--- license: mit language: - en - zh metrics: - accuracy base_model: - Qwen/Qwen3-32B pipeline_tag: text-generation library_name: transformers tags: - blockchain - conversational - web3 - qwen3 eval_results: - task: domain-specific evaluation dataset: DMindAI/DMind_Benchmark metric: normalized web3 score score: 77.44 model: DMind-1 model_rank: 1 / 24 ---
DMind-1
DMind Website Hugging Face X Apache 2.0 Code License: MIT Model License: Model Agreement
## 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) - [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. To support real-time and resource-constrained applications, we further release **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. ## 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. ### DMind-1-mini To address scenarios requiring lower latency and faster inference, we also 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 in-house Web3 expert model. - **GPT-o3 + DeepResearch**: A general-purpose SOTA LLM with broad capabilities. The **Distillation pipeline** combines: - **Web3-specific data distillation**, using filtered instruction-following and QA samples generated by the teachers - **Soft-label supervision**, encouraging the student to match the teachers’ output distributions and uncertainty - **Intermediate representation alignment**, transferring structural knowledge from selected internal layers This multi-level distillation strategy allows DMind-1-mini to maintain high Web3 task performance with significantly reduced computational overhead and latency, making it suitable for real-time applications such as instant Q&A and on-chain analytics, and lightweight agent deployment. ## 2. Evaluation Results ![DMind-1 Web3 Performance](figures/dmind-1-web3-performance.png) We evaluate DMind-1 using the **DMind Benchmark**, a domain-specific evaluation suite tailored to assess large language models in the Web3 context. The benchmark spans 1,917 expert-reviewed questions across nine critical categories—including Blockchain Fundamentals, Infrastructure, Smart Contracts, DeFi, DAO, NFT, Token Economics, Meme, and Security. It combines multiple-choice and subjective open-ended tasks, simulating real-world challenges and requiring deep contextual understanding, which provides a comprehensive assessment of both factual knowledge and advanced reasoning. Under this rigorous evaluation, DMind-1 ranked 1st among 24 leading models, outperforming both proprietary (e.g., Grok-3) and open-source (e.g., DeepSeek-R1) LLMs. Notably, our distilled variant DMind-1-mini also performed strongly, ranking 2nd overall. This demonstrates the effectiveness of our compact distillation pipeline. ## 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** | **#Total Params** | **#Activated Params** | **Context Length** | **Download** | |:--------------:|:----------------:|:--------------------:|:-----------------:|:----------------------------------------------------------------------------:| | DMind-1 | | | | [Hugging Face Link](https://huggingface.co/dmind-ai/dmind-1-base) | | DMind-1-mini | | | | [Hugging Face Link](https://huggingface.co/dmind-ai/dmind-1) | ### 4.2 OpenRouter API ## Contact For questions or support, please contact team@dmind.ai