Instructions to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Transformers:

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

pipe = pipeline("text-generation", model="Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Use Docker

docker model run hf.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

SGLang

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 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 "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2" \
    --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": "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2",
		"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 "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2" \
        --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": "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Ollama:
```
ollama run hf.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
```
Unsloth Desktop

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Docker Model Runner:
```
docker model run hf.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M
```

Lemonade

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Run and chat with the model

lemonade run user.QuantaSparkLabs-Quantum-X-GGUF-i2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

GGUF Files for Quantum-X

These are the GGUF files for QuantaSparkLabs/Quantum-X.

Note: This is the second iteration/revision of this model. A revision is made when a model repo gets updated with a new model. This is the latest version of the model.

[first iteration (1)]

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

Model Card for Quantum-X

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: 424

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2

Base model

QuantaSparkLabs/Quantum-X

Quantized

(4)

this model