Instructions to use ArkAiLab-Adl/nexora-vector-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArkAiLab-Adl/nexora-vector-v0.1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ArkAiLab-Adl/nexora-vector-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArkAiLab-Adl/nexora-vector-v0.1")
model = AutoModelForCausalLM.from_pretrained("ArkAiLab-Adl/nexora-vector-v0.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ArkAiLab-Adl/nexora-vector-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArkAiLab-Adl/nexora-vector-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkAiLab-Adl/nexora-vector-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ArkAiLab-Adl/nexora-vector-v0.1

SGLang

How to use ArkAiLab-Adl/nexora-vector-v0.1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ArkAiLab-Adl/nexora-vector-v0.1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkAiLab-Adl/nexora-vector-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ArkAiLab-Adl/nexora-vector-v0.1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArkAiLab-Adl/nexora-vector-v0.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ArkAiLab-Adl/nexora-vector-v0.1 with Docker Model Runner:
```
docker model run hf.co/ArkAiLab-Adl/nexora-vector-v0.1
```

nexora-vector-v0.1 / README.md

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3-4B
	tags:
	- nexora
	- chat
	- qwen3
	- conversational
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	---
	<p align="center">
	<img src="https://huggingface.co/ArkAiLab-Adl/nexora-vector-v0.1/resolve/main/assets/nexora-vector.png" alt="Nexora-Vector"/>
	</p>

	# Nexora-Vector-v0.1

	<p align="center">
	<img src="https://img.shields.io/badge/status-beta-orange" alt="Status: Beta"/>
	<img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License: Apache 2.0"/>
	<img src="https://img.shields.io/badge/base_model-Qwen3--4B-blueviolet" alt="Base Model"/>
	<img src="https://img.shields.io/badge/output-SVG-green" alt="Output: SVG"/>
	</p>

	> 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.

	---

	## ⚠️ Update Notice

	An issue was identified in the initial release where an incorrect base model was uploaded.
	This has now been fully corrected, and the current version is properly based on Qwen3-4B.

	Users are advised to re-download the latest version to ensure correct behavior and performance.

	---

	## Table of Contents

	- [Overview](#overview)
	- [Model Details](#model-details)
	- [Capabilities](#capabilities)
	- [Limitations](#limitations)
	- [Intended Use](#intended-use)
	- [Architecture & Training](#architecture--training)
	- [Usage Recommendations](#usage-recommendations)
	- [Quantized Versions](#quantized-versions)
	- [Evaluation](#evaluation)
	- [Risks & Considerations](#risks--considerations)
	- [Future Work](#future-work)
	- [Community & Support](#community--support)
	- [License](#license)
	- [Acknowledgements](#acknowledgements)

	---

	## Overview

	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.

	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.

	---

	## Capabilities

	Nexora-Vector-v0.1 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 lightweight icons and minimal design assets
	- Supporting rapid prototyping in vector-based design workflows

	> 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 — the small dataset size 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

	---

	## 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

	### ❌ 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

	---

	## Architecture & Training

	The model is built on Qwen3-4B and fine-tuned using supervised learning to improve structured SVG output generation.

	### 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 Nexora-Vector-v0.1:

	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

	---

	## Quantized Versions

	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:

	\| Version \| Format \| Link \|
	\|---\|---\|---\|
	\| 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) \|
	\| MLX 4-Bit (Apple Silicon) \| MLX \| [Open4bits/nexora-vector-v0.1-mlx-4Bit](https://huggingface.co/Open4bits/nexora-vector-v0.1-mlx-4Bit) \|

	- Use the GGUF version for local inference on Windows, Linux, or macOS with tools like `llama.cpp`, Ollama, or LM Studio.
	- Use the MLX version for optimized inference on Apple Silicon (M1/M2/M3/M4) via the MLX framework.

	---

	## 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 Nexora-Vector-v0.1 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

	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

	---

	## 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

	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.

	---

	## About Nexora

	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.