Make GenAI better understand your enterprise needs by fine-tuning LLMs

[HPE_Experts]

Discovering the power of GenAI for enterprises comes down to fine-tuning large language models (LLMs) into a specialized assistant capable of understanding nuances, adapting to domain-specific knowledge, and generating more accurate and context-aware responses. Models can be fine-tuned to understand an organization’s specific industry and business processes, offering more relevant and context-aware recommendations. C-level executives can now use this improved operational efficiency, improved developer productivity, and enhanced customer experience to predict market trends with greater accuracy. This accuracy and targeted approach enable C-level executives to make data-driven decisions more quickly, fostering innovation and maintaining a competitive edge in their industry.

In today's competitive market, businesses are increasingly relying on artificial intelligence (AI) to streamline their operations, enhance developer productivity, and deliver better outcomes. The global AI market is on an upward trajectory, and within it, the field of AI-powered coding assistants is rapidly gaining momentum. According to a report by Grand View Research, the global AI market is expected to reach $1.8 trillion by 2030, with a substantial portion of that growth coming from sectors like enterprise AI and machine learning (ML) models applied to software development. One of the most promising areas for AI in business is improving developer productivity, operational insights, especially combined with domain knowledge. LLMs like StarCoder2 are at the forefront of this evolution, enabling more efficient coding processes, natural language queries, operational insights, automated troubleshooting, and improved overall system reliability and performance.

Models such as StarCoder2 can automatically generate code, debug, suggest improvements, and optimize performance, dramatically enhancing the software development lifecycle. The adoption of such AI-driven solutions is becoming more critical, with businesses striving to reduce development cycles. Fine-tuning such a model for specific business needs, however, requires specialized expertise and tools.

Business challenges: Navigating complexity in software development

While the potential of AI-driven code generation is clear, businesses often face several challenges when trying to integrate these technologies into their workflows. Some of the key hurdles include:

• Lack of customization: Pretrained models like StarCoder2 offer general capabilities but may not be optimized for a business’s specific industry, technology stack, or use case. Hewlett Packard Enterprise can help with fine-tuning AI models such as StarCoder2 for a specific domain or standard and also provide a scalable solution for inferencing.
• Data security and privacy: Many businesses require that sensitive data, such as proprietary code and intellectual property, stays on-premises or within a private cloud, making it difficult to leverage public cloud-based AI solutions. HPE can enhance data security and privacy by enabling the development of models that are designed to detect and prevent security threats.
• Integration into existing systems: Seamlessly integrating AI-powered models into legacy systems or current development practices can be a time-consuming and technically complex task. HPE AI solutions provide seamless integration by offering scalable infrastructure and AI-optimized hardware that supports smooth deployment of AI-powered models into existing systems.
• Skill gap: While AI models are powerful, fine-tuning them requires specialized expertise that not all organizations possess in-house. Lack of knowledge about model fine-tuning can be a barrier to realizing the full potential of these tools. HPE can help bridge the talent gap by providing powerful pretrained models and services that make fine-tuning simpler with minimal expertise required.

Scalability: AI models can be resource-intensive, and businesses need to ensure that their infrastructure can handle the increased training load and serve LLMs efficiently. Fine-tuned models, such as StarCoder2, allow businesses to leverage a highly adaptable AI system tailored to their needs. With HPE infrastructure and solutions, scaling these models becomes much easier.

Why private GenAI and fine-tuning are needed

Private GenAI and fine-tuning are essential for businesses and organizations that require tailored AI solutions while ensuring data privacy and security. Public AI models rely on vast datasets from a variety of sources, which can pose risks when handling sensitive or proprietary information. By using GenAI, companies can fine-tune models on their data, allowing them to create more relevant, accurate, and context-specific solutions without compromising confidentiality. Fine-tuning also ensures that the AI model is better suited to the organization’s unique needs, optimizing performance for specific tasks, industries, or user preferences.

Leveraging LLM and StarCoder2 for enterprises in development, application modernization, and automation
LLMs like StarCoder2 offer immense potential for enterprises seeking to fine-tune code generation based on enterprise coding standards and frameworks. It also allows enterprises to discover actionable insights from operational data, such as logs and metrics. By fine-tuning StarCoder2 on domain-specific datasets, businesses can equip it to
generate code based on enterprise coding standards or frameworks and interpret complex log files,
system performance metrics, and error reports with remarkable accuracy.
StarCoder2, optimized for programming tasks, helps businesses refactor applications to address their business problems, modernize outdated code, and efficiently integrate with cloud-native technologies. Fine-tuned LLM increases productivity, accelerates innovation, and ensures consistent high-quality output, thereby increasing business value.

How HPE can assist customers in their fine-tuning journey

HPE offers a comprehensive one-stop solution for secure and scalable AI implementation, combining cutting-edge hardware, software, and services. With our AI-optimized infrastructure, businesses can accelerate the development, training, and deployment of AI models while providing high performance, security, and scalability. HPE provides tailored solutions, including powerful GPUs, AI-specific accelerators, and high-speed data storage, to meet the demands of data-intensive workloads. These solutions enable businesses to efficiently scale AI operations, manage vast datasets, and offer optimal model performance. In addition to hardware, HPE offers robust software and services that integrate seamlessly with popular AI frameworks like Hugging Face, making it easy for organizations to fine-tune models on their data. HPE also prioritizes data privacy and security, helping businesses maintain control over their sensitive information and comply with regulations such as GDPR. By offering a fully integrated approach, HPE simplifies AI implementation, allowing organizations to focus on innovation while HPE manages the complexities of infrastructure, security, and scalability.

To better understand the steps involved in fine-tuning StarCoder2 LLM and how HPE fine-tuning service can help,
let's look at Figure 1.

Figure 1. LLM fine-tuning steps and HPE LLM fine-tuning service

Figure 1 shows the steps involved in the fine-tuning process from dataset preparation to deployment and inference. It also shows how HPE can help with each of the steps with the fine-tuning LLM service.

Learn more about Advisory and Professional Services from HPE Services.

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Ben Winston, Systems Architect, Cloud Native Engineering Practice Area, HPE

Ben Winston is a systems architect at HPE Services, specializing in the cloud native engineering practice area. Joining HPE in 2016, Ben has extensive experience in application modernization and digital transformation across various industries. He has worked on fine-tuning AI/ML models, application re-platforming to Kubernetes, greenfield cloud-native application development, application refactoring, creating reference architectures for public cloud, and hybrid cloud environments. Ben plays a pivotal role in helping HPE teams deliver AI/ML, cloud-native solutions to customers worldwide. He also architects enterprise-ready solutions that can be reused across the enterprise to improve cloud, application, and security posture