batteryphil/harmonic-convergence

Harmonic Convergence: Mamba-3 PRIME Baremetal

This is a 300M parameter Mamba-3 architecture trained exclusively using the discrete PRIME lattice optimizer (integer voting).

⚠️ CRITICAL WARNING: Do NOT attempt to load this model using transformers or AutoModelForCausalLM. This model uses custom discrete integer weights (uint16_t mappings to a harmonic prime LUT) instead of standard FP32 gradients. Standard PyTorch/HF loaders will crash or load random noise.

This repository is designed for baremetal execution. The model has been exported to a highly compressed monolithic .bin file, optimized for AVX-512 integer-indexing in pure C.

Files Included

1. prime_mamba3_25000.bin: The monolithic, fully-trained model weights (Step 25,000). Highly compressed (769MB) using uint16_t indices.
2. prime_inference.c: The baremetal C inference wrapper that mmaps the .bin file.
3. prime_kernel.c: The core AVX-512 C kernel for executing the PRIME discrete integer matrix multiplications.
4. build_kernel.sh: Compilation instructions for the C environment.

Baremetal Execution

To run the model natively on a CPU using the included AVX-512 kernel:

# 1. Compile the baremetal C engine
gcc -O3 -march=native -mavx512f -mavx512bw -mavx512dq -fopenmp -ffast-math prime_kernel.c prime_inference.c -o prime_inference -lm

# 2. Execute against the monolithic binary
./prime_inference prime_mamba3_25000.bin

Binary Layout Structure

For developers building custom bootloaders or OS kernels (e.g., llm-baremetal-interactive.img), the prime_mamba3_25000.bin file follows this contiguous memory layout:

• Header (256 bytes): Contains 0x5052494D ("PRIM") magic number, and Config struct (d_model, n_layers, vocab_size, lut_size).
• LUT: 65,536 float32 prime harmonic points.
• Embeddings: vocab_size * d_model standard float32.
• Layers 0-27: Interleaved standard weights (float32) and compressed discrete weights (uint16_t for in_proj and out_proj).

Training Context

This model was trained to syntactically lock onto C/C++ architecture for Operating System Homeostasis generation. It successfully leverages discrete integer updates (SUPERMAJORITY voting) to prevent vanishing gradients over 25,000 steps.