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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)
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
34model_id ="QuantaSparkLabs/Quantum-X"5tokenizer = AutoTokenizer.from_pretrained(model_id)6model = AutoModelForCausalLM.from_pretrained(7 model_id,8 torch_dtype=torch.float32,# or torch.float16 if supported9 device_map="auto"10)1112prompt ="Explain what makes quantum computing special in one sentence."13inputs = tokenizer(prompt, return_tensors="pt").to(model.device)1415outputs = model.generate(16**inputs,17 max_new_tokens=150,18 temperature=0.7,19 do_sample=True,20 pad_token_id=tokenizer.eos_token_id
21)2223print(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:
bibtex
1@misc{quantumx2024,
2 title={Quantum-X: A Compact High-Speed General-Purpose Language Model},
3 author={QuantaSparkLabs},
4 year={2024},
5 url={https://huggingface.co/QuantaSparkLabs/Quantum-X}
6}
🤝 Contributing & Support
For questions, feedback, or to report issues, please use the Discussion tab on this model's Hugging Face repository.
Built with ❤️ by QuantaSparkLabs Model ID: Quantum-X • Release: 2026
