Instructions to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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": "AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M

Ollama
How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with Ollama:
```
ollama run hf.co/AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
```
Unsloth Desktop

How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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": "AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M
```

Lemonade

How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.lumynax-infused-qwen25-15b-instruct-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AbteeXAILab/lumynax-infused-qwen25-15b-instruct-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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 "AbteeXAILab/lumynax-infused-qwen25-15b-instruct-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"

LumynaX Infused Qwen2.5 1.5B Instruct GGUF

Legacy release · Outdated research artifact

This model card documents an early LumynaX experiment. It is no longer maintained, is not recommended for production, and does not represent the current capabilities, architecture, or safety standards of AbteeX AI Labs.

How infusion works

LumynaX Core is the core intelligence model. It governs the inference path and integrates selected open-source models as specialised execution layers.

Prompt  →  LumynaX Core  →  Infused model / MoE experts  →  LumynaX Core  →  Response

LumynaX infusion is the controlled composition of LumynaX Core with a compatible open-source model. Depending on the model family and deployment objective, the integration can operate in two ways:

Routed infusion — LumynaX Core directs inference through the selected model without modifying its weights.
MoE infusion — when required by the architecture, compatible model weights can be composed as specialised experts within a Mixture-of-Experts design.

In both cases, LumynaX Core remains the primary intelligence and orchestration layer, applying sovereignty controls, context, agentic planning, and inference optimisation around model execution. Infusion does not automatically imply a weight merge; each release manifest records the method used by that pack.

This release


Infused model	`Qwen/Qwen2.5-1.5B-Instruct`
Infusion method	Routed runtime and identity integration
Weight composition	None — this pack preserves the source-model weights
Runtime	llama.cpp
Release	v1
Status	Outdated and retained for research provenance only

This package predates the current LumynaX Core implementation. Its included identity, runtime, or deployment wrappers are historical release components—not the complete modern LumynaX pipeline.

Archive access

The artifacts remain available for reproducibility. Before evaluation, verify checksums.sha256, inspect release_export_manifest.json, and review LICENSE.txt.

AbteeX AI Labs · Aotearoa New Zealand