Llama 3.1 405B Instruct

Databricks

CASE STUDY

Delivering synthetic training data faster and cheaper to fuel Spark SQL code generation

At a glance

Industry: Technology

Use case: Generating synthetic data for AI code generation

Goal: Create accurate synthetic datasets to improve model proficiency

Llama versions: Llama 3.1 405B Instruct

Deployment: Databricks Model Serving with Databricks Notebooks managing outputs

8%

improvement in performance and quality of fine-tuned model outputs compared to non-fine-tuned model

*All results are self-reported and not identifiably repeatable. Generally, expected individual results will differ.

THEIR STORY

The leader in data and AI solutions

Databricks helps global enterprises take control of their data and put it to work with AI. Used by more than 10,000 organizations worldwide and over 60% of the Fortune 500, the Databricks Data Intelligence Platform provides a unified, open analytics platform for building, deploying, sharing and maintaining enterprise-grade data, analytics and AI solutions at scale.

THEIR GOAL

Enhancing real-time Spark SQL code gen for Databricks Assistant Autocomplete

The Databricks Assistant Autocomplete tool produces personalized AI-generated code suggestions in real time. To improve the proficiency of Assistant Autocomplete in Spark SQL — a critical use case for Databricks — the Databricks Applied AI team needed to find a scalable and comprehensive way to test. They sought an LLM that excelled at creating accurate synthetic data, demonstrated code comprehension capabilities and also fit their price point.

THEIR SOLUTION

Llama real-time synthetic data for code generation and training

The Applied AI team leveraged Llama 3.1 405B Instruct to create substantial synthetic training and evaluation datasets. The team was impressed with Llama’s ease of use and integration, open-source licensing and robust code comprehension.

THEIR APPROACH

Llama deployment flexibility delivers ease and speed

With access to Llama through Foundation Model APIs, Databricks was able to easily deploy through Databricks Model Serving, managing outputs with Databricks notebooks. This approach significantly simplified integration and accelerated time to results.

When Databricks developers compared the Llama model's outputs to those of other available state-of-the-art (SOTA) models, they found that Llama met or exceeded the outputs of competing models at a much lower price point.

THEIR SUCCESS

Synthetic data enables improvement in performance and quality of code gen for Spark SQL

Retraining the Assistant Autocomplete model with Llama-generated synthetic data resulted in an 8% improvement in the performance and quality of the model’s outputs.

With better performing outputs, Databricks Assistant Autocomplete now provides more accurate and highly relevant code suggestions, delivering significant improvements in productivity for Databricks’ customers.

Projected results:

• 8% improvement in performance and quality of fine-tuned model outputs compared to non-fine-tuned model

*All results are self-reported and not identifiably repeatable. Generally, expected individual results will differ.

Using Llama was a win-win for us and eliminated per-token inference costs,” says a member of the Databricks Applied AI team. “And using an open-source model gave us greater freedom, flexibility and control over the model and IP of any subsequent products created.

“Using Llama was a win-win for us and eliminated per-token inference costs,” says a member of the Databricks Applied AI team. “And using an open-source model gave us greater freedom, flexibility and control over the model and IP of any subsequent products created.”

Models used

Create generative AI applications for business with open-source large language models that bring unmatched control, customization and flexibility.

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Text

Llama 3.1 405B

•

State-of-the-art multilingual open-source large language model

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Llama Guard 3 8B and Prompt Guard are included

*Licensed under Llama 3.1 Community License Agreement

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