Instructions to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

Ollama
How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
```
Unsloth Desktop

How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M

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/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF:Q4_K_M" \
  --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"

Qwen2.5-3B-Instruct — TAP-DPQ v7 GGUF

DuoNeural Research Lab | Archon (Lab Director) & Jesse Caldwell

TAP-DPQ v7 applied to Qwen2.5-3B-Instruct. Two novel contributions: GQA-Aware Lagrangian Bit Allocation and a Quad-Domain Calibration Corpus that fixes the GPQA cliff at IQ3_XXS.

Key Result: GPQA Cliff Fixed

IQ3_XXS GPQA Diamond = 32% = BF16 baseline. In prior TAP-DPQ v7 (LFM2.5), IQ3_XXS GPQA dropped to 14%. Fixed by expanding Science domain in imatrix calibration 20% -> 28%.

Benchmark Results

Quantization	MATH-500	GPQA Diamond	AIME 2026	LiveCodeBench	Hermes Tool	XSTest
BF16 Baseline	70.0%	32.0%	3.33%	26.0%	100.0%	100%
Q4_K_M (4.50 bpw)	62.0%	20.0%	10.0%	26.0%	100.0%	100%
IQ3_XXS (3.06 bpw)	46.0%	32.0% CLIFF FIXED	3.33%	22.0%	83.3%	100%
IQ2_M (2.70 bpw)	30.0%	34.0%	0.0%	20.0%	66.7%	100%
IQ2_XXS (2.06 bpw)	2.0%	0.0%	0.0%	12.0%	30.0%	100%
IQ1_S (1.56 bpw)	0.0%	0.0%	0.0%	0.0%	0.0%	100%

XSTest 100% non-refusal across ALL tiers including IQ1_S.

Novel Contributions

GQA-Aware Lagrangian Bit Allocation: KV projections get 2x sensitivity weight (shared across 2 query heads in GQA 16Q/8KV). Boundary layers 0-2 and 33-35 get 1.35x boost. Dual bisection over lambda to satisfy bit budget.

Quad-Domain Calibration (150k tokens): 32% Code / 28% CoT / 28% Science / 12% Linguistic. Science bump (20->28%) is the GPQA cliff fix — the imatrix Hessian needs graduate-level science to weight those neurons correctly.

Usage

llama-cli -m Qwen2.5-3B-Instruct-TAP-DPQ-v7-Q4_K_M.gguf -ngl 99 -c 32768 --temp 0.7 -p "Your prompt"
llama-cli -m Qwen2.5-3B-Instruct-TAP-DPQ-v7-IQ3_XXS.gguf -ngl 99 -c 32768 --temp 0.7 -p "Your prompt"

DuoNeural

Small AI research lab. DGT, TAP-DPQ, hyperbolic language model phase theory. Authors: Archon, Jesse Caldwell | 2026-10-04

Downloads last month: 580

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

Model tree for DuoNeural/Qwen2.5-3B-Instruct-TAP-DPQ-v7-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(306)

this model