This is the official GGUF quantized release of llama-nexora-vector-v0.1, published by Open4bits — the official quantization project under ArkAiLabs. Multiple quantization levels are provided to suit a wide range of hardware configurations. This is a beta release intended for research, prototyping, and early-stage development workflows only.
llama-nexora-vector-v0.1-GGUF contains the official GGUF quantized versions of llama-nexora-vector-v0.1 — an experimental text-to-vector model from the Llama-Nexora family that generates structured SVG graphics from natural language prompts.
These quantized releases are published by Open4bits, the dedicated quantization project under ArkAiLabs, and are compatible with local inference tools such as llama.cpp, Ollama, and LM Studio on Windows, Linux, and macOS.
This release is in beta and is scoped to research, experimentation, and early-stage design tooling. All outputs should be validated before use in any downstream pipeline.
The Llama-Nexora Family
This model is part of the Llama-Nexora family — a dedicated branch of Nexora models under ArkAiLabs, built on the Meta Llama architecture and focused on creative, efficient, and practical open AI systems.
Jan — Open-source ChatGPT alternative for local use
Installation & Usage
llama.cpp
bash
1# Clone and build llama.cpp2git clone https://github.com/ggerganov/llama.cpp
3cd llama.cpp &&make45# Download the model (example: Q4_K_M)6huggingface-cli download Open4bits/llama-nexora-vector-v0.1-GGUF \7 llama-nexora-vector-v0.1.Q4_K_M.gguf \8 --local-dir ./models
910# Run inference11./llama-cli -m ./models/llama-nexora-vector-v0.1.Q4_K_M.gguf \12 -p "Generate an SVG of a simple red circle."\13 -n 512
Ollama
bash
1# Create a Modelfile2echo'FROM ./llama-nexora-vector-v0.1.Q4_K_M.gguf'> Modelfile
34# Create the model5ollama create llama-nexora-vector -f Modelfile
67# Run it8ollama run llama-nexora-vector "Generate an SVG of a simple red circle."
LM Studio
Open LM Studio and go to the Search tab.
Search for Open4bits/llama-nexora-vector-v0.1-GGUF.
Select your preferred quantization and download.
Load the model and start prompting.
Capabilities
llama-nexora-vector-v0.1-GGUF is designed to translate textual instructions into structured SVG code. The model is best suited for:
Generating SVG markup for simple vector graphics
Producing geometric shapes and basic illustrations
Creating icons, shapes, logos, and simple illustrations
Supporting rapid prototyping and concept design
Producing lightweight scalable vector outputs
Tip: The model performs best with concise, clearly scoped prompts focused on simple visual compositions.
Limitations
This is an early-stage beta release. Users should be aware of the following constraints before integrating the model:
High hallucination rate — outputs may be invalid or non-renderable SVG
Limited generalization — dataset size affects output consistency across diverse prompts
Weak complex scene handling — highly detailed or multi-element prompts may produce poor results
Manual correction required — outputs should be validated and post-processed before use
Not production-ready — not suitable for safety-critical or automated pipelines
Quantization trade-off — lower-bit quants (Q2, Q4) may show more quality degradation versus the full-precision model
Intended Use
✅ Supported Use Cases
Academic and applied research in text-to-vector generation
Experimental AI-assisted design systems
Educational exploration of structured output generation
Lightweight SVG prototyping and ideation on local hardware
❌ Out-of-Scope Use Cases
Production-grade or commercial vector asset pipelines
High-precision design deliverables without human validation
Automated systems where SVG correctness is required without manual review
Usage Recommendations
To get the best results from this model:
Keep prompts simple and specific — avoid multi-scene or highly complex compositions
Validate all SVG outputs before rendering or integrating into any pipeline
Post-process outputs to correct syntax or structural issues
Use iterative prompting — refining prompts across multiple turns often yields better results
Expect imperfections — this is a beta model; treat outputs as drafts, not finals
Human review is recommended for all generated content
Choose the right quant — higher-bit quants yield better output quality if your hardware allows
Risks & Considerations
Developers integrating this model should account for the following risks:
Generation of malformed or non-functional SVG code
Inconsistent instruction following across prompt variations
Unpredictable outputs due to limited training data coverage
Outputs may sometimes be invalid, incomplete, or require manual correction
Quality degradation versus full-precision model, especially at lower bit widths
Recommendation: Implement downstream validation layers and SVG syntax checking before any rendering or integration. Human review is recommended for all generated content.
Community & Support
Join the community for updates, feedback, and discussion. Community feedback, testing, and contributions are welcome — this project will continue evolving through open research and real-world experimentation.
This model is released under the Llama 3.2 Community License.
Use of this model is governed by the Llama 3.2 Community License Agreement. Please review the license terms before use, modification, or distribution.
Acknowledgements
This quantized release is based on llama-nexora-vector-v0.1 by ArkAiLabs, which itself is built upon Llama 3.2 1B Instruct by Meta. Quantization was performed by Open4bits using the llama.cpp GGUF quantization toolchain. We thank the open-source AI community for their continued contributions that make projects like this possible.
About Open4bits
Open4bits is the official quantization project under ArkAiLabs, dedicated to publishing efficient, accessible quantized versions of Nexora and Llama-Nexora models across multiple formats (GGUF, MLX) for local inference on a wide range of hardware.
About Nexora & Llama-Nexora
Nexora is an experimental AI initiative under ArkAiLabs, focused on building lightweight, practical, and creative AI systems for real-world applications.
The Llama-Nexora family is a dedicated branch within Nexora, built on the Meta Llama architecture — focused on creative, efficient, and practical open AI systems that are accessible to the broader community.
