| --- |
| license: mit |
| language: |
| - en |
| - zh |
| metrics: |
| - accuracy |
| base_model: |
| - Qwen/Qwen3-32B |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - blockchain |
| - conversational |
| - web3 |
| - qwen3 |
| |
| |
| |
| |
| |
| |
| |
| --- |
| |
| <p align="center"> |
| <img src="figures/dmind-ai-logo.png" width="300" alt="DMind Logo" /> |
| </p> |
| <hr> |
| <div align="center" style="line-height: 1;"> |
| <a href="https://dmind.ai/" target="_blank" style="margin: 2px;"> |
| <img alt="DMind Website" src="https://img.shields.io/badge/DMind-Homepage-blue?logo=data:image/svg+xml;base64,)" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://huggingface.co/DMindAI" target="_blank" style="margin: 2px;"> |
| <img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-DMind-ffd21f?color=ffd21f&logo=huggingface" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://x.com/dmind_ai" target="_blank" style="margin: 2px;"> |
| <img alt="X" src="https://img.shields.io/badge/X-@DMind-1DA1F2?logo=x" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://huggingface.co/spaces/DMindAI/DMind-1" target="_blank" style="margin: 2px;"> |
| <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DMind--1-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://discord.gg/xxwmPHU3" target="_blank" style="margin: 2px;"> |
| <img alt="Discord" src="https://img.shields.io/badge/Discord-DMind-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| <a href="https://opensource.org/licenses/MIT" target="_blank" style="margin: 2px;"> |
| <img alt="Code License: MIT" src="https://img.shields.io/badge/Code%20License-MIT-yellow.svg" style="display: inline-block; vertical-align: middle;"/> |
| </a> |
| </div> |
| |
|
|
| ## Table of Contents |
| - [Introduction](#introduction) |
| - [1. Model Overview](#1-model-overview) |
| - [2. Evaluation Results](#2-evaluation-results) |
| - [3. Use Cases](#3-use-cases) |
| - [4. Quickstart](#4-quickstart) |
| - [4.1 Model Downloads](#41-model-downloads) |
| - [4.2 OpenRouter API](#42-openrouter-api) |
| - [4.3 OpenRouter Web Chat](#43-openrouter-web-chat) |
| - [License](#license) |
| - [Contact](#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](https://huggingface.co/datasets/DMindAI/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](https://huggingface.co/DMindAI/DMind-1) | |
| | DMind-1-mini | Qwen3-14B | [Hugging Face Link](https://huggingface.co/DMindAI/DMind-1-mini) | |
|
|
| ### 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](https://github.com/QwenLM/Qwen3). |
| - Please ensure compliance with the original base model licenses when using or distributing derivatives. |
|
|
| ## Contact |
| For questions or support, please contact team@dmind.ai |
