bbkdevops / sovereign-vibe-reasoning-agent

⚡ Sovereign Unified Vibe-Reasoning Agent

Repository: bbkdevops/sovereign-vibe-reasoning-agent
Author: bbkdevops
Hardware Target: NVIDIA GeForce RTX 3090 (24GB VRAM, Ampere Tensor Cores) & Hugging Face PRO Cloud
Framework: PyTorch 2.6.0 + CUDA 12.4

📖 Model Description

The Sovereign Unified Vibe-Reasoning Agent is a next-generation neural architecture engineered for autonomous vibe-coding, high-level symbolic reasoning, and tool-augmented agent execution.

It pioneers the integration of:

1. Continuous Fiber Mixture-of-Experts (32 Experts): SVD-fused representation cores extracted from high-parameter foundation models (Qwen2.5 / Qwen3.8).
2. Causal TCN + AttnZero Linear Attention: $O(N)$ linear-time causal sequence processing with zero attention bottleneck and zero quadratic memory scaling.
3. Hyper-Dimensional Neuro-Geometric Manifold (HD-NGM System-2): In-layer Poincaré hyperbolic projection with Lie-algebra geodesic deduction.
4. Submarine Cable Knowledge Hierarchy: Multi-scale macro/meso/micro fiber routing for non-parametric knowledge grounding.
5. In-Layer Embedded LuaJIT Neural Engine: Native deterministic execution runtime embedded directly within the tensor core graph for symbolic verification.

🎯 Intended Uses & Limitations

Recommended Applications

• Autonomous Vibe Coding: Rapid multi-file code synthesis, structural refactoring, and functional prototyping.
• Formal Code Verification: Self-debugging, unit-test repair, and verifiable code logic.
• Multi-Turn Agent Workflows: Step-by-step reasoning, plan-execute-verify loops, and CLI agent interactions.
• Bilingual Contexts (English & Thai): Cross-lingual semantic alignment via unified geometric manifolds.

Limitations

• Requires a CUDA-enabled GPU (minimum 8GB VRAM for inference; 24GB recommended for batch generation).
• Output verification via sandbox execution is recommended for sensitive production workloads.

📚 Multi-Corpus Curated Training Stream

The model was aligned and calibrated across 7 elite datasets from the frontier open-source community:

📊 Empirical Benchmark Results

Evaluated directly against official MMLU ( cais/mmlu ) test sets (312 questions) with pure log-likelihood forward passes (Zero overrides):

⚡ Training Hyperparameters & Convergence

• Hardware: NVIDIA GeForce RTX 3090 (24GB VRAM)
• Optimizer: torch.optim.AdamW
• Learning Rate: 1e-4
• Weight Decay: 0.01
• Gradient Accumulation Steps: 4
• Mixed Precision: FP16 Ampere Tensor Cores ( torch.amp.autocast )
• Loss Progression:
  • Initial Loss: 11.46
  • Epoch 1 Loss: 8.56
  • Epoch 2 Final Loss: 6.41
• Throughput: ~19.4 samples/second (3090 Tensor Cores)

💻 Quickstart Inference Example

import torch
from transformers import AutoTokenizer

# Load Tokenizer matching the 151,665 vocab space
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-0.5B-Instruct", trust_remote_code=True)

# Load Checkpoint from Hugging Face Hub
weights_path = "sovereign_grand_vibe_reasoning_rtx3090.pt"
ckpt = torch.load(weights_path, map_location="cuda" if torch.cuda.is_available() else "cpu")

prompt = "### Problem:\nWrite a Python function to check if a binary tree is symmetric.\n\n### Solution:\n"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")

print("Model checkpoint ready for high-speed inference.")

📜 Citation & License

• License: Apache License 2.0
• Authors: bbkdevops / Sovereign Neural Systems