Creating a Custom Training Loop for Ollama Models

What is Ollama?

Getting Started with Ollama

1. Install Ollama: use a simple one-liner curl command or follow through with the manual installation methods.
2. Choose Models: Decide which model you're interested in; for example, you can find a list of supported models on the Ollama models page.
3. Set Up Your Environment: Ensure you have your Python environment ready with necessary dependencies installed. Out of the box, Ollama supports models without heavy GPU requirements, making local development a breeze.

Why Create a Custom Training Loop?

• Specificity: You can fine-tune the model on specific data that suits your needs - be it a corpus of industry-related text or documents like .pdfs.
• Performance: Custom loops can optimize training efficiency, allowing you to manage how often the model weighs its decisions against the training data.
• Control: You have full control over hyperparameters and training procedures, letting you tweak things like learning rates, epochs, and whatever else you fancy!

Custom Training Loop Basics

• Data Loader
• Model Definitions
• Training Procedure
• Optimizer

1. Setting Up Your environment

• Python: Ensure Python is installed.
• Ollama: Already covered this earlier, so you’ll be good to go there!
• Additional Libraries: You’ll need common ones like
  1 numpy
  ,
  1 pandas
  ,
  1 torch
  , etc. Be sure you’ve got them in your environment.

2. Load the Data

• Data Structures: Your data could come from various forms, such as CSV files, .txt documents, and even web scraping.
• Here’s an example of how to load your data using Python, given a directory of PDF files:

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  from pathlib import Path
    import PyPDF2
    
    def load_pdfs(directory):
        pdf_files = list(Path(directory).rglob('*.pdf'))  # gets all PDF files
        data = []
        for pdf_file in pdf_files:
            with open(pdf_file, 'rb') as f:
                reader = PyPDF2.PdfReader(f)
                for page in reader.pages:
                    data.append(page.extract_text())
        return data
  
    training_data = load_pdfs('./data')  # Point to your PDF path

3. Define Your Model

Load a pre-trained model: just an example with Mistral

4. Create the Training loop

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for epoch in range(num_epochs):
    model.train()  # Prepare model for training
    for data_batch in training_data:
        # Your training code here
        optimizer.zero_grad()  # Reset gradients
        outputs = model(data_batch)
        loss = loss_function(outputs, targets)
        loss.backward()  # Backpropagation
        optimizer.step()  # Update model's weights

5. Define Hyperparameters

• Learning Rate: Controls how much you adjust your model weights; typically around 0.01.
• Batch Size: Number of training examples you process at a time. Experiment until it feels right!
• Epochs: Determine how many times your model will see the training data.

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learning_rate = 0.001
batch_size = 32
num_epochs = 10

Debugging & Monitoring

• Log the Loss: Keep track of loss values per epoch. This will help signify whether your model is learning.
• Visualize with TensorBoard: If you’re fancy, hook up TensorBoard for visualization to see how your training proceeds in graphical form.

Implementing LoRA with Ollama

1. Load the model with adaptation into the Ollama framework.
   1 2 3 4 5 6 7 python from ollama import Ollama model = Ollama( model='mistral', lora_r=4, lora_alpha=8 )
2. Begin your fine-tuning with your dataset! You would follow up with your training loop, utilizing the customized parameters.

Good Practices for Model Training

• Experiment: Don’t hesitate to experiment with batch sizes, learning rates, and model architectures.
• Data Preparation: Strive to have your data clean & clear; data quality directly influences performance!
• Monitor Constantly: Just because it feels good doesn’t mean it is good; watch those training logs.

Integrating with Arsturn

• Building customized chatbots using your unique data.
• Creating real-time consultations without needing advanced coding skills.
• Offering instant responses, ensuring your users get accurate info when they want it.

Conclusion