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  library_name: transformers
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  library_name: transformers
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+ license: apache-2.0
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+ datasets:
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+ - Congliu/Chinese-DeepSeek-R1-Distill-data-110k
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+ - Abhaykoul/Dhanishtha-R1
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+ language:
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+ - en
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+ base_model:
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+ - deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
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  ---
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+ # Dhanishtha Overview
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+ 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.
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+ ## Model Capabilities
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+ 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.
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+ ### Key Features:
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+ - **Structured Internal Reasoning:** Engages in self-dialogue within `<think></think>` tags, iterating through ideas and refining its thought process before responding.
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+ - **Progressive Thought Refinement:** Evaluates multiple perspectives, making logical connections and ensuring a well-rounded answer.
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+ - **Emotionally Intelligent Conversational Style:** Responses are expressive, engaging, and tailored for natural human interaction.
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+ - **Optimized for Critical Thinking & Problem-Solving:** Excels in analytical reasoning, debate, and deep philosophical discussions.
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+ - **Context Awareness:** Maintains logical coherence in extended interactions, avoiding contradictions and ensuring smooth thought progression.
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+ ## Training & Architecture
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+ - **Model Size:** Optimized for high-performance reasoning with balanced efficiency.
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+ - **Training Approach:** Fine-tuned using advanced structured learning techniques to enhance deliberative thinking and introspective processing.
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+ - **Data Sources:** Trained on a diverse dataset covering philosophy, critical reasoning, and problem-solving scenarios to develop a deep intellectual foundation.
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+ ## Performance & Benchmarks
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+ Dhanishtha outperforms conventional models in structured reasoning and contextual depth. The model has been rigorously evaluated across various metrics, demonstrating significant improvements in:
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+ - **Logical Coherence & Argumentation:** Enhanced ability to follow complex discussions and construct persuasive arguments.
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+ - **Depth of Analysis:** Excels in breaking down intricate topics into clear, structured responses.
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+ - **Adaptive Conversational Flow:** Seamlessly shifts between casual and analytical tones based on user input.
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+ ## Deployment & Use Cases
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+ Dhanishtha is designed for:
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+ - **High-precision academic and philosophical discussions**
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+ - **Deep problem-solving and strategic reasoning**
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+ - **Engaging and thought-provoking conversations**
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+ - **Use in AI-driven research and advanced dialogue systems**
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+ ## Credits & License
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+ 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.