HelpingAI/Dhanishtha-2.0-preview-0825
Dhanishtha-2.0-preview-0825 is a 14 billion parameter causal language model developed by HelpingAI, built upon the Qwen3-14B foundation. It is the world's first model to feature Intermediate Thinking capabilities, allowing it to pause, reflect, and self-correct its reasoning multiple times within a single response. With a 32K token context length and support for over 39 languages, this model excels at complex problem-solving, educational assistance, and research support requiring transparent, iterative reasoning.
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Dhanishtha-2.0: Intermediate Thinking AI Model
Dhanishtha-2.0, developed by HelpingAI, is a 14 billion parameter causal language model based on Qwen3-14B, notable for being the first model with Intermediate Thinking capabilities. This allows the model to perform multi-phase reasoning, including self-correction and iterative refinement, within a single response, indicated by <think>...</think> blocks. It also incorporates Structured Emotional Reasoning (SER) using <ser>...</ser> blocks for empathetic responses.
Key Capabilities
- Intermediate Thinking: Pauses to reflect and restart reasoning processes, enabling self-correction.
- Multilingual Support: Inherits 39+ language capabilities from its base model, maintaining reasoning consistency across languages.
- Complex Problem-Solving: Excels at riddles, multi-step reasoning, and scenarios requiring backtracking.
- Structured Emotional Reasoning (SER): Integrates emotional context into responses.
Good For
- Complex Problem Solving: Ideal for multi-step mathematical problems, logical puzzles, and riddles.
- Educational Assistance: Provides detailed explanations with visible reasoning processes.
- Research Support: Useful for analysis requiring multiple perspectives and self-correction.
- Creative Writing: Supports iterative story development with reasoning about plot choices.
While offering advanced reasoning, users should note potential verbosity and increased processing time due to the multiple thinking phases. It is currently in a prototype/preview status and may reflect biases from its base model and training data.