Table of Contents
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:
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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.
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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.
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Domain-Aligned Reasoning and Interaction: DMind-1 exhibits advanced web3-aligned reasoning and interactive capabilities in the following fields:
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Natural Dialogue Fluency: Coherent, context-aware conversations on complex Web3 topics, with strong multi-turn consistency.
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Complex Instruction Following: Reliable execution of multi-step instructions and conditional logic, supporting agent-driven workflows.
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Safe and Compliant Content Generation: Outputs are aligned with domain-specific safety, ethics, and regulatory standards.
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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:
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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.
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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 team@dmind.ai