---
license: mit
language:
- en
- zh
metrics:
- accuracy
base_model:
- Qwen/Qwen3-14B
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: 74.12
model: DMind-1-mini
model_rank: 2 / 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
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 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, with a normalized Web3 score of 77.44
- DMind-1-mini also performed strongly, ranking 2nd overall with a normalized Web3 score of 74.12. 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** | **Base Model** | **Download** |
|:--------------:|:--------------:|:----------------------------------------------------------------------------:|
| DMind-1-mini | Qwen3-14B | [Hugging Face Link](https://huggingface.co/dmind-ai/dmind-1-mini) |
### 4.2 OpenRouter API
You can access **DMind-1-mini** 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-mini` — Full-size expert model
**Example Request (Python):**
```python
import requests
headers = {
"Authorization": "Bearer YOUR_API_KEY",
"Content-Type": "application/json"
}
data = {
"model": "DMind-1-mini",
"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-mini",
"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-mini` as needed.
- Both models support the same API structure for easy integration.
### 4.3 OpenRouter Web Chat
You can try **DMind-1-mini** instantly using the [OpenRouter Web Chat](https://openrouter.ai/chat).
- Select your desired model from the dropdown menu (**DMind-1-mini**).
- 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-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](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}
