---
license: apache-2.0
base_model: ArkAiLab-Adl/nexora-vector-v0.1
tags:
- nexora
- chat
- qwen3
- conversational
- mlx
- mlx-my-repo
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
# Nexora-Vector-v0.1 · MLX 4-Bit
> **Nexora-Vector-v0.1 MLX 4-Bit** is the official Apple MLX 4-bit quantized release of [Nexora-Vector-v0.1](https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1), published by **[Open4bits](https://huggingface.co/Open4bits)** — an official quantization project under **ArkAiLabs**. Nexora-Vector is an experimental text-to-vector model that generates structured SVG graphics from natural language prompts. This variant is optimized for efficient local inference on Apple Silicon hardware via the MLX framework.
---
## Table of Contents
- [Overview](#overview)
- [Model Details](#model-details)
- [Capabilities](#capabilities)
- [Limitations](#limitations)
- [Intended Use](#intended-use)
- [Architecture & Quantization](#architecture--quantization)
- [Usage Recommendations](#usage-recommendations)
- [Original Model](#original-model)
- [Evaluation](#evaluation)
- [Risks & Considerations](#risks--considerations)
- [Future Work](#future-work)
- [Community & Support](#community--support)
- [License](#license)
- [Acknowledgements](#acknowledgements)
---
## Overview
This is the **official MLX 4-bit quantized** release of Nexora-Vector-v0.1, published by **[Open4bits](https://huggingface.co/Open4bits)** — the official quantization project under **ArkAiLabs** — and converted for use with Apple's [MLX](https://github.com/ml-explore/mlx) framework. The base model is a supervised fine-tuned variant of **Qwen3-4B**, adapted specifically to generate structured vector graphics in SVG format from natural language instructions.
This release is in **beta** and is intended for research, experimentation, and early-stage design tooling on Apple Silicon machines. All outputs should be validated before use in any downstream pipeline.
---
## Model Details
| Property | Details |
|---|---|
| **Model Type** | MLX 4-Bit Quantized |
| **Base Model** | [Nexora-Vector-v0.1](https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1) |
| **Original Base** | Qwen3-4B |
| **Fine-tuning Method** | Supervised Fine-Tuning (SFT) |
| **Quantization** | 4-Bit (MLX) |
| **Target Hardware** | Apple Silicon (M1/M2/M3/M4 series) |
| **Framework** | [MLX](https://github.com/ml-explore/mlx) |
| **Output Format** | SVG |
| **License** | Apache 2.0 |
---
## Capabilities
Nexora-Vector-v0.1 is designed to translate textual instructions into structured SVG code. This MLX version retains all capabilities of the original model while enabling fast, memory-efficient inference on Apple Silicon. The model is best suited for:
- Generating SVG markup for simple vector graphics
- Producing geometric shapes and basic illustrations
- Creating lightweight icons and minimal design assets
- Supporting rapid prototyping in vector-based design workflows on macOS
> **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:
- **High hallucination rate** — outputs may be invalid or non-renderable SVG
- **Limited generalization** — the small training dataset (~1,500 samples) affects output consistency
- **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
- **4-bit quality trade-off** — minor quality degradation is expected compared to the full-precision original model
---
## Intended Use
### ✅ Supported Use Cases
- Academic and applied research in text-to-vector generation on Apple Silicon
- Experimental AI-assisted design systems running locally on macOS
- Educational exploration of structured output generation
- Lightweight SVG prototyping and ideation with low memory overhead
### ❌ 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
- Non-Apple-Silicon hardware (use the [GGUF version](https://huggingface.co/Open4bits/nexora-vector-v0.1-GGUF) instead)
---
## Architecture & Quantization
This model is a 4-bit MLX quantization of the original Nexora-Vector-v0.1 weights, which are themselves a supervised fine-tune of **Qwen3-4B**.
### Quantization Details
| Parameter | Details |
|---|---|
| **Quantization Method** | MLX 4-Bit |
| **Source Model** | [ArkAiLab-Adl/nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1) |
| **Framework** | Apple MLX |
| **Memory Reduction** | ~75% vs. full-precision (fp16) |
| **Target Platform** | macOS with Apple Silicon |
### Original Training Configuration
| Parameter | Details |
|---|---|
| **Fine-tuning Method** | Supervised Fine-Tuning (SFT) |
| **Dataset Composition** | Curated prompt–SVG pairs |
| **Dataset Size** | ~1,500 samples |
| **Training Objective** | Structured output generation for SVG formats |
> **Note:** The relatively small dataset size may result in instability and limited generalization across diverse prompts. Improved dataset coverage is planned for future versions.
---
## Usage Recommendations
To get the best results from this model:
1. **Keep prompts simple and specific** — avoid multi-scene or highly complex compositions
2. **Validate all SVG outputs** before rendering or integrating into any pipeline
3. **Post-process outputs** to correct syntax or structural issues
4. **Use iterative prompting** — refining prompts across multiple turns often yields better results
5. **Expect imperfections** — this is a beta model; treat outputs as drafts, not finals
6. **Run on Apple Silicon** — this MLX build is optimized for M1/M2/M3/M4 series chips
---
## Original Model
| Version | Link |
|---|---|
| **Original (full precision)** | [ArkAiLab-Adl/nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1) |
| **GGUF Quantized** | [Open4bits/nexora-vector-v0.1-GGUF](https://huggingface.co/Open4bits/nexora-vector-v0.1-GGUF) |
| **MLX 4-Bit (this model)** | [Open4bits/nexora-vector-v0.1-mlx-4Bit](https://huggingface.co/Open4bits/nexora-vector-v0.1-mlx-4Bit) |
---
## Evaluation
Nexora-Vector-v0.1 has not yet undergone formal benchmark evaluation. Current assessment is qualitative, based on manual testing of SVG generation tasks.
Planned evaluation metrics for future releases include:
| Metric | Description |
|---|---|
| **SVG Validity Rate** | Percentage of outputs that are parseable, valid SVG |
| **Structural Correctness** | Adherence to SVG schema and element hierarchy |
| **Prompt Adherence** | Alignment between user intent and generated output |
| **Visual Consistency** | Stability of outputs across similar prompts |
---
## 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
- Minor quality reduction inherent to 4-bit quantization
**Recommendation:** Implement downstream validation layers and SVG syntax checking before any rendering or integration.
---
## Future Work
The following improvements are planned for upcoming versions of the Nexora Vector series:
- [ ] Expanded and more diverse training dataset
- [ ] Improved SVG syntax correctness and validity rates
- [ ] Reduced hallucination rates
- [ ] Enhanced natural language understanding for complex prompts
- [ ] Support for richer vector compositions and multi-element scenes
- [ ] Formal benchmark evaluation suite
- [ ] Updated MLX quantized releases aligned with future model versions
---
## Community & Support
Join the community for updates and discussion:
💬 **[Join our Discord Server](https://discord.gg/mwdrgYbzuG)**
---
## License
This model is released under the **Apache License 2.0**.
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).
---
## Acknowledgements
This is an official ArkAiLabs release, published under the **[Open4bits](https://huggingface.co/Open4bits)** project — ArkAiLabs' dedicated initiative for quantized model releases. The MLX 4-bit weights are derived from **[Nexora-Vector-v0.1](https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1)**, which is itself built upon **[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B)** by the Qwen team. We thank the MLX team at Apple and the open-source AI community for their continued contributions that make projects like this possible.
---
## About Nexora & Open4bits
**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.
**Open4bits** is ArkAiLabs' official project for quantized model releases, providing optimized variants of Nexora models for efficient local inference across different hardware platforms.