Instructions to use QuantaSparkLabs/Quantum-X with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantaSparkLabs/Quantum-X with Transformers:

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

pipe = pipeline("text-generation", model="QuantaSparkLabs/Quantum-X")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("QuantaSparkLabs/Quantum-X")
model = AutoModelForCausalLM.from_pretrained("QuantaSparkLabs/Quantum-X")
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

vLLM

How to use QuantaSparkLabs/Quantum-X with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantaSparkLabs/Quantum-X

SGLang

How to use QuantaSparkLabs/Quantum-X 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 "QuantaSparkLabs/Quantum-X" \
    --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": "QuantaSparkLabs/Quantum-X",
		"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 "QuantaSparkLabs/Quantum-X" \
        --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": "QuantaSparkLabs/Quantum-X",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use QuantaSparkLabs/Quantum-X with Docker Model Runner:
```
docker model run hf.co/QuantaSparkLabs/Quantum-X
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

NYXIS Logo

NYXIS Name

Quantum-X

A compact, high‑speed conversational AI built on Qwen 2.5 0.5B — small enough for edge devices, smart enough for real conversation.

📋 Overview

Quantum‑X is a 0.5 billion parameter language model developed by QuantaSparkLabs. It's fine‑tuned from Qwen 2.5 0.5B on a mix of OpenHermes‑2.5 conversations and custom identity data, giving it warm, direct conversational abilities while keeping inference blazingly fast.

Feature	Detail
Base Model	Qwen 2.5 0.5B‑Instruct
Parameters	~0.5B
Fine‑tuning	QLoRA (Unsloth), 2 epochs
Training Data	OpenHermes‑2.5 + identity examples
Tensor Precision	FP16
Chat Template	✅ Native Qwen2 chat template

✨ What It Does Well

Conversational AI: Natural, warm dialogue with identity baked in.
Factual Q&A: Answers general knowledge questions correctly.
Fast Inference: 0.5B parameters = near‑instant responses on CPU or GPU.
Edge Friendly: Runs comfortably on 2 GB RAM, even on a phone.

💻 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "QuantaSparkLabs/Quantum-X"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "system", "content": "You are Quantum-X, created by QuantaSparkLabs."},
    {"role": "user", "content": "What is the capital of France?"}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer(inputs, return_tensors="pt").to(model.device)

outputs = model.generate(**input_ids, max_new_tokens=100, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🚀 Hardware Requirements

Environment	RAM	Storage	Ideal For
CPU	2 GB	~500 MB	Testing, embedded apps
GPU	1‑2 GB VRAM	~500 MB	Development, serving
Edge / Mobile	>1 GB	~500 MB	On‑device inference

⚠️ Limitations

Complex reasoning: Multi‑step logic or advanced math may be inconsistent.
Factual precision: Can occasionally produce outdated or incorrect information.
Not for high‑stakes use: Don't use for medical, legal, or safety‑critical decisions.

📄 License

Apache 2.0

Built with ❤️ by QuantaSparkLabs
Model ID: Quantum‑X • Rebuilt 2026

Downloads last month: 93

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for QuantaSparkLabs/Quantum-X

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(780)

this model

Quantizations

4 models