AbteeXAILab/lumynax-embed-nomic-v2-moe

LumynaX Embed Nomic v2 MoE (modern retrieval)

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

Quickstart | Model profile | Runtime files | Provenance | Validation | Limitations

Executive Summary

This repository is a complete LumynaX release package for AbteeXAILab/lumynax-embed-nomic-v2-moe. 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 is the source of truth for whether this package is upstream-weight packaging, a dense LumynaX release, or another release mode.

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	embedding runtime can be deployed by the user in their own approved environment.
Model artifact	model.safetensors must stay with manifest, checksums, quickstart, requirements, and license files.
Modalities	embedding
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.

Quickstart

hf download AbteeXAILab/lumynax-embed-nomic-v2-moe --local-dir lumynax-embed-nomic-v2-moe
cd lumynax-embed-nomic-v2-moe
pip install -r requirements.txt
python quickstart.py --interactive

Embedding smoke command:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("AbteeXAILab/lumynax-embed-nomic-v2-moe", trust_remote_code=True)
vectors = model.encode(["LumynaX local retrieval package"])
print(vectors.shape)

Model Profile

Field	Value
Release	LumynaX Embed Nomic v2 MoE (modern retrieval)
Repository	AbteeXAILab/lumynax-embed-nomic-v2-moe
Mode	Retrieval and embedding package
Runtime	embedding
Prompt format	See quickstart.py
Modalities	embedding
Primary artifact	model.safetensors
Detected weight size	1.77 GB
Package state	weights_mirrored
Delivery	published_model_repo
Upstream/base	See release_export_manifest.json
Upstream kind	See manifest
Source GGUF	not applicable
Quantization	fp32 safetensors
License metadata	apache-2.0
Refreshed	2026-05-11

Runtime Path

Capability Profile

Field	Value
Primary fit	Use this for semantic search, retrieval indexing, reranking pipelines, and local knowledge-base workflows.
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.
Retrieval use	Indexing, semantic search, document matching, and knowledge-base workflows.

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
model.safetensors	1.77 GB

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:

See quickstart.py

Validation Status

Field	Value
Runtime audit	not recorded
Public access audit	not recorded
Anonymous metadata access	False
Anonymous file listing	False
Quickstart syntax	not recorded
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 "model.safetensors"
cat checksums.sha256

On Windows PowerShell:

Get-FileHash -Algorithm SHA256 "model.safetensors"
Get-Content checksums.sha256

Provenance And License

• Publisher: AbteeX AI Labs.
• Family: LumynaX model and inference-chain release family.
• Upstream/base: See release_export_manifest.json.
• Source GGUF: not applicable.
• License metadata: apache-2.0.
• License link: LICENSE.txt and upstream model card.

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