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---
library_name: transformers
license: apache-2.0
datasets:
- Abhaykoul/Dhanishtha-R1
- open-thoughts/OpenThoughts-114k
language:
- en
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
---
# Dhanishtha Overview
Dhanishtha is a cutting-edge reasoning AI model developed by **HelpingAI**, designed for deep introspection and structured logical analysis. Unlike traditional models that generate immediate responses, Dhanishtha employs a unique **deep-thinking process** process—an internal deliberation phase that enhances reasoning depth before presenting refined answers.
## Model Capabilities
Dhanishtha operates in **Dhanishtha Mode**, inspired by the **Dhanishtha Nakshatra**, known for wisdom, rhythm, and intellectual depth. The model engages in a multi-step thought process before providing responses, ensuring high accuracy and coherence.
### Key Features:
- **Structured Internal Reasoning:** Engages in self-dialogue within `<think></think>` tags, iterating through ideas and refining its thought process before responding.
- **Progressive Thought Refinement:** Evaluates multiple perspectives, making logical connections and ensuring a well-rounded answer.
- **Emotionally Intelligent Conversational Style:** Responses are expressive, engaging, and tailored for natural human interaction.
- **Optimized for Critical Thinking & Problem-Solving:** Excels in analytical reasoning, debate, and deep philosophical discussions.
- **Context Awareness:** Maintains logical coherence in extended interactions, avoiding contradictions and ensuring smooth thought progression.
## Training & Architecture
- **Model Size:** Optimized for high-performance reasoning with balanced efficiency.
- **Training Approach:** Fine-tuned using advanced structured learning techniques to enhance deliberative thinking and introspective processing.
- **Data Sources:** Trained on a diverse dataset covering philosophy, critical reasoning, and problem-solving scenarios to develop a deep intellectual foundation.
## Performance & Benchmarks
Dhanishtha outperforms conventional models in structured reasoning and contextual depth. The model has been rigorously evaluated across various metrics, demonstrating significant improvements in:
- **Logical Coherence & Argumentation:** Enhanced ability to follow complex discussions and construct persuasive arguments.
- **Depth of Analysis:** Excels in breaking down intricate topics into clear, structured responses.
- **Adaptive Conversational Flow:** Seamlessly shifts between casual and analytical tones based on user input.
## Deployment & Use Cases
Dhanishtha is designed for:
- **High-precision academic and philosophical discussions**
- **Deep problem-solving and strategic reasoning**
- **Engaging and thought-provoking conversations**
- **Use in AI-driven research and advanced dialogue systems**
## Benchmarks
We report Pass@1 accuracy averaged over 16 samples for each problem.
| Model | AIME 2024 | MATH 500 | AMC 2023 | Minerva Math | OlympiadBench | Avg. |
|-------------------------------|-----------|----------|----------|--------------|----------------|------|
| 2.5-7B-Instruct | 13.3 | 79.8 | 50.6 | 34.6 | 40.7 | 43.8 |
| rStar-Math-7B | 26.7 | 78.4 | 47.5 | - | 47.1 | - |
| Eurus-2.7B-PRIME | 26.7 | 79.2 | 57.8 | 38.6 | 42.1 | 48.9 |
| Qwen2.5-7B-SimpleRL | 26.7 | 82.4 | 62.5 | 39.7 | 43.3 | 50.9 |
| DeepSeek-R1-Distill-Qwen-1.5B | 28.8 | 82.8 | 62.9 | 26.5 | 43.3 | 48.9 |
| Still-1.5B | 32.5 | 84.4 | 66.7 | 29.0 | 45.4 | 51.6 |
| DeepScaleR-1.5B-Preview | 43.1 | 87.8 | 73.6 | 30.2 | 50.0 | 57.0 |
| O1-Preview | 40.0 | 81.4 | - | - | - | - |
| **Dhanishta** | 38.2 | 85.1 | 70.3 | 30.5 | 42.0 | 53.2 |
| **Dhanishta-Large** | - | - | - | - | - | - |
## Credits & License
Dhanishtha is developed and maintained by **HelpingAI**, pushing the boundaries of AI-driven introspection and structured reasoning. The model is open-source and community-driven, encouraging contributions and collaborative innovation. |