Orion-14B-Base
| Property | Value |
|---|---|
| Parameters | 14 Billion |
| Training Data | 2.5T tokens (multilingual) |
| Context Length | Up to 320k tokens |
| License | Apache License 2.0 (code), Custom Community License (model) |
What is Orion-14B-Base?
Orion-14B-Base is a state-of-the-art multilingual language model developed by OrionStarAI. Trained on 2.5T tokens across multiple languages, it demonstrates exceptional performance in English, Chinese, Japanese, and Korean. The model outperforms many competitors in comprehensive evaluations, particularly excelling in examination and professional knowledge benchmarks.
Implementation Details
The model utilizes a transformer-based architecture and is available in multiple variants including the base model, chat model, long-context version, and quantized versions. It supports efficient inference through various methods including vLLM and llama.cpp implementations.
- Supports context lengths up to 320k tokens in the LongChat variant
- Achieves superior performance in multilingual benchmarks
- Offers quantized versions (Int4) reducing model size by 70% while maintaining performance
Core Capabilities
- Multilingual Understanding: Exceptional performance across English, Chinese, Japanese, and Korean
- Strong Reasoning: Top scores in reasoning and professional knowledge benchmarks
- Long Context Processing: Supports extremely long text processing up to 320k tokens
- Efficient Deployment: Available in quantized versions for optimized inference
Frequently Asked Questions
Q: What makes this model unique?
Orion-14B-Base stands out for its exceptional multilingual capabilities and strong performance across various benchmarks, particularly in Asian languages. It achieves state-of-the-art results in many evaluation metrics while maintaining efficiency through various deployment options.
Q: What are the recommended use cases?
The model is well-suited for multilingual applications, professional knowledge tasks, and long-context processing. Its various variants (Chat, RAG, Plugin) make it adaptable for specific use cases like conversational AI, document processing, and function calling.
