deepcogito/cogito-v1-preview-qwen-14B
The deepcogito/cogito-v1-preview-qwen-14B is a 14.8 billion parameter instruction-tuned generative language model developed by Deep Cogito. Utilizing Iterated Distillation and Amplification (IDA) for alignment, this model is optimized for hybrid reasoning, coding, STEM, and multilingual instruction following. It supports a substantial 131,072 token context length and demonstrates strong performance against size-equivalent counterparts in both standard and reasoning modes. This model is particularly suited for applications requiring advanced problem-solving and tool-calling capabilities across over 30 languages.
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Cogito v1 Preview - 14B: Hybrid Reasoning LLM
The deepcogito/cogito-v1-preview-qwen-14B is a 14.8 billion parameter instruction-tuned generative language model from Deep Cogito, designed for text-in/text-out applications. It stands out as a hybrid reasoning model, capable of both direct answering and self-reflection, which can be activated via a specific system prompt or a tokenizer setting (enable_thinking=True). This allows it to perform like advanced reasoning models.
Key Capabilities & Features
- Hybrid Reasoning: Operates in both standard and an enhanced 'deep thinking' mode for complex problem-solving.
- Advanced Alignment: Trained using Iterated Distillation and Amplification (IDA), an efficient strategy for iterative self-improvement.
- Optimized Performance: Excels in coding, STEM, instruction following, and general helpfulness, outperforming size-equivalent models in benchmarks.
- Multilingual Support: Trained in over 30 languages, enhancing its global applicability.
- Extended Context Window: Features a large context length of 131,072 tokens.
- Tool Calling: Supports single, parallel, and multiple tool calls in both standard and deep thinking modes, enabling integration with external functions.
When to Use This Model
- For tasks requiring robust reasoning capabilities beyond standard LLM responses.
- Applications demanding high performance in coding and STEM-related queries.
- Use cases needing strong multilingual instruction following.
- Scenarios where tool integration and complex function calling are essential.
- When a model with a large context window is beneficial for processing extensive inputs.