Instructions to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf",
	filename="lumynax-infused-gemma4-26b-a4b-ud-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-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-gemma4-26b-a4b-gguf:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-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-gemma4-26b-a4b-gguf:UD-Q4_K_M
# Run inference directly in the terminal:
llama cli -hf AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-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-gemma4-26b-a4b-gguf:UD-Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-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-gemma4-26b-a4b-gguf:UD-Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M

LM Studio
Jan

vLLM

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-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-gemma4-26b-a4b-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-gemma4-26b-a4b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M

Ollama
How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with Ollama:
```
ollama run hf.co/AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M
```

Unsloth Studio

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf to start chatting

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-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-gemma4-26b-a4b-gguf:UD-Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/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-gemma4-26b-a4b-gguf:UD-Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-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-gemma4-26b-a4b-gguf:UD-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-gemma4-26b-a4b-gguf:UD-Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M
```

Lemonade

How to use AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AbteeXAILab/lumynax-infused-gemma4-26b-a4b-gguf:UD-Q4_K_M

Run and chat with the model

lemonade run user.lumynax-infused-gemma4-26b-a4b-gguf-UD-Q4_K_M

List all available models

lemonade list

AbteeXAILabs commited on May 11

Commit

13b5d43

verified ·

1 Parent(s): 9ac3619

docs: publish professional LumynaX model card

Browse files

Files changed (2) hide show

README.md +18 -0
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README.md CHANGED Viewed

@@ -43,6 +43,24 @@ This repository is a complete LumynaX release package for `AbteeXAILab/lumynax-i
 LumynaX-infused means the upstream artifact is presented through the LumynaX release layer: local-first runtime scaffolding, LumynaX assistant identity, inference-chain metadata, public documentation, integrity files, and Aotearoa New Zealand-oriented workflow positioning. The release manifest records this as a LumynaX packaging and inference-chain layer around the listed upstream artifact; it does not claim a private LumynaX weight merge.
 ## Quickstart
 ```bash

 LumynaX-infused means the upstream artifact is presented through the LumynaX release layer: local-first runtime scaffolding, LumynaX assistant identity, inference-chain metadata, public documentation, integrity files, and Aotearoa New Zealand-oriented workflow positioning. The release manifest records this as a LumynaX packaging and inference-chain layer around the listed upstream artifact; it does not claim a private LumynaX weight merge.
+## AbteeX LumynaX Public Surface
+This card follows the AbteeX/LumynaX public-facing system used across the release family: warm paper background visuals, black editorial typography, amber proof markers, compact evidence tables, and plain-language runtime instructions. The goal is not decoration; it is operational clarity. A downloader should immediately understand what the package is, what files belong together, what runtime path is expected, what provenance is available, and what limits still apply.
+## Sovereignty And Run Contract
+| Field | Value |
+| --- | --- |
+| Public surface | AbteeX/LumynaX light editorial system: warm paper, black ink, amber status markers, and evidence-first tables. |
+| Sovereign intent | Package is documented for local-first use, explicit provenance, and controlled deployment near governed data. |
+| Runtime residency | `llama_cpp` runtime can be deployed by the user in their own approved environment. |
+| Model artifact | `lumynax-infused-gemma4-26b-a4b-ud-q4_k_m.gguf` must stay with manifest, checksums, quickstart, requirements, and license files. |
+| Modalities | `text` |
+| License discipline | `apache-2.0` metadata is surfaced so downstream users can check redistribution and usage terms. |
+| Audit expectation | Record repo id, artifact checksum, runtime command, prompt template, operator, and deployment environment for production use. |
+| Router readiness | Compatible with the LumynaX MaramaRoute registry pattern for sovereign model selection and fallback planning. |
+| Local serving | Preferred first path is llama.cpp or llama-cpp-python with checksum verification before launch. |
 ## Quickstart
 ```bash

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