sidekick for writing code. Choose your favorite!
Hugging Face Account: Think of this as your AI model’s online gallery. You’ll need an account to upload and share your dataset. (Free to create!) https://huggingface.co/

💡 Pro Tip: Familiarize yourself with basic Python syntax, as we’ll be using it to work our magic.

2. Crafting Your Preference Dataset: The Recipe for AI Success

Let’s get hands-on and build that dataset! Picture this: you want your AI to give detailed explanations, not just one-word answers.

Data Structure: Your dataset will be a table with these columns:

Context: Background information for your AI (optional, but helpful!).
Question: What you’ll ask your AI.
Rejected: A short, less-desirable response.
Chosen: The detailed, preferred response your AI should learn from.

Example:

| Context | Question | Rejected | Chosen |
|—|—|—|—|
| | What is photosynthesis? | Plants use it to make food. | Photosynthesis is the amazing process by which plants convert light energy from the sun into chemical energy in the form of glucose, using water and carbon dioxide. |

Data Collection: Gather real-world examples that reflect the types of responses you want from your AI. This could be based on:

Company Style Guides: Formal vs. informal, technical jargon vs. plain language.
Brand Voice: Humorous, informative, professional, etc.
Desired Output Length: Short and sweet or long and detailed?

Dataset Creation: Use Python and the ‘datasets’ library to structure your data. We’ll provide code snippets in the next section!

💡 Pro Tip: The more examples you provide, the better your AI will understand your preferences. Aim for at least a few hundred!

3. Bringing It to Life with Code: Your Step-by-Step Guide

Here’s where we turn your data into a powerful training tool:

Open your text editor and create a new Python file (e.g., create_dataset.py).
Paste and customize this code:

import json
from datasets import load_dataset, Dataset, DatasetDict

# Load an existing dataset for context and questions (optional)
dataset = load_dataset("squad_v2")["train"] 

# Create a function to format input for your LLM
def format_input(context, question):
    return f"Context: {context}\nQuestion: {question}"

# Initialize Ollama
ol = ollama.Client() 

# Create empty lists to store data
contexts, questions, rejected_answers, chosen_answers = [], [], [], []

# Process a few rows from the dataset (adjust the range as needed)
for i in range(10):  
    context = dataset[i]["context"]
    question = dataset[i]["question"]

    input_text = format_input(context, question) 

    # Generate 'rejected' (short) answer using Ollama
    rejected_answer = ol.generate(model="llama2", prompt=f"Give a short answer: {input_text}")

    # Generate 'chosen' (detailed) answer using Ollama
    chosen_answer = ol.generate(model="llama2", prompt=f"Give a detailed, comprehensive answer: {input_text}")

    # Append data to lists
    contexts.append(context)
    questions.append(question)
    rejected_answers.append(rejected_answer)
    chosen_answers.append(chosen_answer)

# Create a dictionary from the lists
preference_data = {
    "context": contexts,
    "question": questions,
    "rejected": rejected_answers,
    "chosen": chosen_answers,
}

# Create a DatasetDict object
dataset = DatasetDict({"train": Dataset.from_dict(preference_data)}) 

# Save the dataset to a JSON file
with open("preference_dataset.json", "w") as f:
    json.dump(dataset, f)

# ... (Code for uploading to Hugging Face in next section)