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F ine-tuning Large Language Models (LLMs) has become a crucial step in leveraging the power of pre-trained models for specific applications. This article provides a comprehensive guide on how to fine-tune LLMs using your own data, covering everything from prerequisites to deployment. By the end of this article, you will understand the steps involved in adapting LLMs to meet your unique requirements, enhancing their performance on specialized tasks. read more..

  W indows 10 shell items are metadata files that hold details about various objects in the Windows operating system, including shortcuts, files, and folders. These items are invaluable for forensic investigations because they provide insights into the location and usage of these objects. To perform shell item forensics on Windows 10, you can use forensic tools such as Autopsy, EnCase, or Belkasoft Evidence Center, which are capable of extracting and analyzing shell item metadata. Additionally, manual analysis of shell items is possible using the Windows Shellbags parser, a tool that extracts and interprets the binary data stored in shell item files.. read more...

  A memory leak is a faulty condition where a program fails to free up memory it no longer needs. If left unaddressed, memory leaks result in ever-increasing memory usage, which in turn can lead to degraded performance, system instability, and application crashes. Most modern programming languages include a built-in mechanism to protect against this problem, with garbage collection being the most common. Go has a garbage collector (GC) that does a very good job of managing memory. Garbage collectors such as the Go GC automatically track down memory that is no longer used and return it back to the system. read more...  

  Many Companies Hold Vast Data but Are Unprepared for LLM Fine-Tuning: How to Solve It and What to Do About It In today’s data-driven world, companies across various industries generate and store vast amounts of data. From customer interactions and sales transactions to sensor readings and user-generated content, organizations are sitting on treasure troves of information. However, when it comes to leveraging this data for fine-tuning large language models (LLMs), many companies find themselves unprepared. The growing need for AI-powered solutions requires adapting these models to specific organizational needs—a task that demands both the right infrastructure and expertise. The Challenge: Vast Data, But Lacking Readiness for LLM Fine-Tuning Large language models, such as OpenAI’s GPT or Google’s Bert, have revolutionized industries by providing AI capabilities for natural language understanding, generation, and analysis. However, these models are typically pre-trained on generalized d

  T he Compute and Performance Engineering teams at Netflix regularly investigate performance issues in our multi-tenant environment. The first step is determining whether the problem originates from the application or the underlying infrastructure. One issue that often complicates this process is the "noisy neighbor" problem. On Titus, our multi-tenant compute platform, a "noisy neighbor" refers to a container or system service that heavily utilizes the server's resources, causing performance degradation in adjacent containers. We usually focus on CPU utilization because it is our workloads’ most frequent source of noisy neighbor issues, read more 

I ncident response and digital forensics play crucial roles in understanding, mitigating, and preventing security events. However, a common pitfall that can undermine even the most sophisticated investigative efforts is the practice of “cherry picking” – selectively choosing evidence that supports a predetermined conclusion while ignoring contradictory information. Whether you’re a seasoned cybersecurity professional or new to the field, understanding the dangers of cherry picking is crucial for conducting thorough and accurate investigations. Let’s dive in and explore why a holistic approach to evidence gathering and analysis is essential in today’s complex threat landscape, read more...