cogito-v1-preview-qwen-14B
| Property | Value |
|---|---|
| Model Size | 14B parameters |
| Context Length | 128,000 tokens |
| Languages | 30+ languages |
| License | Apache 2.0 |
| Model URL | https://huggingface.co/deepcogito/cogito-v1-preview-qwen-14B |
What is cogito-v1-preview-qwen-14B?
Cogito v1 preview is an advanced hybrid reasoning language model that introduces a unique dual-mode operation: standard LLM responses and self-reflective reasoning. Built on the Qwen architecture, this 14B parameter model employs Iterated Distillation and Amplification (IDA) for enhanced alignment and performance. The model stands out for its extensive multilingual capabilities, superior coding abilities, and innovative tool-calling features.
Implementation Details
The model implements a sophisticated architecture that allows for both direct and reasoning-based responses. It can be integrated using the Hugging Face Transformers library and supports two distinct methods for enabling its deep thinking capabilities: through specific system prompts or via tokenizer settings. The model particularly excels in handling complex tasks requiring analytical thinking and structured output.
- Supports both standard and reasoning-based response modes
- Implements tool calling with single, parallel, and multiple execution options
- Features 128k context window for handling lengthy inputs
- Trained using IDA methodology for enhanced performance
Core Capabilities
- Advanced reasoning and self-reflection capabilities
- Superior performance in STEM and coding tasks
- Comprehensive multilingual support across 30+ languages
- Flexible tool-calling functionality
- Extended context handling up to 128k tokens
Frequently Asked Questions
Q: What makes this model unique?
The model's hybrid architecture allowing both direct and reasoning-based responses, combined with its implementation of Iterated Distillation and Amplification, sets it apart from traditional LLMs. It offers superior performance in both modes compared to size-equivalent models, particularly in STEM and coding tasks.
Q: What are the recommended use cases?
The model is particularly well-suited for complex programming tasks, STEM-related problem-solving, multilingual applications, and scenarios requiring deep analytical thinking or tool integration. Its extensive context window makes it ideal for processing and analyzing lengthy documents or conversations.
