CrystalGPT-2-3B

CrystalGPT-2-3B is an open-source large language model developed by Crystal AI, a subsidiary of Syverra Studios. With approximately 3 billion parameters, it is designed to balance strong reasoning performance, efficient inference, and accessibility for researchers and developers.

CrystalGPT-2-3B is suitable for tasks such as text generation, summarization, question answering, code assistance, and conversational AI.

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✨ Key Features

• 3B parameter language model optimized for efficiency
• Open-source and free for research and commercial use (see License)
• Strong performance on general NLP and instruction-following tasks
• Designed for fine-tuning and downstream adaptation
• Compatible with popular ML frameworks (e.g. Hugging Face Transformers)

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📊 Model Overview

Attribute	Description
Model Name	CrystalGPT-2-3B
Developer	Crystal AI
Owner / Publisher	Syverra Studios
Parameters	~3 Billion
Architecture	Transformer-based decoder-only model
Training Data	A mixture of licensed data, data created by trainers, and publicly available text
Languages	Primarily English (multilingual capability may vary)
License	Open-source (see below)

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🚀 Intended Use

CrystalGPT-2-3B is intended for:

• Research in natural language processing
• Building chatbots and virtual assistants
• Content generation and summarization
• Educational and prototyping purposes
• Fine-tuning for domain-specific applications

Not Recommended For

• Safety-critical or medical decision-making without human oversight
• Fully autonomous systems requiring guaranteed correctness

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🛠️ Installation

You can load CrystalGPT-2-3B using common open-source tooling such as Hugging Face Transformers.

pip install transformers torch

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💻 Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "SyverraStudios/crystalgpt-2-3b"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Explain the basics of quantum computing in simple terms."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

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

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🔧 Fine-Tuning

CrystalGPT-2-3B supports parameter-efficient fine-tuning techniques such as:

• LoRA / QLoRA
• Prefix tuning
• Full fine-tuning (with sufficient compute)

This makes it suitable for customization on modest hardware compared to larger models.

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⚠️ Limitations

• May produce incorrect or hallucinated information
• Performance may degrade outside its primary training domains
• Biases present in training data may be reflected in outputs

Always validate outputs before using them in production.

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📜 License

CrystalGPT-2-3B is released under an open-source license.
Please refer to the LICENSE file in this repository for full terms and conditions.

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📖 Citation

If you use CrystalGPT-2-3B in your research, please cite:

@misc{crystalgpt2_3b,
  title={CrystalGPT-2-3B: An Open-Source 3B Parameter Language Model},
  author={{Crystal AI} and {Syverra Studios}},
  year={2026},
  url={https://github.com/SyverraStudios/crystalgpt-2-3b}
}

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🤝 Contributing

Contributions are welcome!
Please open an issue or submit a pull request for bug fixes, improvements, or documentation updates.

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🌐 About Crystal AI & Syverra Studios

Crystal AI focuses on building efficient, transparent, and adaptable AI models.
Syverra Studios is the parent organization, supporting open research and responsible deployment of advanced AI systems.

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CrystalGPT-2-3B — Clear intelligence, open to everyone.