Eridanus-Opus-14B-r999
| Property | Value |
|---|---|
| Parameter Count | 14 Billion |
| Architecture | Qwen 2.5 14B |
| Context Length | 128K tokens |
| Output Length | 8K tokens |
| Model URL | Hugging Face |
What is Eridanus-Opus-14B-r999?
Eridanus-Opus-14B-r999 is an advanced language model built upon the Qwen 2.5 14B architecture, specifically engineered to enhance reasoning capabilities and multi-step problem-solving. The model has been fine-tuned using chain-of-thought reasoning techniques and specialized datasets, making it particularly effective for complex logical deduction and contextual understanding tasks.
Implementation Details
The model leverages the Transformers library for easy deployment and supports both CPU and GPU implementations with automatic device mapping. It features an extensive context window of 128K tokens and can generate responses up to 8K tokens in length, making it suitable for processing and generating long-form content.
- Supports 29+ languages including English, Chinese, French, Spanish, and more
- Implements advanced instruction-following capabilities
- Utilizes automatic dtype selection for optimal performance
- Features comprehensive chat template integration
Core Capabilities
- Enhanced general knowledge across multiple domains
- Structured data processing and JSON generation
- Long-form content generation with maintained coherence
- Multi-step reasoning and problem-solving
- Multilingual support for global applications
- Educational and research assistance
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its enhanced reasoning capabilities combined with extensive multilingual support and long context window. It excels in structured responses and chain-of-thought reasoning, making it particularly valuable for complex problem-solving scenarios.
Q: What are the recommended use cases?
The model is ideal for educational assistance, research support, multilingual applications, and general-purpose reasoning tasks. It's particularly effective for scenarios requiring detailed explanations, structured data processing, and long-form content generation while maintaining coherence.
