Instructions to use AbteeXAILab/lumynax-doc-donut-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbteeXAILab/lumynax-doc-donut-base with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="AbteeXAILab/lumynax-doc-donut-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("AbteeXAILab/lumynax-doc-donut-base") model = AutoModelForMultimodalLM.from_pretrained("AbteeXAILab/lumynax-doc-donut-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
LumynaX Doc Donut Base (document understanding)
Legacy release · Outdated research artifact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 It is no longer maintained, is not recommended for production, and does not represent the current capabilities, architecture, or safety standards of AbteeX AI Labs.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.
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 | naver-clova-ix/donut-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| Status | Outdated and retained for research provenance only |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.
AbteeX AI Labs · Aotearoa New Zealand
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Model tree for AbteeXAILab/lumynax-doc-donut-base
Base model
naver-clova-ix/donut-base