Primal-Opus-14B-Optimus-v2
| Property | Value |
|---|---|
| Parameter Count | 14 Billion |
| Base Architecture | Qwen 2.5 14B |
| Context Window | 128K tokens |
| Model URL | HuggingFace |
| Supported Languages | 29+ including Chinese, English, French, Spanish, Portuguese, German |
What is Primal-Opus-14B-Optimus-v2?
Primal-Opus-14B-Optimus-v2 is an advanced language model built on the Qwen 2.5 14B architecture, specifically engineered to excel in reasoning tasks. The model has been fine-tuned on a synthetic dataset derived from DeepSeek R1, focusing on enhancing chain-of-thought reasoning and logical problem-solving capabilities. With support for a 128K token context window and the ability to generate up to 8K tokens per output, it represents a significant advancement in large-scale language modeling.
Implementation Details
The model leverages the Transformers library for easy deployment and integration. It features optimized instruction-following capabilities and can handle structured data formats like JSON. The implementation supports both CPU and GPU acceleration, with automatic device mapping for optimal performance.
- Supports extensive context windows up to 128K tokens
- Generates up to 8K tokens per response
- Implements advanced reasoning and logical deduction capabilities
- Features multilingual support across 29+ languages
- Optimized for both reasoning tasks and creative generation
Core Capabilities
- Enhanced reasoning and multi-step logical deduction
- Mathematical and scientific problem-solving
- Code generation and debugging across multiple languages
- Structured data processing and analysis
- Long-form content generation and document writing
- Multilingual text processing and generation
Frequently Asked Questions
Q: What makes this model unique?
The model's primary strength lies in its enhanced reasoning capabilities and extensive context window of 128K tokens. It combines the robust architecture of Qwen 2.5 with specialized fine-tuning for chain-of-thought reasoning, making it particularly effective for complex problem-solving tasks.
Q: What are the recommended use cases?
The model excels in advanced logical reasoning, mathematical problem-solving, code generation, and structured data analysis. It's particularly suitable for applications requiring deep reasoning, long-context comprehension, and multilingual capabilities. However, users should note its high computational requirements and potential variations in performance across different languages.
