---
library_name: transformers
license: apache-2.0
language:
- en
tags:
- qwen2.5
- 0.5B
- conversational
- fast
- lightweight
- quantsaparklabs
pipeline_tag: text-generation
base_model: QuantaSparkLabs/Quantum-X
---

# GGUF Files for Quantum-X

These are the GGUF files for [QuantaSparkLabs/Quantum-X](https://huggingface.co/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)](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF-i1)]

## Downloads

| GGUF Link | Quantization | Description |
| ---- | ----- | ----------- |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q2_K.gguf) | Q2_K | Lowest quality |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_S.gguf) | Q3_K_S |  |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ3_S.gguf) | IQ3_S | Integer quant, preferable over Q3_K_S |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ3_M.gguf) | IQ3_M | Integer quant |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_M.gguf) | Q3_K_M |  |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q3_K_L.gguf) | Q3_K_L |  |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.IQ4_XS.gguf) | IQ4_XS | Integer quant |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q4_K_S.gguf) | Q4_K_S | Fast with good performance |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q4_K_M.gguf) | Q4_K_M | **Recommended:** Perfect mix of speed and performance |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q5_K_S.gguf) | Q5_K_S |  |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q5_K_M.gguf) | Q5_K_M |  |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q6_K.gguf) | Q6_K | Very good quality |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.Q8_0.gguf) | Q8_0 | Best quality |
| [Download](https://huggingface.co/Flexan/QuantaSparkLabs-Quantum-X-GGUF/resolve/main/Quantum-X.f16.gguf) | 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](https://huggingface.co/QuantaSparkLabs/Quantum-X).

You can find more info about me and what I do [here](https://huggingface.co/Flexan/Flexan).

# 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.

| 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

```python
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

---

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