---
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
- open4bits
---
# Llama-Nexora-Vector-v0.1 — GGUF
> This is the **official GGUF 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**. 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.
---
## Table of Contents
- [Overview](#overview)
- [The Llama-Nexora Family](#the-llama-nexora-family)
- [Available Quantizations](#available-quantizations)
- [Which Quant Should I Use?](#which-quant-should-i-use)
- [Model Details](#model-details)
- [Requirements](#requirements)
- [Installation & Usage](#installation--usage)
- [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-GGUF** contains the official GGUF quantized versions 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.
These quantized releases are published by **[Open4bits](https://huggingface.co/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.
| 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-GGUF** | GGUF (Windows / Linux / macOS) | *(this repo)* |
| **llama-nexora-vector-v0.1-mlx-4Bit** | MLX 4-Bit (Apple Silicon) | [Open4bits/llama-nexora-vector-v0.1-mlx-4Bit](https://huggingface.co/Open4bits/llama-nexora-vector-v0.1-mlx-4Bit) |
---
## Available Quantizations
All quantized files are available in this repository. Select the file that best matches your hardware and performance requirements.
### 2-bit
| Quantization | File Size | Description |
|---|---|---|
| **Q2_K** | 581 MB | Smallest size, lowest quality. Use only if very limited on RAM/VRAM. |
### 4-bit
| Quantization | File Size | Description |
|---|---|---|
| **Q4_K_S** | 776 MB | Small 4-bit quantization. Good balance of size and quality. |
| **Q4_0** | 771 MB | Legacy 4-bit format. Widely compatible. |
| **Q4_K_M** | 808 MB | Medium 4-bit quantization. Recommended for most users. |
### 5-bit
| Quantization | File Size | Description |
|---|---|---|
| **Q5_K_S** | 893 MB | Small 5-bit quantization. Better quality than Q4 with modest size increase. |
| **Q5_K_M** | 912 MB | Medium 5-bit quantization. Excellent quality-to-size ratio. |
### 6-bit
| Quantization | File Size | Description |
|---|---|---|
| **Q6_K** | 1.02 GB | High quality, close to full precision. Recommended if you have the RAM. |
### 8-bit
| Quantization | File Size | Description |
|---|---|---|
| **Q8_0** | 1.32 GB | Near full-precision quality. Best quality GGUF option available. |
---
## Which Quant Should I Use?
| Your Situation | Recommended Quant |
|---|---|
| Very limited RAM (< 2GB free) | Q2_K |
| General use / most users | Q4_K_M |
| Want better quality, have the space | Q5_K_M or Q6_K |
| Maximum quality, no size concern | Q8_0 |
| Legacy tooling / broad compatibility | Q4_0 |
> **Tip:** For most users, **Q4_K_M** offers the best balance between model size and output quality.
---
## Model Details
| Property | Details |
|---|---|
| **Model Name** | llama-nexora-vector-v0.1-GGUF |
| **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) |
| **Quantized By** | [Open4bits](https://huggingface.co/Open4bits) |
| **Output Format** | SVG |
| **Release Status** | Beta |
| **License** | Llama 3.2 Community License |
---
## Requirements
GGUF models can be run on **Windows, Linux, and macOS** (including Apple Silicon and Intel) using any of the following tools:
- **[llama.cpp](https://github.com/ggerganov/llama.cpp)** — CLI-based inference
- **[Ollama](https://ollama.com)** — Easy local model runner
- **[LM Studio](https://lmstudio.ai)** — GUI-based local inference app
- **[Jan](https://jan.ai)** — Open-source ChatGPT alternative for local use
---
## Installation & Usage
### llama.cpp
```bash
# Clone and build llama.cpp
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp && make
# Download the model (example: Q4_K_M)
huggingface-cli download Open4bits/llama-nexora-vector-v0.1-GGUF \
llama-nexora-vector-v0.1.Q4_K_M.gguf \
--local-dir ./models
# Run inference
./llama-cli -m ./models/llama-nexora-vector-v0.1.Q4_K_M.gguf \
-p "Generate an SVG of a simple red circle." \
-n 512
```
### Ollama
```bash
# Create a Modelfile
echo 'FROM ./llama-nexora-vector-v0.1.Q4_K_M.gguf' > Modelfile
# Create the model
ollama create llama-nexora-vector -f Modelfile
# Run it
ollama run llama-nexora-vector "Generate an SVG of a simple red circle."
```
### LM Studio
1. Open **LM Studio** and go to the Search tab.
2. Search for `Open4bits/llama-nexora-vector-v0.1-GGUF`.
3. Select your preferred quantization and download.
4. 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:
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
7. **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.
💬 **[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 [llama.cpp](https://github.com/ggerganov/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](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.