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Model: Open4bits/llama-nexora-vector-v0.1-GGUF
Source: Original Platform

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README.md

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+---
+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
+---
+<p align="center">
+  <img src="https://huggingface.co/ArkAiLab-Adl/llama-nexora-vector-v0.1/resolve/main/assets/llama-nexora-vector.jpg" alt="llama-nexora-vector-gguf"/>
+</p>
+
+# Llama-Nexora-Vector-v0.1 — GGUF
+
+<p align="center">
+  <img src="https://img.shields.io/badge/status-beta-orange" alt="Status: Beta"/>
+  <img src="https://img.shields.io/badge/license-Llama%203.2%20Community-blue" alt="License: Llama 3.2 Community"/>
+  <img src="https://img.shields.io/badge/base_model-Llama--3.2--1B-blueviolet" alt="Base Model: Llama 3.2 1B"/>
+  <img src="https://img.shields.io/badge/output-SVG-green" alt="Output: SVG"/>
+  <img src="https://img.shields.io/badge/family-Llama--Nexora-red" alt="Family: Llama-Nexora"/>
+  <img src="https://img.shields.io/badge/format-GGUF-cyan" alt="Format: GGUF"/>
+</p>
+
+> 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.

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