Update README.md

README.md

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----
-license: mit
----
+---
+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
+---
+
+<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/datasets/DMindAI/DMind-1-mini" 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://openrouter.ai/chat" target="_blank" style="margin: 2px;">
+    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DMind--1--mini-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
+
+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/dmind-1-web3-performance.jpeg)
+
+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.
+
+[![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]