Table of Contents
Introduction
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).
To support real-time and resource-constrained applications, we further introduce DMind-1-mini, a compact variant distilled from both DMind-1 and a generalist LLM using a multi-level distillation framework. It retains key domain reasoning abilities while operating with significantly lower computational overhead.
DMind-1 and DMind-1-mini represent a robust foundation for intelligent agents in the Web3 ecosystem.
1. Model Overview
DMind-1-mini
To address scenarios requiring lower latency and faster inference, we introduce DMind-1-mini, a lightweight distilled version of DMind-1 based on Qwen3-14B. DMind-1-mini is trained using knowledge distillation and our custom DeepResearch framework, drawing from two teacher models:
- DMind-1 (Qwen3-32B): Our specialized Web3 domain model.
- GPT-o3 + DeepResearch: A general-purpose SOTA LLM, with its outputs processed through our DeepResearch framework for Web3 domain alignment.
The Distillation pipeline combines:
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Web3-specific data distillation: High-quality instruction-following and QA examples generated by the teacher models.
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Distribution-level supervision: The student model learns to approximate the teachers' output distributions through soft-label guidance, preserving nuanced prediction behavior and confidence calibration.
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Intermediate representation transfer: Knowledge is transferred by aligning intermediate representations between teacher and student models, promoting deeper structural understanding beyond surface-level mimicry.
This multi-level distillation strategy enables DMind-1-mini to maintain high Web3 task performance while significantly reducing computational overhead and latency, making it suitable for real-time applications such as instant Q&A, on-chain analytics, and lightweight agent deployment.
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-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-mini is released under the MIT License.
- Commercial use, modification, and derivative works (including distillation and fine-tuning) are permitted.
- Base Models:
- DMind-1-mini is derived from Qwen3-14B, 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