Cogito v1 Preview LLaMA 8B
| Property | Value |
|---|---|
| Parameter Count | 8 Billion |
| Architecture | LLaMA-based |
| Context Length | 128,000 tokens |
| License | Llama 3.1 Community License |
| Model URL | https://huggingface.co/deepcogito/cogito-v1-preview-llama-8B |
What is cogito-v1-preview-llama-8B?
Cogito v1 preview is an advanced 8B parameter language model that introduces a unique hybrid reasoning approach to natural language processing. Built on the LLaMA architecture, it's specifically designed to operate in both standard LLM mode and an extended thinking mode, leveraging Iterated Distillation and Amplification (IDA) for enhanced performance.
Implementation Details
The model is implemented using a sophisticated training approach that enables both direct responses and self-reflection capabilities. It supports an impressive 128k context length and has been optimized across multiple domains including coding, STEM, and general instruction following. The implementation includes specialized tool calling capabilities and can be easily integrated using the Hugging Face Transformers library.
- Trained on 30+ languages for multilingual support
- Implements both standard and reasoning modes via simple configuration
- Supports various tool calling patterns (single, parallel, multiple)
- Uses bfloat16 precision for efficient inference
Core Capabilities
- Extended thinking mode activated through system prompts or tokenizer settings
- Advanced tool calling functionality with support for complex operations
- High-performance STEM and coding capabilities
- Significant multilingual understanding and generation
- Outperforms size-equivalent models on common industry benchmarks
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 achieved through IDA training, making it particularly effective for complex tasks requiring careful reasoning.
Q: What are the recommended use cases?
The model excels in coding tasks, STEM applications, and scenarios requiring tool integration. It's particularly well-suited for applications requiring multilingual support or complex reasoning chains, making it valuable for educational, technical, and development environments.
