---
base_model:
- ArkAiLab-Adl/llama-nexora-vector-v0.1
license: llama3.2
language:
- en
pipeline_tag: text-generation
tags:
- nexora
- llama-nexora
- vector
- chat
- llama-3
- mlx
- open4bits
---
# Llama-Nexora-Vector-v0.1 — MLX 4-Bit
> This is the **official MLX 4-bit quantized release** of [llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1), published by **[Open4bits](https://huggingface.co/Open4bits)** — the official quantization project under **ArkAiLabs**. This version is optimized for efficient inference on **Apple Silicon** (M1/M2/M3/M4) using the MLX framework. It is a beta release intended for research, prototyping, and early-stage development workflows only.
---
## Table of Contents
- [Overview](#overview)
- [The Llama-Nexora Family](#the-llama-nexora-family)
- [Quantization Details](#quantization-details)
- [Model Details](#model-details)
- [Requirements](#requirements)
- [Capabilities](#capabilities)
- [Limitations](#limitations)
- [Intended Use](#intended-use)
- [Usage Recommendations](#usage-recommendations)
- [Risks & Considerations](#risks--considerations)
- [Community & Support](#community--support)
- [License](#license)
- [Acknowledgements](#acknowledgements)
---
## Overview
**llama-nexora-vector-v0.1-mlx-4Bit** is the official MLX 4-bit quantized version of [llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/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.
This quantized release is published by **[Open4bits](https://huggingface.co/Open4bits)**, the dedicated quantization project under ArkAiLabs, and is designed specifically for optimized local inference on Apple Silicon hardware via the [MLX](https://github.com/ml-explore/mlx) framework. The total model size is **713MB**.
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.
| Model | Type | Link |
|---|---|---|
| **llama-nexora-vector-v0.1** | Original (Full Precision) | [ArkAiLab-Adl/llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1) |
| **llama-nexora-vector-v0.1-mlx-4Bit** | MLX 4-Bit (Apple Silicon) | *(this repo)* |
> For the GGUF quantized version compatible with llama.cpp, Ollama, and LM Studio, visit **[Open4bits](https://huggingface.co/Open4bits)**.
---
## Quantization Details
| Property | Details |
|---|---|
| **Quantization Format** | MLX 4-Bit |
| **Quantized By** | [Open4bits](https://huggingface.co/Open4bits) (official ArkAiLabs quantization project) |
| **Original Model** | [ArkAiLab-Adl/llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1) |
| **Model Size** | 713MB |
| **Target Platform** | Apple Silicon (M1/M2/M3/M4) |
| **Framework** | [MLX](https://github.com/ml-explore/mlx) |
---
## Model Details
| Property | Details |
|---|---|
| **Model Name** | llama-nexora-vector-v0.1-mlx-4Bit |
| **Model Family** | Llama-Nexora |
| **Model Type** | Text-to-SVG (Causal Language Model) |
| **Original Base Model** | [unsloth/Llama-3.2-1B-Instruct](https://huggingface.co/unsloth/Llama-3.2-1B-Instruct) |
| **Original Full Model** | [ArkAiLab-Adl/llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1) |
| **Output Format** | SVG |
| **Release Status** | Beta |
| **License** | Llama 3.2 Community License |
---
## Requirements
- **Hardware:** Apple Silicon Mac (M1, M2, M3, or M4)
- **OS:** macOS 13.3 or later
- **Framework:** [MLX](https://github.com/ml-explore/mlx) and [mlx-lm](https://github.com/ml-explore/mlx-examples/tree/main/llms)
---
## Capabilities
llama-nexora-vector-v0.1-mlx-4Bit 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** — 4-bit quantization may introduce minor degradation in output quality compared to the full-precision model
---
## Intended Use
### ✅ Supported Use Cases
- Academic and applied research in text-to-vector generation
- Experimental AI-assisted design systems on Apple Silicon
- Educational exploration of structured output generation
- Lightweight SVG prototyping and ideation on local Mac 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
- Non-Apple Silicon hardware (use the GGUF version instead)
---
## 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. **Human review is recommended** for all generated content
---
## 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
- Minor quality degradation versus the full-precision model due to 4-bit quantization
**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.
💬 **[Join our Discord Server](https://discord.gg/mwdrgYbzuG)**
---
## License
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](https://www.llama.com/llama3_2/license/). Please review the license terms before use, modification, or distribution.
---
## Acknowledgements
This quantized release is based on **[llama-nexora-vector-v0.1](https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1)** by ArkAiLabs, which itself is built upon **[Llama 3.2 1B Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct)** by Meta. Quantization was performed by **[Open4bits](https://huggingface.co/Open4bits)** using the [MLX](https://github.com/ml-explore/mlx) framework. We thank the open-source AI community for their continued contributions that make projects like this possible.
---
## About Open4bits
**[Open4bits](https://huggingface.co/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.