Quantum-X

A compact, high-speed general-purpose language model designed for efficient inference and versatile AI assistance.

📋 Overview

Quantum-X is a lightweight, 0.1B parameter language model developed by QuantaSparkLabs. Engineered for speed and responsiveness, this model provides a capable foundation for general conversational AI, text generation, and task assistance while maintaining an extremely small computational footprint ideal for edge deployment and experimentation.

The model is fine-tuned using Supervised Fine-Tuning (SFT) to follow instructions and engage in helpful dialogue, making it suitable for applications where low latency and minimal resource consumption are priorities.

✨ Core Features

🎯 General-Purpose AI	⚡ Speed & Efficiency
Conversational AI: Engaging in open-ended dialogue and Q&A.	Minimal Footprint: ~0.1B parameters for near-instant inference.
Text Generation & Drafting: Writing assistance, summarization, and idea generation.	Optimized for Speed: Primary design goal for rapid response times.
Task Assistance: Following instructions for a variety of simple tasks.	Edge & CPU Friendly: Can run efficiently on standard hardware.

📊 Performance & Characteristics

🧠 Model Personality & Output

As a very small model (0.1B parameters), Quantum-X is best suited for less complex tasks. It excels in speed and can handle straightforward generation and Q&A effectively. Users should expect occasional inconsistencies or minor errors in reasoning or factual recall, which is a typical trade-off for models of this scale prioritizing efficiency.

🔬 Evaluation Status

Formal benchmark scores are not yet available. Performance is best evaluated through direct testing on target tasks.

• Strength: Very fast inference, low resource usage.
• Consideration: Limited capacity for complex reasoning or highly precise factual generation compared to larger models.

🏗️ Model Architecture

High-Level Design

Quantum-X is built on a transformer-based architecture, optimized from the ground up for rapid processing.

Training Pipeline

Base Model → Supervised Fine-Tuning (SFT) → Quantum-X
       ↓                    ↓
 [Foundation LLM]   [Instruction & Conversational Data]

🔧 Technical Specifications

Parameter	Value / Detail
Model Type	Transformer-based Language Model
Total Parameters	~0.1 Billion
Fine-tuning Method	Supervised Fine-Tuning (SFT)
Tensor Precision	FP32
Context Window	May vary to 1k-5k tokens

💻 Quick Start

Installation

pip install transformers torch accelerate

Basic Usage (Text Generation)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "QuantaSparkLabs/Quantum-X"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float32, # or torch.float16 if supported
    device_map="auto"
)

prompt = "Explain what makes quantum computing special in one sentence."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

🚀 Deployment Options

Hardware Requirements

Environment	RAM	Storage	Ideal For
Standard CPU	2-4 GB	~400 MB	Testing, lightweight applications
Entry-Level GPU	1-2 GB VRAM	~400 MB	Development & small-scale serving
Edge Device	>1 GB	~400 MB	Embedded applications, mobile (via conversion)

Note: The small size of Quantum-X makes it highly flexible for deployment in constrained environments.

⚠️ Intended Use & Limitations

Appropriate Use Cases

• Educational Tools & Tutoring: Simple Q&A and concept explanation.
• Content Drafting & Brainstorming: Generating ideas, short emails, or social media posts.
• Prototyping & Experimentation: Testing AI features without heavy infrastructure.
• Low-Latency Chat Interfaces: Where response speed is critical over depth.

Out-of-Scope & Limitations

• High-Stakes Decisions: Not for medical, legal, financial, or safety-critical advice.
• Complex Reasoning: Tasks requiring multi-step logic, advanced math, or deep analysis.
• Perfect Factual Accuracy: May generate incorrect or outdated information; always verify critical facts.
• Specialized Tasks: Not fine-tuned for code generation, highly technical writing, or niche domains unless specifically trained.

Bias & Safety

As a general AI model trained on broad data, it may reflect societal biases. A safety layer is recommended for production use.

📄 License & Citation

License: Apache 2.0

Citation:

@misc{quantumx2024,
  title={Quantum-X: A Compact High-Speed General-Purpose Language Model},
  author={QuantaSparkLabs},
  year={2024},
  url={https://huggingface.co/QuantaSparkLabs/Quantum-X}
}

🤝 Contributing & Support

For questions, feedback, or to report issues, please use the Discussion tab on this model's Hugging Face repository.

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Built with ❤️ by QuantaSparkLabs
Model ID: Quantum-X • Release: 2026

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