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	Qwen/Qwen2.5-0.5B-Instruct

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• Factual precision: Can occasionally produce outdated or incorrect information.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