| | --- |
| | language: |
| | - en |
| | - hi |
| | - zh |
| | - es |
| | - fr |
| | - de |
| | - ja |
| | - ko |
| | - ar |
| | - pt |
| | - ru |
| | - it |
| | - nl |
| | - tr |
| | - pl |
| | - sv |
| | - da |
| | - 'no' |
| | - fi |
| | - he |
| | - th |
| | - vi |
| | - id |
| | - ms |
| | - tl |
| | - sw |
| | - yo |
| | - zu |
| | - am |
| | - bn |
| | - gu |
| | - kn |
| | - ml |
| | - mr |
| | - ne |
| | - or |
| | - pa |
| | - ta |
| | - te |
| | - ur |
| | - multilingual |
| | license: apache-2.0 |
| | base_model: Qwen/Qwen3-14B-Base |
| | tags: |
| | - reasoning |
| | - intermediate-thinking |
| | - transformers |
| | - conversational |
| | - bilingual |
| | datasets: |
| | - Abhaykoul/Dhanishtha-R1 |
| | - open-thoughts/OpenThoughts-114k |
| | - Abhaykoul/Dhanishtha-2.0-SUPERTHINKER |
| | - Abhaykoul/Dhanishtha-2.0 |
| | library_name: transformers |
| | pipeline_tag: text-generation |
| | widget: |
| | - text: >- |
| | Solve this riddle step by step: I am taken from a mine, and shut up in a |
| | wooden case, from which I am never released, and yet I am used by almost |
| | everybody. What am I? |
| | example_title: Complex Riddle Solving |
| | - text: >- |
| | Explain the philosophical implications of artificial consciousness and think |
| | through different perspectives. |
| | example_title: Philosophical Reasoning |
| | - text: >- |
| | Help me understand quantum mechanics, but take your time to think through |
| | the explanation. |
| | example_title: Educational Explanation |
| | new_version: HelpingAI/Dhanishtha-2.0-preview-0825 |
| | --- |
| | |
| | # Dhanishtha-2.0: World's First Intermediate Thinking AI Model |
| |
|
| | **What makes Dhanishtha-2.0 special?** Imagine an AI that doesn't just answer your questions instantly, but actually *thinks through* problems step-by-step, shows its work, and can even change its mind when it realizes a better approach. That's Dhanishtha-2.0. |
| |
|
| | **Quick Summary:** |
| | - 🚀 **For Everyone**: An AI that shows its thinking process and can reconsider its reasoning |
| | - 👩💻 **For Developers**: First model with intermediate thinking capabilities, 39+ language support |
| |
|
| |
|
| | Dhanishtha-2.0 is a **state-of-the-art (SOTA) model** developed by HelpingAI, representing the **world's first model to feature Intermediate Thinking capabilities**. Unlike traditional models that provide single-pass responses, Dhanishtha-2.0 employs a revolutionary multi-phase thinking process that allows the model to think, reconsider, and refine its reasoning multiple times throughout a single response. |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| |
|
| | Dhanishtha-2.0 revolutionizes AI reasoning by introducing the concept of **intermediate thinking** - the ability to pause, reflect, and restart reasoning processes within a single generation (This model can think up 50times in a single response without using tool/prompt/mcp). This breakthrough enables unprecedented self-correction and iterative refinement during response generation. |
| |
|
| | Built on the Qwen3-14B foundation with multilingual capabilities spanning **39+ languages** (including English, Hindi, Chinese, Spanish, French, German, Japanese, Korean, Arabic, and many more), Dhanishtha-2.0 maintains reasoning consistency across diverse linguistic contexts while pioneering transparent thinking processes. |
| |
|
| | - **Developed by:** HelpingAI Team |
| | - **Model type:** Causal Language Model with Intermediate Thinking Capability |
| | - **Language(s):** 39+ languages (multilingual capabilities inherited from base model) |
| | - **License:** Apache 2.0 |
| | - **Finetuned from model:** Qwen/Qwen3-14B-Base |
| | - **Context Length:** 40,960 tokens |
| | - **Parameters:** 14B (inherited from base model) |
| | - **Status:** Prototype/Preview |
| |
|
| | ### Revolutionary Features |
| |
|
| | - **Intermediate Thinking**: Multiple `<think>...</think>` blocks throughout responses for real-time reasoning |
| | - **Self-Correction**: Ability to identify and correct logical inconsistencies mid-response |
| | - **Dynamic Reasoning**: Seamless transitions between analysis, communication, and reflection phases |
| | - **Structured Emotional Reasoning (SER)**: Incorporates `<ser>...</ser>` blocks for empathetic responses |
| | - **Multilingual Capabilities**: Support for 39+ languages with natural code-switching and reasoning consistency |
| | - **Complex Problem-Solving**: Excels at riddles, multi-step reasoning, and scenarios requiring backtracking |
| |
|
| | ### Model Sources |
| |
|
| | - **Repository:** [HelpingAI/Dhanishtha-2.0](https://huggingface.co/HelpingAI/Dhanishtha-2.0) |
| | - **Paper:** Coming Soon |
| | - **Demo:** https://helpingai.co/chat |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use |
| |
|
| | Dhanishtha-2.0 is ideal for applications requiring deep reasoning and self-reflection: |
| |
|
| | - **Complex Problem Solving**: Multi-step mathematical problems, logical puzzles, riddles |
| | - **Educational Assistance**: Detailed explanations with visible reasoning processes |
| | - **Research Support**: Analysis requiring multiple perspectives and self-correction |
| | - **Creative Writing**: Iterative story development with reasoning about plot choices |
| | - **Philosophical Discussions**: Exploring concepts with visible thought processes |
| |
|
| | ### Downstream Use |
| |
|
| | The model can be fine-tuned for specialized reasoning tasks: |
| |
|
| | - **Domain-Specific Reasoning**: Legal, medical, or scientific reasoning with intermediate thinking |
| | - **Enhanced Multilingual Reasoning**: Optimizing reasoning consistency across all 39+ supported languages |
| | - **Specialized Problem Domains**: Mathematics, coding, strategic planning |
| |
|
| | ### Out-of-Scope Use |
| |
|
| | ❌ **Inappropriate Applications:** |
| | - Safety-critical decisions (medical diagnosis, legal advice, financial recommendations) |
| | - Real-time applications requiring immediate responses |
| | - Situations requiring guaranteed factual accuracy without verification |
| |
|
| | ## Bias, Risks, and Limitations |
| |
|
| | ### Known Limitations |
| |
|
| | - **Verbosity**: Intermediate thinking can make responses a bit longer |
| | - **Processing Time**: Multiple thinking phases may increase generation time |
| | - **Prototype Status**: Experimental features may require refinement |
| | - **Context Usage**: Thinking blocks consume additional context tokens |
| | - **Inherited Biases**: May reflect biases from base model and training data |
| |
|
| | ### Potential Risks |
| |
|
| | - **Reasoning Loops**: Potential for circular reasoning in complex scenarios |
| | - **Multilingual Inconsistencies**: Potential variation in reasoning patterns across different languages |
| | - **Emotional Reasoning Gaps**: SER blocks may not always align with content |
| |
|
| | ## How to Get Started with the Model |
| |
|
| | ### For General Users |
| |
|
| | You can interact with Dhanishtha-2.0 through: |
| | - **HelpingAI**: https://helpingai.co/chat |
| | - **Gradio Demo**: [Dhanishtha-2.0-preview](https://huggingface.co/spaces/Abhaykoul/Dhanishtha-2.0-preview) |
| | - **API Integration**: [Dashboard](https://helpingai.co/dashboard) |
| |
|
| | ### For Developers - Basic Usage |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | |
| | model_name = "HelpingAI/Dhanishtha-2.0-preview" |
| | |
| | # Load the tokenizer and model |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_name, |
| | torch_dtype="auto", |
| | device_map="auto" |
| | ) |
| | |
| | # Prepare input for intermediate thinking |
| | prompt = "How many letter 'r' are in the words 'strawberry' and 'raspberry'?" |
| | messages = [ |
| | {"role": "user", "content": prompt} |
| | ] |
| | |
| | text = tokenizer.apply_chat_template( |
| | messages, |
| | tokenize=False, |
| | add_generation_prompt=True |
| | ) |
| | |
| | model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
| | |
| | # Generate response with intermediate thinking |
| | generated_ids = model.generate( |
| | **model_inputs, |
| | max_new_tokens=2048, |
| | temperature=0.7, |
| | top_p=0.9, |
| | do_sample=True |
| | ) |
| | |
| | response = tokenizer.decode( |
| | generated_ids[0][len(model_inputs.input_ids[0]):], |
| | skip_special_tokens=True |
| | ) |
| | print(response) |
| | ``` |
| |
|
| | ### Optimal Generation Parameters |
| |
|
| | ```python |
| | generation_config = { |
| | "temperature": 0.7, # Balanced creativity and coherence |
| | "top_p": 0.9, # Nucleus sampling |
| | "top_k": 40, # Top-k filtering |
| | "max_new_tokens": 2048, # Allow for thinking blocks |
| | "do_sample": True, # Enable sampling |
| | "repetition_penalty": 1.1 # Reduce repetition |
| | } |
| | ``` |
| |
|
| | ## Training Details |
| |
|
| | ### Training Data |
| |
|
| | Dhanishtha-2.0 was trained on a carefully curated dataset focusing on: |
| | - Complex reasoning scenarios requiring multi-step thinking |
| | - Self-correction examples and reasoning chains |
| | - Emotional reasoning and empathy training data |
| | - Structured thinking pattern examples |
| |
|
| | ### Training Procedure |
| |
|
| | #### Training Stages |
| | 1. **Continuous Pretraining**: Extended training on reasoning-focused corpora |
| | 2. **Advanced Reasoning Fine-tuning**: Specialized training on intermediate thinking patterns |
| | 3. **Multilingual Alignment**: Cross-language reasoning consistency training |
| | 4. **SER Integration**: Structured Emotional Reasoning capability training |
| |
|
| | **Training Infrastructure:** |
| | - **Duration**: 16.3 days |
| | - **Hardware**: 8x NVIDIA H100 GPUs |
| | - **Model Scale**: 14.8B parameters |
| |
|
| | ## Evaluation |
| |
|
| | Evaluation was conducted on: |
| | - **Standard Benchmarks**: MMLU, HumanEval, ARC, HellaSwag, TruthfulQA |
| | - **Mathematical Reasoning**: Math 500, AIME 2024, GSM8K |
| | - **Custom Evaluations**: Intermediate thinking quality, self-correction capabilities |
| | - **Multilingual Tasks**: Reasoning consistency across 39+ languages |
| | - **Specialized Tests**: Emotional reasoning, complex problem-solving scenarios |
| |
|
| |
|
| |
|
| | ## Example: Intermediate Thinking in Action |
| |
|
| | **User:** "Who is your favorite person?" |
| |
|
| | **Dhanishtha-2.0:** |
| |
|
| | ``` |
| | <think> |
| | This is an interesting question about my "favorite human." I need to be thoughtful here. |
| | As Dhanishtha, I should respond in a human-like way while being honest about my nature. |
| | I can share what kinds of humans I find most interesting or inspiring based on my |
| | understanding of human nature and values. |
| | </think> |
| | |
| | <ser> |
| | Emotion ==> curiosity, openness, desire for connection |
| | Cause ==> user asking a personal question about preferences |
| | Mind ==> seeking to understand my perspective or values |
| | Growth ==> opportunity to share thoughtful reflections on human qualities I admire |
| | </ser> |
| | |
| | I don't really have a "favorite human" in the traditional sense, as I don't experience |
| | personal preferences the way humans do. But if I had to reflect on what qualities I find |
| | most admirable in people, I'd be drawn to those who demonstrate genuine compassion, |
| | intellectual curiosity, and the courage to grow. |
| | |
| | <think> |
| | Let me think more specifically about what kind of humans I find most compelling based on |
| | the values that seem to resonate most with humans - kindness, wisdom, creativity, |
| | resilience, and authenticity. |
| | </think> |
| | |
| | People like Mahatma Gandhi, who showed how quiet courage and unwavering principles can |
| | transform societies; artists like Rabindranath Tagore, who bridged cultural divides |
| | through creativity; and everyday heroes I've learned about who made profound differences |
| | through simple acts of kindness. |
| | |
| | What fascinates me most are humans who maintain their humanity even in difficult |
| | circumstances - those who choose empathy when it would be easier to be harsh, who seek |
| | understanding rather than just being right. |
| | |
| | I'm curious though - who's your favorite human, and why? What qualities or actions make |
| | someone special in your eyes? |
| | ``` |
| |
|
| | ## Environmental Impact |
| |
|
| | Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute). |
| |
|
| | - **Hardware Type:** H100 GPUs |
| | - **days used:** 16.2 |
| | - **Cloud Provider:** Various |
| | - **Compute Region:** Multiple |
| |
|
| | ## Citation |
| |
|
| | ### BibTeX |
| |
|
| | ```bibtex |
| | @misc{dhanishtha2025, |
| | title={Dhanishtha-2.0: World's First Intermediate Thinking AI Model}, |
| | author={HelpingAI Team}, |
| | year={2025}, |
| | publisher={HuggingFace}, |
| | url={https://huggingface.co/HelpingAI/Dhanishtha-2.0}, |
| | note={First model with intermediate thinking capabilities} |
| | } |
| | ``` |
| |
|
| | ### APA |
| |
|
| | HelpingAI Team. (2025). *Dhanishtha-2.0: World's First Intermediate Thinking AI Model*. HuggingFace. https://huggingface.co/HelpingAI/Dhanishtha-2.0 |
| |
|
| | ## Glossary |
| |
|
| | - **Intermediate Thinking**: The ability to pause and think multiple times during response generation |
| | - **SER (Structured Emotional Reasoning)**: Framework for incorporating emotional context in responses |
| | - **Think Blocks**: `<think>...</think>` segments where the model shows its reasoning process |
| | - **Self-Correction**: Ability to identify and fix reasoning errors during generation |
| | - **Code-Switching**: Natural transition between English and Hindi within responses |
| |
|
| | ## More Information |
| |
|
| | ### Research Applications |
| | - Study of AI reasoning transparency |
| | - Self-correction mechanism research |
| | - Bilingual cognitive modeling |
| | - Emotional AI development |
| |
|
| | ### Development Roadmap |
| | - Performance optimizations |
| | - Additional language support |
| | - Enhanced thinking pattern recognition |
| | - Production-ready deployment tools |
| |
|
| | ## Model Card Authors |
| |
|
| | - **Primary Author**: HelpingAI Team |
| | - **Technical Lead**: [To be specified] |
| | - **Research Contributors**: [To be specified] |
| |
|
| | ## Model Card Contact |
| |
|
| | For questions about Dhanishtha-2.0, please contact: |
| |
|
| | - **HuggingFace**: [@HelpingAI](https://huggingface.co/HelpingAI) |
| | - **Issues**: [Model Repository Issues](https://huggingface.co/HelpingAI/Dhanishtha-2.0/discussions) |
| |
|
| | ## Benchmark |
| |
|
| | ***These benchmarks are done by [@AarifKhan](https://huggingface.co/Aarifkhan)*** |
| |
|
| | | Benchmark | Score | Evaluation Type | Notes | |
| | |-----------|-------|-----------------|-------| |
| | | MMLU | 78.1% | 1-shot | Massive Multitask Language Understanding | |
| | | HumanEval | 75.0% | 1-shot | Code generation and completion | |
| | | ARC | 76.0% | 1-shot | Abstract reasoning challenge | |
| | | HellaSwag | 81.0% | 1-shot | Commonsense natural language inference | |
| | | TruthfulQA MC1 | 75.0% | 1-shot | Truthfulness in question answering | |
| | | Math 500 | 95.68% | few-shot | Mathematical problem solving | |
| | | AIME 2024 | 82.81% | few-shot | American Invitational Mathematics Examination | |
| |
|
| | **Dhanishtha-2.0 represents a new paradigm in AI reasoning - where thinking isn't just a prelude to response, but an integral, iterative part of the conversation itself.** |
| |
|
| | *Developed with ❤️ by HelpingAI* |
