Cogito v1 Preview LLaMA 3B
| Property | Value |
|---|---|
| Parameter Count | 3 Billion |
| Context Length | 128,000 tokens |
| Languages | 30+ |
| License | Llama 3.2 Community License Agreement |
| Author | DeepCogito |
| Model URL | https://huggingface.co/deepcogito/cogito-v1-preview-llama-3B |
What is cogito-v1-preview-llama-3B?
Cogito v1 Preview is an innovative instruction-tuned generative model that introduces a unique hybrid reasoning approach. Built on the LLaMA architecture, this model can operate in both standard LLM mode and self-reflection mode, leveraging Iterated Distillation and Amplification (IDA) for enhanced performance. The model represents a significant advancement in combining direct response capabilities with deep reasoning abilities.
Implementation Details
The model implements a sophisticated architecture that allows for dual-mode operation. It's trained using IDA, an efficient alignment strategy for superintelligence that employs iterative self-improvement. The implementation supports both standard inference and extended thinking modes, with specific optimizations for tool calling and multi-language processing.
- Supports 128k context length for extensive processing
- Implements both standard and reasoning modes via simple switching mechanisms
- Features built-in tool calling capabilities (single, parallel, multiple)
- Optimized for STEM, coding, and instruction following
Core Capabilities
- Hybrid reasoning with switchable thinking modes
- Superior multilingual support across 30+ languages
- Advanced tool calling and integration capabilities
- Optimized performance in coding and STEM tasks
- Extensive context handling (128k tokens)
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its hybrid reasoning capability, allowing it to switch between standard LLM responses and deep thinking modes. This is achieved through IDA training and can be activated either through specific system prompts or tokenizer settings.
Q: What are the recommended use cases?
The model excels in various applications including coding tasks, STEM problem-solving, multilingual processing, and tool-integrated workflows. It's particularly effective for scenarios requiring both quick responses and deeper analytical thinking.
