Flexan/QuantaSparkLabs-Quantum-X-GGUF-i2

GGUF Files for Quantum-X

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

[!NOTE] 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)]

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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

<p align="center"> <img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/preview imgagee.png" alt="NYXIS Logo" width="160" height="160" style="border-radius: 50%; object-fit: cover;"> </p>

<p align="center"> <img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/logoname.png" alt="NYXIS Name" width="700" style="border-radius: 18px;"> </p>

<p align="center"> <a href="https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct"><img src="https://img.shields.io/badge/Base-Qwen%202.5%200.5B-blueviolet" alt="Base Model"></a> <a href="https://huggingface.co/datasets/teknium/OpenHermes-2.5"><img src="https://img.shields.io/badge/Data-OpenHermes%202.5-00BFFF" alt="Training Data"></a> <a href="#"><img src="https://img.shields.io/badge/Fine--Tune-QLoRA%20%2B%20Unsloth-FF6F00" alt="Fine-Tune Method"></a> <a href="#"><img src="https://img.shields.io/badge/Size-0.5B%20Params-lightgrey" alt="Model Size"></a> <a href="#"><img src="https://img.shields.io/badge/Speed-Blazing%20Fast-brightgreen" alt="Speed"></a> <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-yellow" alt="License"></a> </p>

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.

✨ 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

⚠️ 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

<center>Built with ❤️ by QuantaSparkLabs<br>Model ID: Quantum‑X • Rebuilt 2026</center>