Abstract: Single image super-resolution (SISR) aims to reconstruct a high-resolution image from its low-resolution observation. Recent deep learning-based SISR models show high performance at the ...

Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1 gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid tsc_known_freq ...

A Doppler shift is defined as a change of frequency of light or sound when an object is moving toward or away from an observer. Edwin Hubble observed in 1929 that galaxies appear on average to be ...

A significant bottleneck in large language models (LLMs) that hampers their deployment in real-world applications is the slow inference speeds. LLMs, while powerful, require substantial computational ...

Artificial Intelligence (AI) has seen tremendous growth, transforming industries from healthcare to finance. However, as organizations and researchers develop more advanced models, they face ...

In artificial intelligence, one common challenge is ensuring that language models can process information quickly and efficiently. Imagine you’re trying to use a language model to generate text or ...

I'm using llama-cpp-python==0.2.60, installed using this command CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python. I'm able to load a model using type_k=8 and type_v=8 (for q8_0 cache).