Update README.md

README.md

@@ -42,30 +42,346 @@ language:
 - ur
 - multilingual
 license: apache-2.0
-base_model: HelpingAI/Dhanishtha-2.0-preview
+base_model: Qwen/Qwen3-14B-Base
 tags:
 - reasoning
 - intermediate-thinking
 - transformers
 - conversational
 - bilingual
-- mlx
 datasets:
 - Abhaykoul/Dhanishtha-R1
 - open-thoughts/OpenThoughts-114k
 - Abhaykoul/Dhanishtha-2.0-SUPERTHINKER
 - Abhaykoul/Dhanishtha-2.0
-library_name: mlx
+
+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?'
+- 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
+- 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
+- text: >-
+    Help me understand quantum mechanics, but take your time to think through
     the explanation.
   example_title: Educational Explanation
 ---
+
+# 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 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*