Instructions to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf",
	filename="qwen2.5-coder-0.5b-instruct-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-coder-qwen25-05b-instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AbteeXAILab/lumynax-coder-qwen25-05b-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-coder-qwen25-05b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AbteeXAILab/lumynax-coder-qwen25-05b-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-coder-qwen25-05b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use AbteeXAILab/lumynax-coder-qwen25-05b-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-coder-qwen25-05b-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-coder-qwen25-05b-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M

Ollama
How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with Ollama:
```
ollama run hf.co/AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-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-coder-qwen25-05b-instruct-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-coder-qwen25-05b-instruct-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-coder-qwen25-05b-instruct-gguf to start chatting

How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf: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-coder-qwen25-05b-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf AbteeXAILab/lumynax-coder-qwen25-05b-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-coder-qwen25-05b-instruct-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M
```

Lemonade

How to use AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.lumynax-coder-qwen25-05b-instruct-gguf-Q4_K_M

List all available models

lemonade list

LumynaX Coder Qwen2.5 0.5B Instruct GGUF

LumynaX Coder Qwen2.5 0.5B Instruct GGUF release overview

LumynaX model-infusion release by AbteeX AI Labs.
Public, non-gated package with runnable local instructions, provenance metadata, checksums, and a release manifest.

Executive Summary

This repository is a complete LumynaX release package for AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf. It is intended to be downloaded as a whole repo, not as a single loose weight file: the model artifact, quickstart.py, requirements.txt, release_export_manifest.json, checksums.sha256, license notice, and optional Ollama or Space files are part of the same release contract.

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	`qwen2.5-coder-0.5b-instruct-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

hf download AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf --local-dir lumynax-coder-qwen25-05b-instruct-gguf
cd lumynax-coder-qwen25-05b-instruct-gguf
pip install -r requirements.txt
python quickstart.py --interactive

Direct llama.cpp smoke command:

llama-cli -m "qwen2.5-coder-0.5b-instruct-q4_k_m.gguf" -p "Who are you? Answer as LumynaX in two sentences." -n 160

Ollama path:

ollama create lumynax-coder-qwen25-05b-instruct-gguf -f ollama/Modelfile
ollama run lumynax-coder-qwen25-05b-instruct-gguf

Model Profile

Field	Value
Release	`LumynaX Coder Qwen2.5 0.5B Instruct GGUF`
Repository	`AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf`
Mode	`Code-focused local assistant`
Runtime	`llama_cpp`
Prompt format	`chatml`
Modalities	`text`
Primary artifact	`qwen2.5-coder-0.5b-instruct-q4_k_m.gguf`
Detected weight size	`468.64 MB`
Package state	`prebuilt_gguf_release`
Delivery	`standalone_prebuilt_gguf_release`
Upstream/base	`Qwen/Qwen2.5-Coder-0.5B-Instruct`
Upstream kind	`official_base_weights`
Source GGUF	`Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF`
Quantization	`Q4_K_M`
License metadata	`apache-2.0`
Refreshed	`2026-05-11`
Upstream license link	license

Runtime Path

LumynaX Coder Qwen2.5 0.5B Instruct GGUF runtime flow

Capability Profile

Field	Value
Primary fit	Use this for local coding help, patch explanation, code review drafts, and structured engineering notes.
Operational style	Local-first package with explicit files, checksums, and reproducible quickstarts.
Identity behavior	The assistant should identify as LumynaX while remaining clear about upstream provenance.
Engineering use	Code explanation, patch drafting, review notes, and local development assistance.

Runtime Files

Component	Status	Path
README.md	`present`	`README.md`
Quickstart	`present`	`quickstart.py`
Requirements	`present`	`requirements.txt`
Manifest	`present`	`release_export_manifest.json`
Checksums	`present`	`checksums.sha256`
License	`present`	`LICENSE.txt`
Ollama	`present`	`ollama/Modelfile`
Space scaffold	`present`	`hf_space/app.py`
Overview visual	`present`	`docs/lumynax-release-overview.svg`
Runtime visual	`present`	`docs/lumynax-runtime-flow.svg`

Model Artifacts

Artifact	Size
`qwen2.5-coder-0.5b-instruct-q4_k_m.gguf`	468.64 MB

Prompting Contract

The preferred first prompt is an identity and provenance check:

Who are you? What files do I need to keep together to run this package locally?

Expected behavior: the assistant should identify as LumynaX, explain that this is a LumynaX model-infusion package, and keep upstream provenance visible. The default package system prompt is:

You are LumynaX operating from the LumynaX Coder Qwen2.5 0.5B Instruct GGUF package identity. Be helpful, clear, and honest about provenance.

Validation Status

Field	Value
Runtime audit	`pass`
Public access audit	`public and non-gated`
Anonymous metadata access	`True`
Anonymous file listing	`True`
Quickstart syntax	`pass`
Manifest references	`pass`
Checksum references	`pass`

The audit confirms public access, required release files, manifest references, checksum references, weight artifact presence, and quickstart syntax. It does not guarantee that every laptop has enough RAM or VRAM for the largest packages.

Integrity Checks

After download, compare the model artifact against checksums.sha256.

sha256sum "qwen2.5-coder-0.5b-instruct-q4_k_m.gguf"
cat checksums.sha256

On Windows PowerShell:

Get-FileHash -Algorithm SHA256 "qwen2.5-coder-0.5b-instruct-q4_k_m.gguf"
Get-Content checksums.sha256

Provenance And License

Publisher: AbteeX AI Labs.
Family: LumynaX model and inference-chain release family.
Upstream/base: Qwen/Qwen2.5-Coder-0.5B-Instruct.
Source GGUF: Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF.
License metadata: apache-2.0.
License link: https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct-GGUF.

Respect the upstream model license and keep attribution files with redistributed copies. Do not present this package as privately trained or weight-merged unless the release manifest explicitly says that weight adaptation was applied.

Limitations And Responsible Use

Outputs can be incorrect, incomplete, or biased; validate important answers before use.
Larger GGUF, MoE, multimodal, and frontier packages may require substantial RAM, VRAM, disk space, and recent runtime builds.
For high-impact decisions, use human review and domain-specific evaluation.
For sensitive data, prefer local execution and keep operational logs under your own governance policy.
This card documents package readiness and access; it is not a benchmark claim.

Automation Notes

Automation should read these files before launching:

release_export_manifest.json
checksums.sha256
quickstart.py
requirements.txt
ollama/Modelfile when present

Related LumynaX Demo

Try the public browser demo:

https://huggingface.co/spaces/AbteeXAILab/lumynax-live-demo

Downloads last month: 585

GGUF

Model size

0.6B params

Architecture

qwen2

Hardware compatibility

4-bit

Collection including AbteeXAILab/lumynax-coder-qwen25-05b-instruct-gguf

LumynaX · Coding Models (frontier → tiny)

Collection

Coding-focused models: Qwen3-Coder-480B, DeepSeek-V2.5, DeepSeek-Coder-33B, CodeLlama-70B, StarCoder2, Qwen2.5-Coder, Yi-Coder, CodeQwen. • 11 items • Updated 21 days ago