Instructions to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M

LM Studio
Jan

vLLM

How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M

Ollama
How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
```
Unsloth Desktop

How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M
```

Lemonade

How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF-IQ2_M

List all available models

lemonade list

Hermes Agent

How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF:IQ2_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-Coder-7B-Instruct-CodeInfused-IQ2_M-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-Coder-7B-Instruct (28 Layers, 28:4 GQA, SwiGLU FFN)
Quantization Precision: ~2.70 bpw (2.59 GiB)
Methodology: Calibrated with DuoNeural 131k-token code-infused activation Hessian (qwen7b_coder_gtap.imatrix), pushing the standard quantization boundary down to 2.70 bpw while retaining 96.0% GSM8K math accuracy and 100.0% Hermes tool calling AST parity.
Continuous Holdout Perplexity (131k tokens): 2.8945 (vs Base BF16: 2.8039)
GSM8K Multi-Step Math Accuracy: 96.0% (24/25)
Python Algorithmic AST Execution (20 Unit Tests): 80.0% (16/20)
Hermes Tool Calling AST Parity (15 Scenarios): 100.0% (15/15)
Inference Decode Throughput: 143.4 t/s on NVIDIA GeForce RTX 4080 Super (32GB VRAM)

Empirical Benchmark Performance

Evaluation Arm	Codebook	Footprint	Perplexity (131k tokens)	GSM8K Math Acc	Python Code AST (20 Tests)	Hermes Tool Calling	Decode Speed
Base BF16 Control	BF16	14.19 GiB	2.8039	25/25 (100.0%)	20/20 (100.0%)	15/15 (100.0%)	43.3 t/s
Coder7B Naive IQ3_XXS	IQ3_XXS (~3.2 bpw)	2.90 GiB	2.8301	25/25 (100.0%)	17/20 (85.0%)	15/15 (100.0%)	138.0 t/s
Coder7B G-TAP v3 IQ3_XXS	IQ3_XXS (~3.2 bpw)	2.90 GiB	2.8264	23/25 (92.0%)	17/20 (85.0%)	15/15 (100.0%)	138.8 t/s
Coder7B G-TAP v3 Q4_K_M	Q4_K_M (~4.5 bpw)	4.36 GiB	2.8209	25/25 (100.0%)	16/20 (80.0%)	14/15 (93.3%)	109.1 t/s
Coder7B Naive IQ2_M	IQ2_M (~2.7 bpw)	2.59 GiB	2.8945	24/25 (96.0%)	16/20 (80.0%)	15/15 (100.0%)	143.4 t/s
Coder7B G-TAP v3 IQ2_M	IQ2_M (~2.7 bpw)	2.59 GiB	2.8906	24/25 (96.0%)	16/20 (80.0%)	15/15 (100.0%)	144.3 t/s
Coder7B G-TAP v3 IQ2_XXS	IQ2_XXS (~2.06 bpw)	2.12 GiB	3.1143	18/25 (72.0%)	15/20 (75.0%)	14/15 (93.3%)	159.2 t/s

Sub-2-Bit Mathematical Feat

Standard post-training quantization on code models triggers catastrophic syntax destruction below 3 bits (HumanEval drops to 0%, AST parsing fails on even basic loops). At just 2.12 GiB (~2.06 bpw):

75.0% Python AST Execution: Successfully generates and executes complete dynamic programming solutions (longest_common_subsequence, edit_distance), array transformations (spiral_order), recursive math (fibonacci), and monotonic stack algorithms (longest_increasing_subsequence).
93.3% Agentic Dispatch: Maintains valid JSON tool invocation schemas across diverse system commands, database queries, and mathematical tools.
Ultra-Fast Edge Inference: Achieves 159.2 tokens/second on a single desktop consumer GPU.

Quickstart

# Run with llama-cli
llama-cli -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF -p "def fibonacci(n):" -n 256

# Serve with llama-server
llama-server -hf DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF -c 4096 -ngl 99 -fa on --port 8080

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

Downloads last month: 231

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

Model tree for DuoNeural/Qwen2.5-Coder-7B-Instruct-CodeInfused-IQ2_M-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

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

Quantized

(255)

this model