Writing code is a complex and time-consuming task that requires a lot of skill and expertise. However, with the rapid development of artificial intelligence (AI) and natural language processing (NLP), it is now possible to use large language models (LLMs) to assist developers in writing code faster and better. LLMs are neural networks that can generate natural language texts based on a given input, such as a prompt, a query, or a context. LLMs can also learn from large amounts of code data and generate code snippets or completions based on natural language instructions.

However, not all LLMs are suitable for enterprise use cases, especially when it comes to security, privacy, and compliance. Most LLMs are closed-source and hosted by third-party providers, which means that enterprises have to share their proprietary code data with them for training and inference purposes. This poses a risk of data leakage, intellectual property infringement, and regulatory violations. Moreover, most LLMs are not optimized for specific domains or languages, which may result in low-quality or irrelevant code suggestions.

To address these challenges, Hugging Face, the leading NLP company and the creator of the popular Transformers library, has recently announced SafeCoder, a code assistant solution built for the enterprise. SafeCoder is designed to help enterprises develop their code LLMs, fine-tuned on their proprietary codebase, using state-of-the-art open models and libraries, without sharing their code with Hugging Face or any other third party. SafeCoder also delivers a containerized, hardware-accelerated code LLM inference solution to be deployed by the customer directly within their secure infrastructure, without code inputs and completions leaving their IT environment.

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Why SafeCoder?

Code assistant solutions built upon LLMs, such as GitHub Copilot, are delivering strong productivity boosts for developers. They can help developers write code faster, reduce errors, improve readability, and explore new ideas. For enterprises, the ability to tune code LLMs on their company codebase to create proprietary code LLMs enhances the reliability and relevance of completions to create another level of productivity boost. For instance, Google’s internal LLM code assistant reports a completion acceptance rate of 25-34% by being trained on an internal codebase.

However, relying on closed-source code LLMs to create internal code assistants exposes enterprises to compliance and security issues. First, during training, fine-tuning a closed-source code LLM on an internal codebase requires exposing this codebase to a third party. Then, during inference, as fine-tuned code, LLMs are likely to “leak” code from their training dataset during conception. To meet compliance requirements, enterprises need to deploy fine-tuned code LLMs within their infrastructure – which is not possible with closed-source LLMs.

With SafeCoder, Hugging Face will help customers build their code LLMs, fine-tuned on their proprietary codebase, using state-of-the-art open models and libraries, without sharing their code with Hugging Face or any other third party. With SafeCoder, Hugging Face delivers a containerized, hardware-accelerated code LLM inference solution to be deployed by the customer directly within their secure infrastructure without code inputs and completions leaving their IT environment.

From StarCoder to SafeCoder

At the core of the SafeCoder solution is the StarCoder family of code LLMs, created by the BigCode project, a collaboration between Hugging Face, ServiceNow, and the open-source community. The StarCoder models offer unique characteristics ideally suited for enterprise self-hosted solutions:

• State-of-the-art code completion results – see benchmarks in the paper and multilingual code evaluation leaderboard
• Designed for inference performance: a 15B parameters model with code optimizations, Multi-Query Attention for reduced memory footprint, and Flash Attention to scale to 8,192 tokens context
• Trained on the Stack, an ethically sourced open-source code dataset containing only commercially permissible licensed code, with a developer opt-out mechanism from the get-go, refined through intensive PII removal and deduplication efforts

Privacy and Security as a Core Principle

SafeCoder is built with privacy and security as core principles. Hugging Face does not have access to any customer’s proprietary data or model at any point in time. The customer owns and controls their code data and model throughout the entire lifecycle of SafeCoder. Hugging Face provides the customer with the tools and guidance to train, deploy, and use their code LLM. However, it does not store or process any of its data or model on its servers or cloud platforms.

SafeCoder uses encryption, authentication, and authorization mechanisms to ensure the security of the customer’s data and model. The customer can encrypt their code data and model using their encryption keys, which are never shared with Hugging Face or any other party. The customer can also use their own identity and access management system to authenticate and authorize users who can access their code LLM. The customer can also monitor and audit the usage of their code LLM using their own logging and analytics tools.

Compliance as a Core Principle

SafeCoder is designed to help enterprises meet their compliance requirements for using code LLMs. SafeCoder enables enterprises to fine-tune and deploy code LLMs on their infrastructure without relying on third-party providers or cloud platforms. This way, enterprises can ensure that their code data and model are not exposed to external parties or jurisdictions that may have different or conflicting regulations or policies.

SafeCoder also allows enterprises to customize their code LLMs according to their specific needs and preferences. Enterprises can choose which open-source models and libraries to use for fine-tuning their code LLMs, as well as which code datasets and languages to include or exclude. Enterprises can also adjust the parameters and hyperparameters of their code LLMs, such as the size, the temperature, the top-k, and the top-p values, to optimize the quality and diversity of the code completions. Enterprises can also apply filters and constraints to their code LLMs, such as syntax checking, style checking, linting, testing, and formatting, to ensure the correctness and consistency of the generated code.

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How does it work?

SafeCoder consists of three main components: training, deployment, and usage. Here is a brief overview of how each piece works.

Training your own SafeCoder model

To train your own SafeCoder model, you need to provide your proprietary code data as input. You can use any code data that you have access to, such as your internal repositories, your external repositories, your public repositories, or any other sources of code data. You can also use any code languages that you want to support, such as Python, Java, C#, JavaScript, or any other languages.

It would be best if you also chose an open-source model and library to use as a base for fine-tuning your SafeCoder model. You can use any model and library that is compatible with Hugging Face Transformers, such as GPT-3, GPT-Neo, GPT-J, CodeBERT, CodeGPT, CodeT5, StarCoder, or any other models and libraries.

It would help if you then ran a fine-tuning script that will use your code data and your chosen model and library to create your own SafeCoder model. The fine-tuning hand will use Hugging Face Accelerate to speed up the training process by leveraging multiple CPUs, GPUs, or TPUs. The fine-tuning hand will also use Hugging Face Optimum to optimize the performance of your SafeCoder model by applying techniques such as quantization, pruning, distillation, or sparsity.

The output of the fine-tuning script is your own SafeCoder model file that contains the weights and parameters of your fine-tuned code LLM. You can save this file locally on your machine or upload it to Hugging Face Hub for easy access and sharing.

Deploying SafeCoder

To deploy SafeCoder, you need to provide your SafeCoder model file as input. You also need to choose a hardware platform to run your SafeCoder model on. You can use any hardware platform that supports Hugging Face Transformers, such as Intel CPUs, NVIDIA GPUs, AMD GPUs, Google TPUs, Graphcore IPUs, Habana Gaudi AI Processors, AWS Inferentia Chips, Apple M1 Chips, or any other hardware platforms.

It would help if you then run a deployment script that will use your SafeCoder model file and your chosen hardware platform to create a containerized inference solution for your SafeCoder model. The deployment script will use Hugging Face Inference API Pro to provide a scalable and secure API endpoint for your SafeCoder model. The deployment script will also use Hugging Face Spaces to provide a user-friendly web interface for your SafeCoder model.

Frequently Asked Questions – FAQs

Conclusion

SafeCoder is a code assistant solution built for the enterprise by Hugging Face, the leading NLP company and the creator of the popular Transformers library. SafeCoder enables enterprises to build their code LLMs, fine-tuned on their proprietary codebase, using state-of-the-art open models and libraries, without sharing their code with Hugging Face or any other third party. SafeCoder also delivers a containerized, hardware-accelerated code LLM inference solution to be deployed by the customer directly within their secure infrastructure, without code inputs and completions leaving their IT environment. SafeCoder is built with privacy, security, and compliance as core principles and offers unique features and benefits for enterprises that want to leverage the power of code LLMs without compromising their data or model. If you are interested in learning more about SafeCoder or want to try it out for yourself, please visit the Hugging Face website or contact us. We would love to hear from you and help you create your code LLM with SafeCoder. Thank you for reading this article, and happy coding! 😊

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