Trillion-7B-preview-GGUF
| Property | Value |
|---|---|
| Parameter Count | 7.76B |
| Context Length | 4,096 tokens |
| Architecture | Transformer Decoder with RoPE, SwiGLU, RMSNorm |
| Training Tokens | 2T |
| License | Apache-2.0 |
What is Trillion-7B-preview-GGUF?
Trillion-7B-preview is an advanced multilingual language model that pushes the boundaries of efficiency and performance. With 7.76B parameters, it achieves competitive results while using significantly less compute (~9.3×10²² FLOPs) compared to similar models. The model particularly excels in Korean language tasks while maintaining strong capabilities in English, Japanese, and Chinese.
Implementation Details
The model features a sophisticated architecture with 32 layers and 32 attention heads, utilizing modern techniques like RoPE, SwiGLU, and RMSNorm. It has been trained on 2T tokens and employs a vocabulary size of 128,128 tokens, enabling robust multilingual capabilities.
- Advanced transformer architecture with 32-layer design
- 4,096 token context window for handling longer sequences
- Optimized for multi-language processing with special focus on Asian languages
- Efficient compute utilization while maintaining competitive performance
Core Capabilities
- Strong performance in Korean language tasks (80.02% on HAERAE)
- Competitive mathematical reasoning (72.25% on GSM8k)
- Robust instruction following capabilities (79.13% on IFEval)
- Effective multilingual understanding across English, Korean, Japanese, and Chinese
- Balanced performance in coding tasks (55.48% on HumanEval)
Frequently Asked Questions
Q: What makes this model unique?
The model uniquely balances efficiency and performance, achieving 66.5% average performance while using significantly less compute than competitors. It particularly excels in Korean language tasks while maintaining strong capabilities across multiple languages.
Q: What are the recommended use cases?
The model is well-suited for multilingual applications, particularly those involving Asian languages. It shows strong capabilities in instruction following, mathematical reasoning, and coding tasks, making it versatile for various applications while maintaining reasonable compute requirements.
