DMindAI/DMind-1-mini

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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

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:

• Web3-specific data distillation: High-quality instruction-following and QA examples generated by the teacher models.

• Distribution-level supervision: The student model learns to approximate the teachers' output distributions through soft-label guidance, preserving nuanced prediction behavior and confidence calibration.

• 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:

• 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-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