An example of AI inference would be a self-driving car that is capable of recognizing a stop sign, even on a road it has never driven on before. The process of identifying this stop sign in a new context is inf
Streaming inference is often used in Internet of Things systems. It’s not set up to interact with people in the way an LLM is. Instead, a pipeline of data, such as regular measurements from machine sensors, flows into an ML algorithm that then continually makes predictions. Patterns in the...
AnLLM Compressorcan help make these challenges less difficult and make AI inference faster. What is vLLM? How Red Hat can help Red Hat AIis a platform of products and services that can help your enterprise at any stage of the AI journey - whether you’re at the very beginning or ready...
That, in turn, translates to reduced latency and inference costs. For example, a fine-tuned Llama 7B model can be astronomically more cost-effective (around 50 times) on a per-token basis compared to an off-the-shelf model like GPT-3.5, with comparable performance. Common use cases LLM ...
vLLM is one of multiple inference serving runtimes offered withRed Hat® OpenShift® AI. OpenShift AI is a flexible, scalable MLOps platform with tools to build, deploy, and manage AI-enabled applications. OpenShift AI supports the full lifecycle of AI/ML experiments and models, on-premis...
LLM temperature is a parameter that influences the language model’s output, determining whether the output is more creative or predictable.
Streaming inference is often used in Internet of Things systems. It’s not set up to interact with people in the way an LLM is. Instead, a pipeline of data, such as regular measurements from machine sensors, flows into an ML algorithm that then continually makes predictions. Patterns in the...
Finally, one of the security problems with LLMs is that users may upload secure, confidential data into them in order to increase their own productivity. But LLMs use the inputs they receive to further train their models, and they are not designed to be secure vaults; they may expose ...
Hallucination in generative question answering This type of hallucination occurs when an LLM makes an erroneous inference from its source information and arrives at an incorrect answer to a user question. This can happen even when relevant source material is provided.For example, if a user asks, ...
Source: Information is Beautiful This evolution is illustrated in the graph above. As we can see, the first modern LLMs were created right after the development of transformers, with the most significant examples being BERT –the first LLM developed by Google to test the power of transformers–...