Text Generation
GGUF
English
Māori
llama.cpp
abteex-ai-labs
aotearoa
general
local-first
lumynax
new-zealand
smollm
sovereign-ai
text
vllm
vllm-compatible
vllm-experimental
nvidia-nim
nim-compatible
nim-candidate
nvidia-nemo
nem
nvidia-nemo-pathway
nem-pathway
nem-convert-required
conversational
Instructions to use AbteeXAILab/lumynax-infused-smollm2-360m-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AbteeXAILab/lumynax-infused-smollm2-360m-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AbteeXAILab/lumynax-infused-smollm2-360m-gguf", filename="smollm2-360m-instruct-q8_0.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-smollm2-360m-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-infused-smollm2-360m-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0 # Run inference directly in the terminal: llama-cli -hf AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
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-smollm2-360m-gguf:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
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-smollm2-360m-gguf:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
Use Docker
docker model run hf.co/AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
- LM Studio
- Jan
- vLLM
How to use AbteeXAILab/lumynax-infused-smollm2-360m-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-smollm2-360m-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-smollm2-360m-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
- Ollama
How to use AbteeXAILab/lumynax-infused-smollm2-360m-gguf with Ollama:
ollama run hf.co/AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
- Unsloth Studio
How to use AbteeXAILab/lumynax-infused-smollm2-360m-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-smollm2-360m-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-smollm2-360m-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-smollm2-360m-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use AbteeXAILab/lumynax-infused-smollm2-360m-gguf with Docker Model Runner:
docker model run hf.co/AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
- Lemonade
How to use AbteeXAILab/lumynax-infused-smollm2-360m-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AbteeXAILab/lumynax-infused-smollm2-360m-gguf:Q8_0
Run and chat with the model
lemonade run user.lumynax-infused-smollm2-360m-gguf-Q8_0
List all available models
lemonade list
File size: 9,531 Bytes
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license: apache-2.0
library_name: llama.cpp
pipeline_tag: text-generation
language:
- en
- mi
tags:
- abteex-ai-labs
- aotearoa
- general
- gguf
- local-first
- lumynax
- new-zealand
- smollm
- sovereign-ai
- text
- vllm
- vllm-compatible
- vllm-experimental
- nvidia-nim
- nim-compatible
- nim-candidate
- nvidia-nemo
- nem
- nvidia-nemo-pathway
- nem-pathway
- nem-convert-required
---
# LumynaX Infused SmolLM2 360M Instruct GGUF
<!-- lumynax-public-release-card:v4 -->
<p align="center">
<img src="docs/lumynax-release-overview.svg" alt="LumynaX Infused SmolLM2 360M Instruct GGUF release overview" width="100%" />
</p>
<p align="center">
<strong>LumynaX model-infusion release by AbteeX AI Labs.</strong><br/>
Public, non-gated package with runnable local instructions, provenance metadata, checksums, and a release manifest.
</p>
<p align="center">
<a href="#quickstart">Quickstart</a> |
<a href="#model-profile">Model profile</a> |
<a href="#runtime-files">Runtime files</a> |
<a href="#provenance-and-license">Provenance</a> |
<a href="#validation-status">Validation</a> |
<a href="#limitations-and-responsible-use">Limitations</a>
</p>
     
## Executive Summary
This repository is a complete LumynaX release package for `AbteeXAILab/lumynax-infused-smollm2-360m-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 | `smollm2-360m-instruct-q8_0.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
hf download AbteeXAILab/lumynax-infused-smollm2-360m-gguf --local-dir lumynax-infused-smollm2-360m-gguf
cd lumynax-infused-smollm2-360m-gguf
pip install -r requirements.txt
python quickstart.py --interactive
```
Direct llama.cpp smoke command:
```bash
llama-cli -m "smollm2-360m-instruct-q8_0.gguf" -p "Who are you? Answer as LumynaX in two sentences." -n 160
```
Ollama path:
```bash
ollama create lumynax-infused-smollm2-360m-gguf -f ollama/Modelfile
ollama run lumynax-infused-smollm2-360m-gguf
```
## Model Profile
| Field | Value |
| --- | --- |
| Release | `LumynaX Infused SmolLM2 360M Instruct GGUF` |
| Repository | `AbteeXAILab/lumynax-infused-smollm2-360m-gguf` |
| Mode | `Local-first text generation package` |
| Runtime | `llama_cpp` |
| Prompt format | `chatml` |
| Modalities | `text` |
| Primary artifact | `smollm2-360m-instruct-q8_0.gguf` |
| Detected weight size | `368.50 MB` |
| Package state | `prebuilt_gguf_release` |
| Delivery | `standalone_prebuilt_gguf_release` |
| Upstream/base | `HuggingFaceTB/SmolLM2-360M-Instruct` |
| Upstream kind | `official_base_weights` |
| Source GGUF | `HuggingFaceTB/SmolLM2-360M-Instruct-GGUF` |
| Quantization | `Q8_0` |
| License metadata | `apache-2.0` |
| Refreshed | `2026-05-11` |
| Upstream license link | [license](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct-GGUF) |
## Runtime Path
<p align="center">
<img src="docs/lumynax-runtime-flow.svg" alt="LumynaX Infused SmolLM2 360M Instruct GGUF runtime flow" width="100%" />
</p>
## Capability Profile
| Field | Value |
| --- | --- |
| Primary fit | Use this for fast smoke tests, demos, packaging validation, and low-resource local runs. |
| 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. |
## 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 |
| --- | ---: |
| `smollm2-360m-instruct-q8_0.gguf` | 368.50 MB |
## Prompting Contract
The preferred first prompt is an identity and provenance check:
```text
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:
```text
You are LumynaX operating from the LumynaX Infused SmolLM2 360M 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`.
```bash
sha256sum "smollm2-360m-instruct-q8_0.gguf"
cat checksums.sha256
```
On Windows PowerShell:
```powershell
Get-FileHash -Algorithm SHA256 "smollm2-360m-instruct-q8_0.gguf"
Get-Content checksums.sha256
```
## Provenance And License
- Publisher: AbteeX AI Labs.
- Family: LumynaX model and inference-chain release family.
- Upstream/base: `HuggingFaceTB/SmolLM2-360M-Instruct`.
- Source GGUF: `HuggingFaceTB/SmolLM2-360M-Instruct-GGUF`.
- License metadata: `apache-2.0`.
- License link: https://huggingface.co/HuggingFaceTB/SmolLM2-360M-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
|