library_name, license, language, tags, pipeline_tag, base_model

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

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)]

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

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

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