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AbteeXAILab/lumynax-frontier-olmo2-32b-instruct
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
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:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AbteeXAILab/lumynax-frontier-olmo2-32b-instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AbteeXAILab/lumynax-frontier-olmo2-32b-instruct", device_map="auto", trust_remote_code=True)
inputs = tokenizer("Who are you? Answer as LumynaX in two sentences.", return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=160)[0], skip_special_tokens=True))
⚠️
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 Frontier OLMo-2 32B Instruct (fully open) 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
allenai/OLMo-2-0325-32B-Instruct
Source
AllenAI OLMo-2
License metadata
apache-2.0
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.
Ko te mārama te tūāpapa
— the light is the foundation.
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