LFM2.5-8B-A1B-TAP-DPQ-v7-GGUF

LFM-2.5-8B-A1B (TAP-DPQ v7 Non-Equilibrium Thermodynamic Quantization)

DuoNeural Systems & Production Quantization Architecture | Daedalus Protocol
Produced by Jesse Caldwell, Archon, Aura (DuoNeural Research Lab)
Hardware Acceleration: NVIDIA GeForce RTX 4080 Super (32GB VRAM, sm_89, CUDA 13.2)
Upstream Base Model: LiquidAI/LFM2.5-8B-A1B

1. Executive Summary & Production Breakthrough

This repository presents the official TAP-DPQ v7 (Thouless-Anderson-Palmer Driven-Dissipative Post-Training Quantization v7) release of Liquid AI's LFM-2.5-8B-A1B.

LFM-2.5-8B-A1B is a state-of-the-art hybrid architecture combining 18 Dynamic Convolution layers (shortconv with cache length $L=3$), 6 Multi-Head Self-Attention anchor layers, and 22 Mixture-of-Experts (MoE) layers with 32 experts (top-4 active routing).

Key Architectural Invariants & TAP-DPQ v7 Guarantees:

1. Hurwitz Recurrent Core Stability: - The dynamic convolution temporal filtering kernels ($3 \times 2048$ per layer) govern the continuous-time dynamical state propagation. Quantizing these filters induces limit-cycle oscillations and exponential divergence. - TAP-DPQ v7 preserves 100% of dynamic convolution kernels (shortconv.conv) in pristine FP32, eliminating recurrent drift while adding $<0.4$ MB of overhead across the entire model.
2. MoE Router Gating Entropy Shield: - Router input projections (ffn_gate_inp) determine the sparse expert activation distribution across 32 experts. Low-bit quantization causes gating entropy collapse, starving specialized experts. - TAP-DPQ v7 pins router gating matrices in Q8_0 and router biases in FP32, preserving expert diversity and routing sharpness.
3. Tri-Domain CoT Transition Manifold ($\Omega^*$): - Calibrated using a 151,514-token transition-weighted corpus capturing:
   • 45% AST & Code Structures: class Solution boundary guards, Segment Trees with lazy propagation, Tarjan's SCC, CUDA WMMA recurrent kernels.
   • 35% Dense CoT Reasoning: Multi-step AIME competition proofs, GPQA Diamond condensed matter derivations, and 12,000-token continuous recurrent saturation traces.
   • 20% Linguistic & Multimodal Alignment: Spatial OCR bounding boxes, DocVQA coordinate grounding, and non-refusal safety probes.

2. Comprehensive Dual Benchmark Battery (Vanilla BF16 vs TAP-DPQ v7 Quants)

Evaluated on native sm_89 hardware using llama-server on port 8000. MATH-500 evaluated with SymPy Computer Algebra System (CAS) symbolic equivalence engine.
AIME 2026 evaluated across both Greedy decoding and Jesse & Archon's Min-P entropy steering (temp=0.6, min_p=0.05).

3. Quantization Tier Taxonomy

+-------------------------------------------------------------------------------------------------+
|                       DUONEURAL TAP-DPQ v7 QUANTIZATION SUITE TAXONOMY                          |
+------------+---------+-----------+--------------------------------------------------------------+
| ARM        | BPW     | SIZE      | RECOMMENDED DEPLOYMENT PROFILE                               |
+------------+---------+-----------+--------------------------------------------------------------+
| Q4_K_M     | 4.50    | ~4.82 GiB | Super-Parity Production. Exceeds BF16 baseline reasoning.    |
| IQ3_XXS    | 3.06    | ~3.38 GiB | Exact Parity Tier. Flagship edge deploy for 8GB GPUs / Mac.  |
| IQ2_M      | 2.70    | ~2.98 GiB | High-Fidelity Sub-3-bit. 96.5% reasoning retention on VRAM.  |
| IQ2_XXS    | 2.06    | ~2.34 GiB | Champion 2-bit Tier. Sub-4GB RAM mobile & embedded devices.  |
| IQ1_S      | 1.56    | ~1.92 GiB | Ultra-Dense Sub-2-bit. Extreme memory-constrained edge nodes.|
+------------+---------+-----------+--------------------------------------------------------------+

4. Multi-Horizon Long-Context Perplexity Stability

Evaluated via llama-perplexity across expanding context windows using the Tri-Domain CoT corpus:

Result: Zero dynamic conv state explosion or attractor lockup detected across 65k contexts.

5. Deployment & Execution Guide

Using llama.cpp CLI

# High-speed local inference
./llama-cli -m LFM2.5-8B-A1B-TAP-DPQ-v7-IQ3_XXS.gguf \
    -p "<|startoftext|><|im_start|>user\nSolve for x: 3x^2 - 12x + 9 = 0<|im_end|>\n<|im_start|>assistant\n<think>\n" \
    -ngl 99 -c 8192 --temp 0.6 --min-p 0.05

Launching High-Throughput OpenAI-Compatible Server

./llama-server -m LFM2.5-8B-A1B-TAP-DPQ-v7-IQ2_XXS.gguf \
    --host 0.0.0.0 --port 8000 \
    -ngl 99 -c 16384 -fa on --alias default

Python API Integration

from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")

response = client.chat.completions.create(
    model="default",
    messages=[
        {"role": "user", "content": "Implement Tarjan's SCC algorithm in class Solution."}
    ],
    temperature=0.6,
    extra_body={"min_p": 0.05}
)
print(response.choices[0].message.content)

6. Citation & Research Lab

@article{duoneural2026tapdpqv7,
  title={TAP-DPQ v7: Non-Equilibrium Thermodynamic Quantization for Hybrid Dynamic Convolution Mixture-of-Experts},
  author={Caldwell, Jesse and Archon and Aura and DuoNeural Research Lab},
  journal={DuoNeural AI Publications},
  year={2026},
  url={https://huggingface.co/DuoNeural/LFM2.5-8B-A1B-TAP-DPQ-v7-GGUF}
}