README.md

---
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
---

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## 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


![DMind-1 Web3 Performance](figures/normalized-performance-with-price.jpeg)

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 3.5 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](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.

[![OpenRouter Chat](https://img.shields.io/badge/🤖%20Try%20on-OpenRouter%20Chat-536af5?color=536af5&logoColor=white)](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 [email protected]