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Transforming language models into effective red holders is not without its challenges. Modern large language models have transformed the way we interact with technology, yet they are still struggling to prevent the generation of harmful content. Efforts such as rejection training help these models refuse risky requests, but even these protective measures can be bypassed … Read more

Adaptation of large language models to specialized domains remains challenging, especially in fields that require spatial reasoning and structured problem solving, although specialized in complex reasoning. Semiconductor Layout Design is a good example where AI tools have to interpret geometric restrictions and ensure accurate component location. Researchers develop advanced AI architectures to improve LLMS ‘ability … Read more

Quantization is a crucial technique for deep learning for reducing calculation costs and improving model efficiency. Large language models require significant treatment power, making quantization important to minimize memory consumption and improve the rate of inference. By converting high precision weights to lower bit formats, such as Int8, Int4 or InT2, quantization reduces storage requirements. … Read more