Instructions to use AbteeXAILab/lumynax-doc-layoutlmv3-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AbteeXAILab/lumynax-doc-layoutlmv3-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AbteeXAILab/lumynax-doc-layoutlmv3-base")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AbteeXAILab/lumynax-doc-layoutlmv3-base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbteeXAILab/lumynax-doc-layoutlmv3-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AbteeXAILab/lumynax-doc-layoutlmv3-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbteeXAILab/lumynax-doc-layoutlmv3-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AbteeXAILab/lumynax-doc-layoutlmv3-base

SGLang

How to use AbteeXAILab/lumynax-doc-layoutlmv3-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AbteeXAILab/lumynax-doc-layoutlmv3-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbteeXAILab/lumynax-doc-layoutlmv3-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AbteeXAILab/lumynax-doc-layoutlmv3-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AbteeXAILab/lumynax-doc-layoutlmv3-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AbteeXAILab/lumynax-doc-layoutlmv3-base with Docker Model Runner:
```
docker model run hf.co/AbteeXAILab/lumynax-doc-layoutlmv3-base
```

lumynax-doc-layoutlmv3-base / README.md

AbteeXAILabs

chore: standardise legacy release metadata

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	---
	license: cc-by-nc-4.0
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	- mi
	tags:
	- abteex-ai-labs
	- aotearoa
	- doc-ai
	- layout
	- layoutlm
	- local-first
	- lumynax
	- new-zealand
	- sovereign-ai
	- legacy
	- outdated
	base_model: microsoft/layoutlmv3-base
	---

	# LumynaX Doc LayoutLMv3 Base (document layout+text)

	> Legacy release · Outdated research artifact

	This model card documents an early LumynaX experiment. It is no longer maintained, is not recommended for production, and does not represent the current capabilities, architecture, or safety standards of AbteeX AI Labs.

	## How infusion works

	LumynaX Core is the core intelligence model. It governs the inference path and integrates selected open-source models as specialised execution layers.

	```text
	Prompt → LumynaX Core → Infused model / MoE experts → LumynaX Core → Response
	```

	LumynaX infusion is the controlled composition of LumynaX Core with a compatible open-source model. Depending on the model family and deployment objective, the integration can operate in two ways:

	- Routed infusion — LumynaX Core directs inference through the selected model without modifying its weights.
	- MoE infusion — when required by the architecture, compatible model weights can be composed as specialised experts within a Mixture-of-Experts design.

	In both cases, LumynaX Core remains the primary intelligence and orchestration layer, applying sovereignty controls, context, agentic planning, and inference optimisation around model execution. Infusion does not automatically imply a weight merge; each release manifest records the method used by that pack.

	## This release

	\| \| \|
	\|---\|---\|
	\| Infused model \| [`microsoft/layoutlmv3-base`](https://huggingface.co/microsoft/layoutlmv3-base) \|
	\| Infusion method \| Routed runtime and identity integration \|
	\| Weight composition \| None — this pack preserves the source-model weights \|
	\| Runtime \| Transformers \|
	\| Release \| v0.1.0 \|
	\| Status \| Outdated and retained for research provenance only \|

	This package predates the current LumynaX Core implementation. Its included identity, runtime, or deployment wrappers are historical release components—not the complete modern LumynaX pipeline.

	## Archive access

	The artifacts remain available for reproducibility. Before evaluation, verify `checksums.sha256`, inspect `release_export_manifest.json`, and review `LICENSE.txt`.

	- [Source repository](https://github.com/Aimaghsoodi/lumynax-doc-layoutlmv3-base)
	- [AbteeX AI Labs](https://abteex.com)
	- [LumynaX](https://lumynax.com)
	- [Contact](mailto:aimaghsoodi@abteex.com)

	---

	AbteeX AI Labs · Aotearoa New Zealand