SafeCoder vs. Closed-source Code Assistants

For decades, software developers have designed methodologies, processes, and tools that help them improve code quality and increase productivity. For instance, agile, test-driven development, code reviews, and CI/CD are now staples in the software industry.

In "How Google Tests Software" (Addison-Wesley, 2012), Google reports that fixing a bug during system tests - the final testing stage - is 1000x more expensive than fixing it at the unit testing stage. This puts much pressure on developers - the first link in the chain - to write quality code from the get-go.

For all the hype surrounding generative AI, code generation seems a promising way to help developers deliver better code fast. Indeed, early studies show that managed services like GitHub Copilot or Amazon CodeWhisperer help developers be more productive.

However, these services rely on closed-source models that can't be customized to your technical culture and processes. Hugging Face released SafeCoder a few weeks ago to fix this. SafeCoder is a code assistant solution built for the enterprise that gives you state-of-the-art models, transparency, customizability, IT flexibility, and privacy.

In this post, we'll compare SafeCoder to closed-source services and highlight the benefits you can expect from our solution.

State-of-the-art models

SafeCoder is currently built on top of the StarCoder models, a family of open-source models designed and trained within the BigCode collaborative project.

StarCoder is a 15.5 billion parameter model trained for code generation in over 80 programming languages. It uses innovative architectural concepts, like Multi-Query Attention (MQA), to improve throughput and reduce latency, a technique also present in the Falcon and adapted for LLaMa 2 models.

StarCoder has an 8192-token context window, helping it take into account more of your code to generate new code. It can also do fill-in-the-middle, i.e., insert within your code, instead of just appending new code at the end.