QuantaSparkLabs-Quantum-X-GGUF-i1

GGUF Files for Quantum-X

These are the GGUF files for QuantaSparkLabs/Quantum-X.

[!NOTE]
Note: This is the first iteration/revision of this model. A revision is made when a model repo gets updated with a new model.

[second iteration (2)]

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Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, 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 the model, please refer to the original model repo.

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

📊 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

💻 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

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

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