Databricks hits $188B valuation, extending its run as AI’s favorite second act

Introduction

In this tutorial, you'll learn how to work with Databricks' open-source AI models for coding, following the trend that has helped Databricks reach a $188B valuation. You'll set up a basic environment, load an open weight AI model, and run a simple code completion task. This hands-on approach will give you practical experience with the tools that are making waves in the AI industry.

Prerequisites

Before starting this tutorial, you'll need:

• A computer with internet access
• Python 3.7 or higher installed
• Basic understanding of Python programming
• Access to a Databricks account (you can create a free trial at databricks.com)

Step-by-step Instructions

Step 1: Set Up Your Development Environment

Install Required Python Packages

The first step is to install the necessary Python packages that will allow you to interact with Databricks' AI models. Open your terminal or command prompt and run the following command:

pip install databricks-cli transformers torch

This installs the Databricks CLI, Hugging Face Transformers library, and PyTorch - all essential for working with AI models.

Step 2: Create a Databricks Account

Sign Up for Free Access

Visit databricks.com and create a free account. Databricks offers a generous free tier that's perfect for learning and experimentation. The free account includes access to their AI/ML platform where you can run experiments and access open weight models.

Step 3: Configure Your Databricks CLI

Set Up Authentication

After creating your account, you'll need to configure the Databricks CLI. Run this command in your terminal:

databricks configure

You'll be prompted to enter your Databricks instance URL and a personal access token. You can generate this token in your Databricks account settings under User Settings > Access Tokens.

Step 4: Load an Open Weight AI Model

Initialize the Model

Now we'll create a simple Python script to load an open weight AI model for coding tasks. Create a new file called ai_coder.py and add this code:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the tokenizer and model
model_name = "Salesforce/codet5p-770m-py"  # This is a popular open weight model for code

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_name)

print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(model_name)

print("Model loaded successfully!")

This code loads a model specifically designed for code generation. The model name "Salesforce/codet5p-770m-py" is a popular open weight model that Databricks and the AI community have been using for coding tasks.

Step 5: Test the Model with Sample Code

Generate Code Completion

Let's add code to test our model's capabilities. Modify your ai_coder.py file to include this:

# Test the model with a simple code prompt
prompt = "def fibonacci(n):"
input_ids = tokenizer.encode(prompt, return_tensors='pt')

# Generate code
with torch.no_grad():
    outputs = model.generate(input_ids, max_length=100, num_return_sequences=1)
    generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print("Generated code:")
print(generated_code)

This code demonstrates how to use the model for code completion. We provide a simple function definition and ask the model to continue it. The model will generate a complete Fibonacci function based on our prompt.

Step 6: Run Your AI Coding Assistant

Execute the Script

Save your file and run it with:

python ai_coder.py

You should see output showing the model loading and then generating a Fibonacci function. This simple example shows how open weight models can be used to create AI coding assistants.

Step 7: Explore Databricks Workspace

Navigate the Platform

Log into your Databricks workspace and explore the available notebooks. You'll find pre-built notebooks that demonstrate how to work with various AI models. Look for the 'AI' or 'Machine Learning' sections where you can find examples of how companies like Databricks are using open weight models to reduce costs and improve development efficiency.

Step 8: Experiment with Different Models

Try Alternative Open Weight Models

Databricks supports various open weight models. Try modifying your code to use different models:

# Try a different model
model_name = "codellama/CodeLlama-7b-hf"  # Alternative model

# Re-initialize with new model
print("Loading new model...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

print("New model loaded!")

Each model has its own strengths and may perform better for different types of coding tasks. This flexibility is one of the advantages of using open weight models.

Summary

In this tutorial, you've learned how to set up a development environment for working with open weight AI models for coding, similar to what Databricks has been doing to build their AI capabilities. You've installed the necessary tools, loaded a code generation model, and tested it with a simple example. This hands-on experience gives you insight into how companies like Databricks are leveraging open weight models to create cost-effective AI solutions for developers.

As you continue exploring, remember that the AI landscape is rapidly evolving. Databricks' success demonstrates the value of open weight models in reducing costs while maintaining high performance. Keep experimenting with different models and use cases to deepen your understanding of this exciting field.