news
redhat.com propping up the large language models (LLMs) hype instead of Linux and Free software
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Red Hat Official ☛ Scalable and cost-effective fine-tuning for LLMs [Ed: Red Hat is posting nothing today except about this hype, buzzword, and maybe Ponzi scheme]
So the question becomes: To gain a competitive advantage, how can you adapt a general purpose LLM to a specific use case, knowledge domain, lingo, customer input, etc.? And how can you do it in a cost-effective way? Ideally, you want to start small, evolve quickly and continuously provide value to your business.
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Red Hat Official ☛ Optimizing GPU ROI: Inference by day, training by night [Ed: More "AI" nonsense]
A fundamental shift in workload management can dramatically improve this inefficiency. Artificial intelligence (AI) workloads naturally fall into two distinct categories: inference and training. Inference runs during business hours, responding to real-time user demands with low-latency requirements. Training, on the other hand, is compute-intensive but can tolerate delays, interruptions and batch processing—making it the perfect candidate for off-hour execution.
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Red Hat Official ☛ Alignment tuning and RAG: What you should know [Ed: Still propping up the large language models (LLMs) hype]
Incorporating artificial intelligence (AI) into an organization isn't a matter of flipping a switch; it requires careful customization to suit specific business needs. When adapting large language models (LLMs) for the enterprise, alignment tuning and retrieval-augmented generation (RAG) are two strategies that can be used separately or together to tune an AI model. While alignment tuning, a variation of fine tuning, focuses on shaping the model's responses and behavior, RAG relies on integrating external data into the model's workflow. Both approaches customize LLM behavior and output suited for a variety of different use cases and types of data. So, let’s explore each method to help you determine the best fit for your needs.