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…
Runs locally from ~637.8 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS.gguf | GGUF | IQ3_XXS | 637.8 MB | Download |
Model Details
| Model ID | DuoNeural/Qwen2.5-1.5B-Instruct-GTAP-v3-IQ3_XXS-GGUF |
|---|---|
| Author | DuoNeural |
| Pipeline | text-generation |
| License | apache-2.0 |
| Base model | Qwen/Qwen2.5-1.5B-Instruct |
| Last modified | 2026-09-29T03:31:01.000Z |
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:
- Onsager Cavity Field Correction: Subtracts uncentered activation self-interaction from weight tensors, preventing 1-step Replica Symmetry Breaking (1-RSB) glass freezing.
- Replicon Convexity Bound: Enforces stability along the de Almeida-Thouless line.
- 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 TestbedRun 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.
Source: Hugging Face · Compare models