Instructions to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

LM Studio
Jan

vLLM

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Ollama
How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
```
Unsloth Desktop

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS
```

Lemonade

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF-IQ3_XXS

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF:IQ3_XXS" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF

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: Qwen/Qwen2.5-Math-7B-Instruct (28 Layers, 28:4 GQA, SwiGLU FFN)
Quantization Precision: ~3.2 bpw (2.90 GiB)
Methodology: Calibrated with DuoNeural 131k-token code-infused activation Hessian (qwen7b_math_gtap.imatrix), preserving delicate algebraic chain-of-thought representations.
Continuous Holdout Perplexity (131k tokens): 8.8915
GSM8K Multi-Step Math Accuracy: 100.0% (25/25)
Competition & Olympiad Mathematics (15 Problems): 86.7% (13/15)
Symbolic Python Math Unit Tests (10 Tests): 90.0% (9/10)
Inference Decode Throughput: 173.1 t/s on NVIDIA GeForce RTX 4080 Super (32GB VRAM)

Mathematical Reasoning Invariants at Sub-4-Bit

Standard post-training quantization on formal mathematical models introduces discretization noise that breaks sensitive algebraic deduction chains and symbolic parity. By applying Generalized Thouless-Anderson-Palmer (G-TAP v3) statistical mechanics:

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.
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.
Radial Forward Gain Conservation: Enforces strict norm equality $|W_\text{G-TAP}|_F = |W_\text{orig}|_F$, preserving numeric scale across 28 sequential SwiGLU feedforward blocks.

Quickstart

# Run with llama.cpp
llama-cli -hf DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF -p "Solve step by step: Compute the remainder when 3^100 is divided by 7." -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}}
}

Downloads last month: 171

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

3-bit

Model tree for DuoNeural/Qwen2.5-Math-7B-Instruct-CodeInfused-IQ3_XXS-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Math-7B

Finetuned

Qwen/Qwen2.5-Math-7B-Instruct

Quantized

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