These are the officially trained Hypernetwork weights for the MESA Architecture, designed as an $O(1)$ KV-Cache scalable successor to Doc-to-LoRA.

Instead of storing context in memory activations (KV-cache), MESA uses a System-1 linear skimmer and a Deep Weight Programmer (Hypernetwork) to instantly compile textual context into transient LoRA matrices in a single forward pass.

This specific checkpoint (mesa_kl_ep2.pt) was trained using a Dual-Objective KL-Divergence Generative Regularization loss to solve the Stability-Plasticity dilemma. It successfully drops $D_{KL}$ to $0.0588$, allowing the base model to preserve mathematically flawless English grammar during zero-shot weight injection.

These are not standard LLM weights. These are the weights for the MESA DeepWeightProgrammer module, which dynamically generates LoRA matrices for Qwen/Qwen2.5-0.5B-Instruct.

To use these weights, you must run the MESA pipeline from our GitHub repository: