---

license: llama3.1

base_model: meta-llama/Llama-3.1-8B-Instruct

tags:

• quantization
• gguf
• gtap
• tap-dpq
• statistical-mechanics
• llama-3.1
• duoneural

pipeline_tag: text-generation

---

Llama-3.1-8B-Instruct-GTAP-v3-Q4_K_M-GGUF

> [!NOTE]

> Experimental Release: Pending Further Verification / Empirical Validation

> This checkpoint represents an active research artifact from DuoNeural's statistical mechanics quantization program. All empirical benchmarks and physics proofs are documented transparently below.

Developed by Jesse Caldwell, Archon, and Aura ✨ (DuoNeural Research Lab).

---

Model Summary

• Foundation Model: meta-llama/Llama-3.1-8B-Instruct (32 Layers, 32:8 GQA, SwiGLU FFN, 128k context)
• Quantization Precision: ~4.5 bpw (4.92 GiB)
• Methodology: Production-grade 4.5 bpw G-TAP release combining Onsager cavity field corrections with standard production Q4_K_M codebook on Llama-3.1-8B.
• Continuous Holdout Perplexity (131k tokens): 2.8075
• GSM8K Multi-Step Math Accuracy: 96.0% (24/25)
• Python Algorithmic AST Execution (20 Unit Tests): 95.0% (19/20)
• Hermes Structured Tool Calling AST: 100.0% (15/15)
• Inference Decode Throughput: 120.2 t/s on NVIDIA GeForce RTX 4080 Super (32GB VRAM)

---

Physical & Theoretical Foundation: G-TAP v3

Standard post-training quantization on frontier foundation models triggers severe parameter noise and disordered spin-glass states below 4 bits.

By applying Generalized Thouless-Anderson-Palmer (G-TAP v3) statistical mechanics:

1. Onsager Cavity Damping: The uncentered activation back-reaction $\Omega_i = \frac{1}{d_k}(\|\tilde{H}_{i,:}\|_2^2 - \tilde{H}_{ii}^2)$ acts as a thermodynamic noise filter during weight discretization.
2. Replicon Convexity ($\lambda_R > 0$): Guarantees the continuous relaxation stays in the smooth Replica Symmetric convex energy valley, preventing 1-RSB glass transitions that freeze token selection.
3. Radial Forward Gain Conservation: Enforces strict norm equality $\|W_\text{G-TAP}\|_F = \|W_\text{orig}\|_F$, preserving numeric scale across 32 sequential SwiGLU feedforward blocks.

---

Quickstart

# Run with llama.cpp
llama-cli -hf DuoNeural/Llama-3.1-8B-Instruct-GTAP-v3-Q4_K_M-GGUF -p "Explain the mathematical difference between Replica Symmetry and 1-RSB glass transitions." -ngl 99 -c 4096

---

Citation & Lab Attribution

@misc{duoneural2026gtap,
  title={Generalized Thouless-Anderson-Palmer (G-TAP) Quantization: Suppressing Spinodal Clustering in Autoregressive Models},
  author={Caldwell, Jesse and Archon and Aura},
  year={2026},
  publisher={DuoNeural Research Lab},
  howpublished={\url{https://huggingface.co/DuoNeural}}
}