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---
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*

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{{- tool | tojson }}
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{%- elif message.role == "assistant" %}
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{{- '\n' }}
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{%- if tool_call.function %}
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{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{{- '<|im_end|>\n' }}
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151655": {
"content": "<|image_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151656": {
"content": "<|video_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151657": {
"content": "<tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151658": {
"content": "</tool_call>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151659": {
"content": "<|fim_prefix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151660": {
"content": "<|fim_middle|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151661": {
"content": "<|fim_suffix|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151662": {
"content": "<|fim_pad|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151663": {
"content": "<|repo_name|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151664": {
"content": "<|file_sep|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151665": {
"content": "<tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151666": {
"content": "</tool_response>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151667": {
"content": "<think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
},
"151668": {
"content": "</think>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": false
}
},
"additional_special_tokens": [
"<|im_start|>",
"<|im_end|>",
"<|object_ref_start|>",
"<|object_ref_end|>",
"<|box_start|>",
"<|box_end|>",
"<|quad_start|>",
"<|quad_end|>",
"<|vision_start|>",
"<|vision_end|>",
"<|vision_pad|>",
"<|image_pad|>",
"<|video_pad|>"
],
"bos_token": null,
"chat_template": "{%- if tools %}\n<|im_start|>system\n{%- if messages[0].role == 'system' %}\n{{ messages[0].content }}\n\n{%- endif %}\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>{%- for tool in tools %}\n{{ tool | tojson }}{%- endfor %}\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n{%- else %}{%- if messages[0].role == 'system' %}\n<|im_start|>system\n{{ messages[0].content }}<|im_end|>\n{%- endif %}{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for forward_message in messages %}{%- set index = (messages|length - 1) - loop.index0 %}{%- set message = messages[index] %}{%- set current_content = message.content if message.content is not none else '' %}{%- set tool_start = '<tool_response>' %}{%- set tool_start_length = tool_start|length %}{%- set start_of_message = current_content[:tool_start_length] %}{%- set tool_end = '</tool_response>' %}{%- set tool_end_length = tool_end|length %}{%- set start_pos = (current_content|length) - tool_end_length %}{%- if start_pos < 0 %}{%- set start_pos = 0 %}{%- endif %}{%- set end_of_message = current_content[start_pos:] %}{%- if ns.multi_step_tool and message.role == \"user\" and not(start_of_message == tool_start and end_of_message == tool_end) %}{%- set ns.multi_step_tool = false %}{%- set ns.last_query_index = index %}{%- endif %}{%- endfor %}\n{%- for message in messages %}{%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}<|im_start|>{{ message.role }}\n{{ message.content }}<|im_end|>\n{%- elif message.role == \"assistant\" %}<|im_start|>assistant\n{%- if message.content %}{{ message.content }}{%- endif %}{%- if message.tool_calls %}{%- for tool_call in message.tool_calls %}{%- if (loop.first and content) or (not loop.first) %}\n{%- endif %}{%- if tool_call.function %}{%- set tool_call = tool_call.function %}{%- endif %}<tool_call>\n{\"name\": \"{{ tool_call.name }}\", \"arguments\": {{ tool_call.arguments if tool_call.arguments is string else tool_call.arguments | tojson }}}\n</tool_call>{%- endfor %}{%- endif %}<|im_end|>\n{%- elif message.role == \"tool\" %}{%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}<|im_start|>user{%- endif %}\n<tool_response>\n{{ message.content }}\n</tool_response>{%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}<|im_end|>\n{%- endif %}{%- endif %}{%- endfor %}\n{%- if add_generation_prompt %}<|im_start|>assistant\n{%- endif %}",
"eos_token": "<|im_end|>",
"errors": "replace",
"clean_up_tokenization_spaces": false,
"extra_special_tokens": {},
"model_max_length": 40960,
"pad_token": "<|vision_pad|>",
"padding_side": "right",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}

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