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batteryphil/
mamba3-baremetal-rlf

Name: mamba3-baremetal-rlf
Availability: InStock

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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

bash

# 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:

Loop Injection: A low-rank bottleneck captures the recurrent loop states, augmented by Gaussian Noise exploration.
Latent Scratchpad: 8 prefix tokens are bound inside the latent continuous array.
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.

batteryphil/mamba3-baremetal-rlf