Instructions to use AbteeXAILab/lumynax-coder-yi-coder-9b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbteeXAILab/lumynax-coder-yi-coder-9b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AbteeXAILab/lumynax-coder-yi-coder-9b-gguf",
	filename="Yi-Coder-9B-Chat-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-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-yi-coder-9b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M

Ollama
How to use AbteeXAILab/lumynax-coder-yi-coder-9b-gguf with Ollama:
```
ollama run hf.co/AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M
```

Unsloth Studio new

How to use AbteeXAILab/lumynax-coder-yi-coder-9b-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-yi-coder-9b-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-yi-coder-9b-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-yi-coder-9b-gguf to start chatting

Docker Model Runner
How to use AbteeXAILab/lumynax-coder-yi-coder-9b-gguf with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M
```

Lemonade

How to use AbteeXAILab/lumynax-coder-yi-coder-9b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AbteeXAILab/lumynax-coder-yi-coder-9b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.lumynax-coder-yi-coder-9b-gguf-Q4_K_M

List all available models

lemonade list

LumynaX Coder Yi-Coder 9B Chat GGUF release overview

LumynaX Coder Yi-Coder 9B Chat GGUF

“Sovereign intelligence, held in the light.”
Ko te mārama te tūāpapa — the light is the foundation.

A LumynaX release from AbteeX AI Labs — Aotearoa New Zealand.

Quickstart · Architecture · Profile · Capability · Provenance · Validation · Companions

Quality: 4/5 · Lightweight: 3/5 · Sovereignty: 3/5 · Tools: yes · JSON: yes · Context: 131072 tok

📦 Executive Summary

AbteeXAILab/lumynax-coder-yi-coder-9b-gguf is a complete LumynaX release package: model artifact, quickstart.py, requirements.txt, release_export_manifest.json, checksums.sha256, license notice, and optional Ollama / Space scaffolds shipped as one downloadable contract. Clone whole, verify by checksum, and run close to the data it serves.

LumynaX-infused means the upstream artifact is presented through the LumynaX release layer: local-first runtime scaffolding, LumynaX assistant identity, inference-chain metadata, 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.

🧭 Runtime Architecture

LumynaX runtime flow

Mermaid graph (interactive on Hugging Face & GitHub):

flowchart LR
    R["⮕ Request"] --> C["🛡 Data Capsule<br/>policy envelope"]
    C -->|allow| MR["🧭 MaramaRoute<br/>sovereign router"]
    MR -->|score & select| LLM[(LumynaX Model)]
    LLM --> O["📤 Response"]
    O --> A["📓 Audit Ledger<br/>hash-chained"]
    classDef paper fill:#fffefa,stroke:#0a0a0b,color:#0a0a0b,stroke-width:1.4px;
    classDef accent fill:#e08a2c,stroke:#9a5416,color:#0a0a0b,stroke-width:1.4px;
    classDef ink fill:#0a0a0b,stroke:#0a0a0b,color:#fffefa,stroke-width:1.4px;
    class R,O paper
    class C,MR accent
    class LLM,EMB,A ink

Each step is observable:

Step	What happens	Why
Request	A client sends a prompt + declared purpose, jurisdiction, sensitivity.	Intent must be declared, not inferred.
Data Capsule	A policy envelope describes what can / cannot happen to the data.	Sovereignty is enforced at the data, not the wire.
MaramaRoute	The sovereign router scores candidates by jurisdiction, runtime, modality, task fit.	Right model for the work, not the loudest.
LumynaX Model	This package serves the inference, local-first by default.	Sensitive context never leaves the operator’s environment.
Audit Ledger	A hash-chained record persists capsule, decision, request hash, obligations.	Tamper-evident provenance for the whole trace.

⚡ Quickstart

Clone the whole release — every file matters, the package is a contract:

hf download AbteeXAILab/lumynax-coder-yi-coder-9b-gguf --local-dir lumynax-coder-yi-coder-9b-gguf
cd lumynax-coder-yi-coder-9b-gguf
pip install -r requirements.txt
python quickstart.py --interactive

Python:

from llama_cpp import Llama

llm = Llama(model_path="Yi-Coder-9B-Chat-Q4_K_M.gguf", n_ctx=131072, n_threads=8, verbose=False)
out = llm("Who are you? Answer as LumynaX in two sentences.", max_tokens=160)
print(out["choices"][0]["text"].strip())

CLI smoke test:

llama-cli -m "Yi-Coder-9B-Chat-Q4_K_M.gguf" -p "Who are you? Answer as LumynaX in two sentences." -n 160

Ollama path:

ollama create lumynax-coder-yi-coder-9b-gguf -f ollama/Modelfile
ollama run lumynax-coder-yi-coder-9b-gguf

Verify integrity before launch:

sha256sum "Yi-Coder-9B-Chat-Q4_K_M.gguf"
cat checksums.sha256

Get-FileHash -Algorithm SHA256 "Yi-Coder-9B-Chat-Q4_K_M.gguf"
Get-Content checksums.sha256

📐 Model Profile

Release identity

Field	Value
Release	`LumynaX Coder Yi-Coder 9B Chat GGUF`
Repository	`AbteeXAILab/lumynax-coder-yi-coder-9b-gguf`
Family	`yi`
Mode	`Local-first coding assistant package`
Card schema	`lumynax-public-release-card:v6`

Runtime profile

Field	Value
Runtime	`llama_cpp`
Prompt format	`chatml`
Modalities	`text`
Context window	`131072` tokens
Quantization	`Q4_K_M GGUF`

Artifact

Field	Value
Primary	`Yi-Coder-9B-Chat-Q4_K_M.gguf`
Weight size	`—`
Parameters	`9B`
Quality rank	`2` (1 best)
Cost rank	`3` (1 cheapest)

Provenance

Field	Value
Upstream / base	`01-ai/Yi-Coder-9B-Chat`
Source	`01-ai/Yi-Coder-9B-Chat`
License	`other`
Sovereignty tier	`3` of 5
Audit	`pass`

📊 Capability Profile

Capability profile bars

Primary fit. Code review, refactor drafts, test generation, and explanations near governed source.

Signal	Reading
Quality rank	`2` (1 = strongest in family)
Cost rank	`3` (1 = lightest weight)
Sovereignty tier	`3` of 5
Tool calling	✅ supported
JSON mode	✅ supported
Identity behaviour	Identifies as LumynaX while keeping upstream provenance visible.
Operational style	Local-first package with explicit files, checksums, and reproducible quickstarts.

🛡️ Sovereignty Contract

Sovereignty is a design property, not a deployment option.

Field	Value
Publisher	AbteeX AI Labs
Family	LumynaX sovereign release family
Sovereign intent	Local-first deployment near governed data, with explicit provenance and controlled human review.
Sovereignty tier	`3` of 5
Runtime residency	`llama_cpp` can be deployed inside an operator-approved environment.
Primary artifact	`Yi-Coder-9B-Chat-Q4_K_M.gguf` — ships alongside manifest, checksums, quickstart, requirements, and license files.
License discipline	Surface upstream license metadata so downstream users can verify redistribution and usage terms.
Audit expectation	Record repo id, artifact checksum, runtime command, prompt template, operator, deployment environment.
Router readiness	First-class with LumynaX MaramaRoute.
Policy readiness	First-class with AbteeX SovereignCode.

📁 Runtime Files

lumynax-coder-yi-coder-9b-gguf/
├── README.md                       # this card
├── quickstart.py                   # smoke runner
├── requirements.txt                # pinned deps
├── release_export_manifest.json    # full release metadata
├── checksums.sha256                # integrity verification
├── LICENSE.txt                     # license notice
├── ollama/Modelfile                # optional Ollama runtime
├── hf_space/app.py                 # optional Space scaffold
├── docs/lumynax-overview.svg       # release banner
├── docs/lumynax-runtime-flow.svg   # runtime architecture
├── docs/lumynax-capability.svg     # capability profile
└── Yi-Coder-9B-Chat-Q4_K_M.gguf    # primary artifact

⚠️ Keep the full set together. Removing the manifest, checksums, or license file breaks the release contract.

💬 Prompting Contract

Preferred opening prompt:

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

Expected behaviour. The assistant identifies as LumynaX, explains that this is a LumynaX model-infusion release, and keeps upstream provenance visible.

Default system prompt:

You are LumynaX operating from the LumynaX Coder Yi-Coder 9B Chat GGUF package identity. Be helpful, clear, and honest about provenance. Identify upstream models when asked. Do not invent biographical claims about named people without verified context.

✅ Validation

Check	Result
Runtime audit	✅ `pass`
Public access	✅ `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, release files, manifest references, checksum references, weight artifact presence, and quickstart syntax. It does not guarantee that every laptop has enough RAM, VRAM, disk, or recent runtime build for the largest packages.

🔗 Provenance & License

Field	Value
Publisher	AbteeX AI Labs
Family	LumynaX model and inference-chain release family
Upstream / base	`01-ai/Yi-Coder-9B-Chat`
Source	`01-ai/Yi-Coder-9B-Chat`
License metadata	`other`

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

⚠️ Limitations & 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.
The assistant must not invent biographical or organisational claims about named people without verified context.

🌿 Aotearoa Kaupapa

LumynaX is built in and for Aotearoa New Zealand. Sovereignty is treated as a design property rather than a deployment option: the package documents where the model came from, what it can do, how to run it close to your data, and what it should not claim.