---
library_name: transformers
license: apache-2.0
language:
- en
---
# 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
```bash
pip install transformers torch accelerate
```

### Basic Usage (Text Generation)
```python
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:**
```bibtex
@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.

---

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

---