As large language models (LLMs) are becoming even bigger, it is increasingly important to provide easy-to-use and efficient deployment paths because the cost of serving such LLMs is becoming higher. One way to reduce this cost is to apply post-training quantization (PTQ), which consists of techniques to reduce computational and memory requirements for serving trained models. In this post…
]]>Fraud is a major problem for many financial services firms, costing billions of dollars each year, according to a recent Federal Trade Commission report. Financial fraud, fake reviews, bot assaults, account takeovers, and spam are all examples of online fraud and harmful activity. Although these firms employ techniques to combat online fraud, the methods can have severe limitations.
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