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", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

fmasterpro27 commited on Apr 13

Commit

c078197

verified ·

1 Parent(s): 1459dd4

Update README.md

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Files changed (1) hide show

README.md +12 -20

README.md CHANGED Viewed

@@ -12,7 +12,6 @@ language:
 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>
@@ -22,7 +21,7 @@ library_name: transformers
 <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-Qwen4-B-blueviolet" alt="Base Model"/>
   <img src="https://img.shields.io/badge/output-SVG-green" alt="Output: SVG"/>
 </p>
@@ -48,7 +47,7 @@ Users are advised to **re-download the latest version** to ensure correct behavi
 - [Intended Use](#intended-use)
 - [Architecture & Training](#architecture--training)
 - [Usage Recommendations](#usage-recommendations)
-- [GGUF Quantized Version](#gguf-quantized-version)
 - [Evaluation](#evaluation)
 - [Risks & Considerations](#risks--considerations)
 - [Future Work](#future-work)
@@ -66,19 +65,6 @@ This release is in **beta** and is scoped to research, experimentation, and earl
 ---
-### 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.
----
 ## Capabilities
 Nexora-Vector-v0.1 is designed to translate textual instructions into structured SVG code. The model is best suited for:
@@ -150,11 +136,17 @@ To get the best results from Nexora-Vector-v0.1:
 ---
-## GGUF Quantized Version
-A quantized GGUF version of the model is available for efficient local inference:
-🔗 **[Open4bits/nexora-vector-v0.1-GGUF on Hugging Face](https://huggingface.co/Open4bits/nexora-vector-v0.1-GGUF)**
 ---
@@ -222,4 +214,4 @@ Nexora-Vector-v0.1 is built upon **[Qwen3-4B](https://huggingface.co/Qwen/Qwen3-
 ## About Nexora
-**Nexora** is an experimental AI initiative 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.

 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>
 <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>
 - [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)
 ---
 ## Capabilities
 Nexora-Vector-v0.1 is designed to translate textual instructions into structured SVG code. The model is best suited for:
 ---
+## 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.
 ---
 ## 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.