cogito-v1-preview-qwen-32B
| Property | Value |
|---|---|
| Parameter Count | 32 Billion |
| Context Length | 128,000 tokens |
| Language Support | 30+ languages |
| License | Apache 2.0 |
| Model URL | https://huggingface.co/deepcogito/cogito-v1-preview-qwen-32B |
What is cogito-v1-preview-qwen-32B?
Cogito v1 preview is an advanced language model that introduces a unique hybrid reasoning approach to AI. Built on the Qwen architecture, this 32B parameter model can operate in both standard LLM mode and self-reflection mode, leveraging Iterated Distillation and Amplification (IDA) for enhanced performance. The model stands out for its ability to switch between direct responses and deep thinking modes, making it particularly effective for complex tasks.
Implementation Details
The model implements a sophisticated architecture that supports both standard inference and extended thinking capabilities. It can be deployed using the Hugging Face Transformers library, with options to enable deep thinking through either system prompts or tokenizer settings. The model supports bfloat16 precision and includes built-in tool calling capabilities for enhanced functionality.
- Trained using Iterated Distillation and Amplification (IDA)
- Supports 128k context length for processing long sequences
- Implements both standard and reasoning modes
- Features comprehensive tool calling support (single, parallel, multiple)
Core Capabilities
- Multilingual support across 30+ languages
- Advanced coding and STEM task handling
- Self-reflection and reasoning capabilities
- Flexible tool calling implementation
- Superior performance on industry benchmarks compared to size-equivalent models
Frequently Asked Questions
Q: What makes this model unique?
The model's hybrid reasoning capability sets it apart, allowing it to switch between direct responses and deep thinking modes. This is complemented by its extensive multilingual support and advanced tool calling features, making it highly versatile for various applications.
Q: What are the recommended use cases?
The model excels in coding tasks, STEM applications, and scenarios requiring complex reasoning. Its extensive context length and multilingual capabilities make it suitable for document analysis, technical writing, and cross-lingual applications. The tool calling feature enables integration with external systems for enhanced functionality.
