Posts by Mehdi Rezagholizadeh

AMD-HybridLM: Towards Extremely Efficient Hybrid Language Models

The rapid rise of deep learning applications has intensified the demand for language models that offer a balance between accuracy and efficiency—especially in settings constrained by memory, compute, or real-time requirements. While Transformer-based models have revolutionized natural language processing, their quadratic attention complexity and large key–value (KV) cache requirements pose serious challenges for deployment, particularly on edge devices or in latency-sensitive environments.

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