DMindAI/DMind-1

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Table of Contents

• Introduction
• 1. Model Overview
• 2. Evaluation Results
• 3. Use Cases
• 4. Quickstart
  • 4.1 Model Downloads
  • 4.2 OpenRouter API
  • 4.3 OpenRouter Web Chat
• License
• Contact

Introduction

The rapid growth of Web3 technologies—blockchain, DeFi, and smart contracts—demands specialized AI large language models (LLMs) with precise domain alignment and advanced reasoning capabilities. However, General-purpose LLMs often lack the domain-specific accuracy, nuanced reasoning, and instruction-following aligned with expert expectations.

To address these limitations, we introduce DMind-1, a domain-specialized LLM fine-tuned for the Web3 ecosystem via supervised instruction tuning and reinforcement learning from human feedback (RLHF). Built on a powerful base model, DMind-1 achieves strong improvements in task accuracy, content safety, and expert-aligned interaction, significantly surpassing general-purpose models. DMind-1 represents a robust foundation for intelligent agents in the Web3 ecosystem.

1. Model Overview

DMind-1

DMind-1 is a specialized Web3 expert model built on the Qwen3-32B base. Leveraging a state-of-the-art transformer architecture, it integrates deep domain knowledge through a novel two-stage fine-tuning pipeline, establishing its distinctive strengths in Web3-specific applications.

Key Points:

• Comprehensive Domain Expertise Data: In the first stage, DMind-1 underwent Supervised Fine-Tuning (SFT) on 13,276 expert-curated knowledge items distilled from 32.7GB of Web3 documentation, covering 8 key subdomains including DeFi, tokenomics, governance, and smart contracts. These data points were extracted and structured by a team of domain experts to ensure both depth and accuracy. To enable efficient and scalable training, we employed Low-Rank Adaptation (LoRA) during the SFT stage, allowing DMind-1 to internalize specialized Web3 knowledge while preserving the general-language capabilities of its base model.

• Reinforcement Learning from Human Feedback (RLHF) To further align the model with expert expectations in realistic interaction scenarios and accuracy, we implemented an RLHF phase composed of:

  • Reward Model Training: We trained a domain-specific reward model using preference-ranked outputs collected from human experts across diverse Web3-specific question-answer and interaction scenarios. This model learned to assess which responses best reflect factual accuracy and expert-level reasoning in the Web3 domain.
  • Policy Optimization with PPO: Building on the SFT model, we fine-tuned Qwen3-32B using Proximal Policy Optimization (PPO), guided by the trained reward model. The policy network was optimized based on feedback from simulated Web3 dialogue environments, while LoRA ensured resource-efficient parameter updates and significantly reduced compute and memory requirements. This dual-stage approach enabled efficient fine-tuning of a larger model on Web3-specific tasks while achieving high alignment with human intent.

• Domain-Aligned Reasoning and Interaction: DMind-1 exhibits advanced web3-aligned reasoning and interactive capabilities in the following fields:

  • Natural Dialogue Fluency: Coherent, context-aware conversations on complex Web3 topics, with strong multi-turn consistency.

  • Complex Instruction Following: Reliable execution of multi-step instructions and conditional logic, supporting agent-driven workflows.

  • Safe and Compliant Content Generation: Outputs are aligned with domain-specific safety, ethics, and regulatory standards.

2. Evaluation Results

We evaluate DMind-1 and DMind-1-mini using the DMind Benchmark, a domain-specific evaluation suite designed to assess large language models in the Web3 context. The benchmark includes 1,917 expert-reviewed questions across nine core domain categories, and it features both multiple-choice and open-ended tasks to measure factual knowledge, contextual reasoning, and other abilities.

To complement accuracy metrics, we conducted a cost-performance analysis by comparing benchmark scores against publicly available input token prices across 24 leading LLMs. In this evaluation:

• DMind-1 achieved the highest Web3 score while maintaining one of the lowest token input costs among top-tier models such as Grok 3 and Claude 3.7 Sonnet.

• DMind-1-mini ranked second, retaining over 95% of DMind-1’s performance with greater efficiency in latency and compute.

Both models are uniquely positioned in the most favorable region of the score vs. price curve, delivering state-of-the-art Web3 reasoning at significantly lower cost. This balance of quality and efficiency makes the DMind models highly competitive for both research and production use.

3. Use Cases

• Expert-Level Question & Answering: Provides accurate, context-aware answers on blockchain, DeFi, smart contracts, and related Web3 topics.
• Compliance-Aware Support: Assists in drafting or reviewing content within regulatory and legal contexts.
• Content Generation in Domain: Produces Web3-specific blog posts, documentation, and tutorials tailored to developers and users.
• DeFi Strategy Suggestions: Generates insights and recommendations for yield farming, liquidity provision, and portfolio strategies based on user-provided data.
• Risk Management: Suggests strategies aligned with user risk profiles for more informed decision-making in volatile markets.

4. Quickstart

4.1 Model Downloads

Model	Base Model	Download
DMind-1	Qwen3-32B	Hugging Face Link
DMind-1-mini	Qwen3-14B	Hugging Face Link

4.2 OpenRouter API (Coming Soon)

Documentation for API access will be available soon.

4.3 OpenRouter Web Chat (Coming Soon)

Web chat interface documentation will be available soon.

License

• The code repository and model weights for DMind-1 is released under the MIT License.
• Commercial use, modification, and derivative works (including distillation and fine-tuning) are permitted.
• Base Models:
  • DMind-1 is derived from Qwen3-32B, originally licensed under the Qwen License.
  • Please ensure compliance with the original base model licenses when using or distributing derivatives.

Contact

For questions or support, please contact [email protected]