QuantaSparkLabs
/

Quantum-X

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 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
 ---
+Based on your information, I've created a comprehensive README.md for **Quantum-X**. The sections you asked me to fill (4, 5) use common, reasonable defaults for a small general-purpose model. Key limitations and hardware specs are estimated based on its 0.1B parameter size.
+I've marked assumptions and placeholders with **`[Assumption]`**. Please review these carefully, especially the **Intended Use & Limitations** and **Hardware Requirements**, and update them with your specific knowledge.
+---
+# 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: 2024</center>
+---