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To: falconllm@tii.ae, aidrc.contact@tii.aeCC: hakim.hacid@tii.ae (Chief Researcher, AIDRC)Subject: Architectural Proposal: Dynamic SSM State Expansion for Falcon H1R (Local Inference)Dear Falcon Reasoning Team,First, happy release day for the Falcon H1R 7B! I am a local AI power-user in Denmark, and I am currently benchmarking the H1R on consumer-grade hardware (RTX 2070). The 14T token density and the DeepConf reasoning architecture are remarkable leaps for the 7B parameter class.I have a technical proposal regarding the Hybrid Mamba-Transformer architecture that I believe could revolutionize how these models handle high-fidelity RAG (Retrieval-Augmented Generation) on consumer hardware.The Observation: Currently, the SSM/Mamba hidden state is fixed at training time. While this offers "constant VRAM" benefits, it introduces "information compression blurriness" (hallucination) when the context window is saturated with dense RAG data.The Proposal: Dynamic State Expansion (DSE)Would it be feasible to implement a feature in the inference engine (vLLM/llama.cpp) that allows the model to expand its internal latent state $z$ during inference by offloading a higher-dimensional memory vector to System RAM or a RAM-disk?For Enterprise/H100 users: They keep the fixed state for max speed.For Consumer users (8GB-24GB VRAM): We could sacrifice a small amount of latency to "expand" the hidden state into our system RAM. This would prevent the "blurry" compression effect in long-context tasks without requiring the massive VRAM overhead of a standard KV-cache.As a user running this locally on a 36 SM (RTX 2070) setup, I believe this "Flexible Memory" approach would make Falcon H1R the definitive choice for private, high-accuracy RAG.Thank you for your incredible work on open-access AI.Best regards, Person.