HelpingAI

Currently, only the LLM and Classfication section of this model are fully ready. This model contains layers from our diffrent models To aline layers we have done post-training after merging layers

License: other license. License name: helpingai. License link: https://helpingai.co/license.

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. 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 - Intermediate Thinking: Multiple ` ... ` 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 ` ... ` 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 - Repository: HelpingAI/Dhanishtha-2.0 - Demo: https://chat.helpingai.co 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 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 ❌ Inappropriate Applications: - Safety-critical decisions (medical diagnosis, legal advice, financial recommendations) - Real-time applications requiring immediate responses - Situations requiring guaranteed factual accuracy without verification - 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 - 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 You can interact with Dhanishtha-2.0 through: - HelpingAI: https://helpingai.co/chat - Gradio Demo: Dhanishtha-2.0-preview - API Integration: Dashboard 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 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: 4 days - Hardware: 8x NVIDIA H100 GPUs - Model Scale: 14.8B parameters 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 Carbon emissions can be estimated using the Machine Learning Impact calculator. - Hardware Type: H100 GPUs - days used: 16.2 - Cloud Provider: Various - Compute Region: Multiple HelpingAI Team. (2025). Dhanishtha-2.0: World's First Intermediate Thinking AI Model. HuggingFace. https://huggingface.co/HelpingAI/Dhanishtha-2.0 - 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: ` ... ` 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 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 - Primary Author: HelpingAI Team - Technical Lead: [To be specified] - Research Contributors: [To be specified] For questions about Dhanishtha-2.0, please contact: - HuggingFace: @HelpingAI - Issues: Model Repository Issues 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.

HelpingAI is licensed under an other license. For more information, visit the license link at https://helpingai.co/license.

A library for natural language processing using transformers. It is licensed under Apache 2.0.

Base model HelpingAI/HelpingAI2.5-10B with other license.