Condor-Opus-14B-Exp
| Property | Value |
|---|---|
| Parameter Count | 14 Billion |
| Base Architecture | Qwen 2.5 14B |
| Context Length | 128K tokens |
| Output Length | 8K tokens |
| Model URL | Hugging Face |
What is Condor-Opus-14B-Exp?
Condor-Opus-14B-Exp represents a significant advancement in large language model development, built upon the Qwen 2.5 14B architecture. This model has been specifically optimized for enhanced reasoning capabilities and multi-step problem-solving, incorporating chain-of-thought reasoning methodologies in its training process. With support for 29+ languages and an impressive 128K token context window, it stands out in the field of general-purpose AI models.
Implementation Details
The model implements a sophisticated architecture optimized for both general knowledge and specialized reasoning tasks. It utilizes advanced fine-tuning techniques with chain-of-thought reasoning datasets, resulting in improved comprehension and structured response generation.
- Long-context processing capability up to 128K tokens
- Output generation capacity of 8K tokens
- Multilingual support across 29+ languages
- Enhanced instruction-following capabilities
- Optimized for structured data processing and long-form content generation
Core Capabilities
- Advanced reasoning and logical deduction
- Multilingual content generation and translation
- Structured data analysis and output formatting
- Educational and research assistance
- Conversational AI applications
- Long-form content creation with maintained coherence
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive features include its enhanced reasoning capabilities through chain-of-thought training, extensive 128K context window, and strong multilingual support across 29+ languages. Its performance metrics show particular strength in mathematical reasoning (52.27% on MATH Lvl 5) and instruction following (40.43% on IFEval).
Q: What are the recommended use cases?
The model excels in educational applications, research assistance, complex problem-solving, multilingual content generation, and structured data processing. It's particularly well-suited for applications requiring detailed reasoning and long-form content generation while maintaining coherence throughout extended outputs.
