batteryphil/mamba3-baremetal-rlf

Mamba3-p14c RLF (Bare-Metal Reasoning Engine)

Model Description

The Mamba3-p14c RLF is an experimental hardware-native State Space Model (SSM) engineered to run directly above motherboard firmware. Stripping away the need for an operating system, this model operates entirely inside a generic UEFI runtime (llama2.efi).

It integrates **Recursive Latent Forcing (RLF)**—a hybrid continuous-thought reasoning architecture inspired by Mamba2BackboneRecursion. Instead of generating discrete text <think> tokens, the reasoning engine traps execution in the latent space through a fixed constraint loop ($N=10$), actively controlled by a secondary HaltingHead, yielding O(1) execution memory for infinite semantic branching.

Model Details

• Architecture: Mamba / SSM
• Parameter Size: ~130M Base + Low-Rank (r=64) RLF Loop Bridge
• Config: d_model=768, 24 Layers
• Format: Custom .mamb v2 Extended Binary Format
• Runtime: Bare-Metal C-Engine (UEFI llama2.efi), No-OS required

Execution Environment

This model is packaged explicitly for bare-metal execution on x86_64 UEFI architecture. Standard execution requires booting the llama2.efi payload directly from a FAT32 logical volume or mapping via Virtual QMP monitors.

Hardware Prerequisites

• RAM: Minimum 8GB system memory recommended. (If mapped through a QEMU visual or serial monitor, standard KVM mappings may overlap with the legacy 0x0B0000 VGA frame buffer if constrained beneath 4GB).
• Storage: Dual-drive architecture supported (fat:rw VirtIO payload block + QCOW2 IDE disk).
• Platform: x86_64 UEFI firmware.

Bare-Metal QEMU Execution

# Launch the silicon natively inside QEMU
qemu-system-x86_64 \
    -machine pc -enable-kvm -cpu host -m 8192 \
    -display none -serial stdio -monitor none -vga none -no-reboot \
    -drive if=pflash,format=raw,readonly=on,file=/usr/share/OVMF/OVMF_CODE_4M.fd \
    -drive if=pflash,format=raw,file=ov_test.fd \
    -drive format=qcow2,file=disk_unified.qcow2,if=ide

Recursive Latent Forcing (RLF)

Traditional Large Language Models rely on discrete, text-based reasoning tokens to trace thought patterns. The Mamba3-p14c completely abandons textual intermediate tokens inside its reasoning loop.

Mechanics:

1. Loop Injection: A low-rank bottleneck captures the recurrent loop states, augmented by Gaussian Noise exploration.
2. Latent Scratchpad: 8 prefix tokens are bound inside the latent continuous array.
3. Halting Head: An autonomous linear probe outputs a deterministic float. Once the threshold is cleared, the continuous loop collapses forward through the decoding layer and drops the final discrete text answer.

Usage Limitations

• This model requires the custom Native C-Engine interpreter. It is incompatible with standard PyTorch transformers execution without porting the .mamb extension headers back.
• Output text is generated natively through UEFI graphical routines via the ssm_rlf_infer() REPL loop.