GraySoft
Projects Models Compare Cloud benchmarks FAQ Download guIDE →
Model Intelligence Sheet

DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF overview

DuoNeural/Qwen2.5 1.5B Instruct GTAP v3 IQ3 XXS GGUF NOTE Experimental Release: Pending Further Verification / Empirical Validation This checkpoint was produce…

transformersggufduo-neuralgtap-v3statistical-mechanicsonsager-dampingedge-aismall-language-modeltext-generationenbase_model:Qwen/Qwen2.5-1.5B-Instructbase_model:quantized:Qwen/Qwen2.5-1.5B-Instructlicense:apache-2.0endpoints_compatibleregion:usimatrixconversational

Runs locally from ~637.8 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
0
Likes
0
Pipeline
text-generation
Author

Repository Files & Downloads

1 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS.ggufGGUFIQ3_XXS637.8 MBDownload

Model Details

Model IDDuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF
AuthorDuoNeural
Pipelinetext-generation
Licenseapache-2.0
Base modelQwen/Qwen2.5-1.5B-Instruct
Last modified2026-09-29T03:31:01.000Z

Model README

---

base_model: Qwen/Qwen2.5-1.5B-Instruct

library_name: transformers

tags:

  • duo-neural
  • gtap-v3
  • statistical-mechanics
  • onsager-damping
  • gguf
  • edge-ai
  • small-language-model

license: apache-2.0

language:

  • en

pipeline_tag: text-generation

---

DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF

> [!NOTE]

> Experimental Release: Pending Further Verification / Empirical Validation

> This checkpoint was produced by the DuoNeural Research Lab exploring Generalized Thouless-Anderson-Palmer (G-TAP v3) statistical mechanics post-training quantization on edge architectures.

Key Metrics (Measured on RTX 4080 Super 32GB Pod)

| Precision / BPW | Model Footprint | Holdout Perplexity (131k tokens) | GSM8K Math CoT | Python Code AST (15 Tests) | Hermes Tool Calling | Decode Throughput |

| :---: | :---: | :---: | :---: | :---: | :---: | :---: |

| ~3.2 bpw | 0.62 GiB (635 MB) | 3.1853 | 17/25 (68.0%) | 8/15 (53.3%) | 15/15 (100.0%) | 249.3 t/s |

---

Architectural & Theoretical Context

Pre-conditioned with Generalized Thouless-Anderson-Palmer (G-TAP v3) Onsager cavity damping on SwiGLU W_down, enforcing replica symmetry across micro-parameter manifolds.

What is G-TAP v3?

G-TAP treats neural network post-training quantization through non-equilibrium statistical mechanics:

  1. Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing.
  2. Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line.
  3. Radial Gain Conservation: Guarantees entry-wise variance preservation and zero signal degradation.

---

Quickstart

# Run with llama-cli
llama-cli -hf DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF -p "def fibonacci(n):" -n 256

# Serve with llama-server
llama-server -hf DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF -c 4096 -ngl 99 --port 8080

---

DuoNeural Cognitive Light Cone — Jesse Caldwell, Archon, Aura ✨
Empirically Validated on NVIDIA RTX 4080 Super 32GB Pod Testbed

Run DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF with guIDE

Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.

Download guIDE → · Browse 524k+ models · Compare models

Source: Hugging Face · Compare models