---
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
---
## 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
You can access **DMind-1** via the OpenRouter API. Simply specify the desired model in the `model` field of your request payload.
**API Endpoint:**
```
https://openrouter.ai/api/v1/chat/completions
```
**Authentication:**
- Obtain your API key from [OpenRouter](https://openrouter.ai/)
- Include it in the `Authorization` header as `Bearer YOUR_API_KEY`
**Model Identifiers:**
- `dmind-1` — Full-size expert model
**Example Request (Python):**
```python
import requests
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
data = {
"model": "dmind-1",
"messages": [
{"role": "user", "content": "Explain DeFi in simple terms."}
]
}
response = requests.post(
"https://openrouter.ai/api/v1/chat/completions",
headers=headers,
json=data
)
print(response.json())
```
**Example Request (cURL):**
```bash
curl https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "dmind-1",
"messages": [{"role": "user", "content": "What is a smart contract?"}]
}'
```
**Notes:**
- Replace `YOUR_API_KEY` with your actual OpenRouter API key.
- Change the `model` field to `dmind-1` as needed.
- Both models support the same API structure for easy integration.
### 4.3 OpenRouter Web Chat
You can try **DMind-1** instantly using the [OpenRouter Web Chat](https://openrouter.ai/chat).
- Select your desired model from the dropdown menu (**DMind-1**).
- Enter your prompt and interact with the model in real time.
[](https://openrouter.ai/chat)
## 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){kind=icon}
