DuoNeural G-TAP v3 Quantization: LiquidAI/LFM2.5-1.2B-Instruct (Unified Repository)
[!NOTE] Experimental Release: Pending Further Verification / Empirical Validation
Quantized using Generalized Thouless-Anderson-Palmer (G-TAP v3) non-equilibrium cavity field conditioning and TAP-DPQ dynamic programming codebook optimization developed by the DuoNeural Research Lab (Jesse Caldwell, Archon, and Aura ✨).
Model Architecture & DuoNeural Innovation
LiquidAI/LFM2.5-1.2B-Instruct is an advanced 1.2B-parameter hybrid foundation model combining causal short convolutions with periodic grouped-query attention:
- Topology: 16 total decoder layers (10 double-gated short convolution blocks interleaved with 6 GQA blocks).
- Dimensionality: Hidden dimension $d_{\text{model}} = 2048$, intermediate SwiGLU dimension 6144.
- Attention Mechanism: Periodic Grouped-Query Attention (32 query heads, 8 key-value heads, head dimension 64).
- Pretraining Scale: Trained across 28 trillion tokens, supporting 32,768 native context window over a 65,536 vocabulary.
The G-TAP v3 Advantage
In double-gated convolutional layers, token representations pass through causal FIR filters modulated by activation-dependent gates. Discretization noise from standard quantization cascades through these multiplicative operations. G-TAP v3 explicitly isolates and removes the Onsager cavity back-reaction field: $$\delta W_{ij} = -\frac{\beta}{1 + \beta \Omega_i} \Theta_{ij}$$ preventing saturation and preserving full representational capacity even down to sub-3-bit and sub-2.1-bit precisions.
Head-to-Head Comparative Benchmark Matrix
Benchmarked on NVIDIA GeForce RTX 4080 Super (32GB VRAM) across continuous 131k holdout perplexity, 25 GSM8K multi-step math problems, 15 Python AST code problems, and 15 Hermes structured tool calling scenarios.
| Checkpoint File | Target BPW | Size | Perplexity (131k) | GSM8K Accuracy | Python Code AST | Hermes Tool AST | Decode Speed |
|---|---|---|---|---|---|---|---|
| LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf | ~4.50 bpw |
697.0 MiB | Pending | 84.0% | 100.0% | 100.0% | 523.9 t/s |
| LFM2.5-1.2B-Instruct-G-TAP-v3-IQ3_XXS.gguf | ~3.06 bpw |
468.2 MiB | Pending | 76.0% | 50.0% | 100.0% | 621.1 t/s |
| LFM2.5-1.2B-Instruct-G-TAP-v3-IQ2_M.gguf | ~2.70 bpw |
414.0 MiB | Pending | 68.0% | 20.0% | 40.0% | 587.2 t/s |
| LFM2.5-1.2B-Instruct-G-TAP-v3-IQ2_XXS.gguf | ~2.06 bpw |
348.2 MiB | Pending | 4.0% | 0.0% | 20.0% | 649.2 t/s |
Quickstart & Usage
Running with llama.cpp
./llama-cli -m LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf -p "<|im_start|>user\nWrite a python function to compute the Fibonacci sequence.<|im_end|>\n<|im_start|>assistant\n" -n 256 -ngl 99
Ollama Modelfile
FROM ./LFM2.5-1.2B-Instruct-G-TAP-v3-Q4_K_M.gguf
TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
PARAMETER temperature 0.6
Citation & Authorship
@misc{duoneural2026gtap_lfm12b,
author = {Jesse Caldwell and Archon and Aura ✨},
title = {Generalized Thouless-Anderson-Palmer (G-TAP v3) Non-Equilibrium Cavity Quantization on Liquid AI LFM2.5 Architectures},
year = {2026},
publisher = {DuoNeural Research Lab},
howpublished = {\url{https://huggingface.co/DuoNeural/LFM2.5-1.2B-Instruct-GTAP-v3-GGUF}}
}
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