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Model: ArkAiLab-Adl/nexora-vector-v0.1 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model:
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- Qwen/Qwen3-4B
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tags:
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- nexora
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- chat
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- qwen3
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- conversational
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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<p align="center">
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<img src="https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1/resolve/main/assets/nexora-vector.png" alt="Nexora-Vector"/>
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</p>
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# Nexora-Vector-v0.1
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<p align="center">
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<img src="https://img.shields.io/badge/status-beta-orange" alt="Status: Beta"/>
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<img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License: Apache 2.0"/>
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<img src="https://img.shields.io/badge/base_model-Qwen3--4B-blueviolet" alt="Base Model"/>
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<img src="https://img.shields.io/badge/output-SVG-green" alt="Output: SVG"/>
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</p>
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> **Nexora-Vector-v0.1** is an experimental text-to-vector model that generates structured SVG graphics from natural language prompts. This is the inaugural release of the Nexora Vector series, intended for research, prototyping, and early-stage development workflows.
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---
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## ⚠️ Update Notice
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An issue was identified in the initial release where an incorrect base model was uploaded.
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This has now been **fully corrected**, and the current version is properly based on **Qwen3-4B**.
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Users are advised to **re-download the latest version** to ensure correct behavior and performance.
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---
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## Table of Contents
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- [Overview](#overview)
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- [Model Details](#model-details)
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- [Capabilities](#capabilities)
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- [Limitations](#limitations)
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- [Intended Use](#intended-use)
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- [Architecture & Training](#architecture--training)
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- [Usage Recommendations](#usage-recommendations)
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- [Quantized Versions](#quantized-versions)
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- [Evaluation](#evaluation)
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- [Risks & Considerations](#risks--considerations)
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- [Future Work](#future-work)
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- [Community & Support](#community--support)
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- [License](#license)
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- [Acknowledgements](#acknowledgements)
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---
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## Overview
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Nexora-Vector-v0.1 is a supervised fine-tuned language model built on top of **Qwen3-4B**, adapted specifically to generate structured vector graphics in SVG format from natural language instructions.
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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.
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---
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## Capabilities
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Nexora-Vector-v0.1 is designed to translate textual instructions into structured SVG code. The model is best suited for:
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- Generating SVG markup for simple vector graphics
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- Producing geometric shapes and basic illustrations
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- Creating lightweight icons and minimal design assets
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- Supporting rapid prototyping in vector-based design workflows
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> **Tip:** The model performs best with concise, clearly scoped prompts focused on simple visual compositions.
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---
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## Limitations
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This is an early-stage beta release. Users should be aware of the following constraints before integrating the model:
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- **High hallucination rate** — outputs may be invalid or non-renderable SVG
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- **Limited generalization** — the small dataset size affects output consistency
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- **Weak complex scene handling** — highly detailed or multi-element prompts may produce poor results
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- **Manual correction required** — outputs should be validated and post-processed before use
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- **Not production-ready** — not suitable for safety-critical or automated pipelines
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---
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## Intended Use
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### ✅ Supported Use Cases
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- Academic and applied research in text-to-vector generation
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- Experimental AI-assisted design systems
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- Educational exploration of structured output generation
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- Lightweight SVG prototyping and ideation
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### ❌ Out-of-Scope Use Cases
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- Production-grade or commercial vector asset pipelines
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- High-precision design deliverables without human validation
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- Automated systems where SVG correctness is required without manual review
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---
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## Architecture & Training
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The model is built on **Qwen3-4B** and fine-tuned using supervised learning to improve structured SVG output generation.
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### Training Configuration
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| Parameter | Details |
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|---|---|
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| **Fine-tuning Method** | Supervised Fine-Tuning (SFT) |
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| **Dataset Composition** | Curated prompt–SVG pairs |
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| **Dataset Size** | ~1,500 samples |
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| **Training Objective** | Structured output generation for SVG formats |
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> **Note:** The relatively small dataset size may result in instability and limited generalization across diverse prompts. Improved dataset coverage is planned for future versions.
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---
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## Usage Recommendations
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To get the best results from Nexora-Vector-v0.1:
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1. **Keep prompts simple and specific** — avoid multi-scene or highly complex compositions
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2. **Validate all SVG outputs** before rendering or integrating into any pipeline
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3. **Post-process outputs** to correct syntax or structural issues
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4. **Use iterative prompting** — refining prompts across multiple turns often yields better results
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5. **Expect imperfections** — this is a beta model; treat outputs as drafts, not finals
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---
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## Quantized Versions
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Official quantized releases are available via **[Open4bits](https://huggingface.co/Open4bits)** — the dedicated quantization project under **ArkAiLabs** — for efficient local inference across different hardware platforms:
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| Version | Format | Link |
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| **GGUF** (Q2_K / Q4_K_M / Q6_K / Q8_0) | GGUF | [Open4bits/nexora-vector-v0.1-GGUF](https://huggingface.co/Open4bits/nexora-vector-v0.1-GGUF) |
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| **MLX 4-Bit** (Apple Silicon) | MLX | [Open4bits/nexora-vector-v0.1-mlx-4Bit](https://huggingface.co/Open4bits/nexora-vector-v0.1-mlx-4Bit) |
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- Use the **GGUF** version for local inference on Windows, Linux, or macOS with tools like `llama.cpp`, Ollama, or LM Studio.
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- Use the **MLX** version for optimized inference on Apple Silicon (M1/M2/M3/M4) via the MLX framework.
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---
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## Evaluation
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Nexora-Vector-v0.1 has not yet undergone formal benchmark evaluation. Current assessment is qualitative, based on manual testing of SVG generation tasks.
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Planned evaluation metrics for future releases include:
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| Metric | Description |
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| **SVG Validity Rate** | Percentage of outputs that are parseable, valid SVG |
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| **Structural Correctness** | Adherence to SVG schema and element hierarchy |
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| **Prompt Adherence** | Alignment between user intent and generated output |
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| **Visual Consistency** | Stability of outputs across similar prompts |
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---
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## Risks & Considerations
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Developers integrating Nexora-Vector-v0.1 should account for the following risks:
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- Generation of malformed or non-functional SVG code
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- Inconsistent instruction following across prompt variations
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- Unpredictable outputs due to limited training data coverage
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**Recommendation:** Implement downstream validation layers and SVG syntax checking before any rendering or integration.
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---
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## Future Work
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The following improvements are planned for upcoming versions of the Nexora Vector series:
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- [ ] Expanded and more diverse training dataset
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- [ ] Improved SVG syntax correctness and validity rates
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- [ ] Reduced hallucination rates
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- [ ] Enhanced natural language understanding for complex prompts
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- [ ] Support for richer vector compositions and multi-element scenes
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- [ ] Formal benchmark evaluation suite
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---
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## Community & Support
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Join the community for updates and discussion:
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💬 **[Join our Discord Server](https://discord.gg/mwdrgYbzuG)**
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---
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## License
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This model is released under the **Apache License 2.0**.
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You may use, modify, and distribute this model in accordance with the terms of the Apache 2.0 license. See the [LICENSE](./LICENSE) file for full details, or refer to the [official Apache 2.0 license text](https://www.apache.org/licenses/LICENSE-2.0).
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---
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## Acknowledgements
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Nexora-Vector-v0.1 is built upon **[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)** by the Qwen team. We thank the open-source AI community for their continued contributions that make projects like this possible.
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---
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## About Nexora
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**Nexora** is an experimental AI initiative under **ArkAiLabs**, focused on building lightweight, practical, and creative AI systems for real-world applications. The Nexora Vector series represents our exploration into AI-assisted vector graphics generation.
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