DMind-3-mini GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 05fa625ea.

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🌌 The Era of Sovereign Intelligence

Not your weights, not your intelligence. In the dark forest of Web3, an individual without private AI is merely prey.

We stand at the intersection of history's two most significant exponential curves. Artificial Intelligence seeks to solve Knowledge & Logic, while Blockchain seeks to solve Trust & Value. However, the current convergence is flawed. Mainstream AI remains a centralized Computational Leviathan that runs on opaque servers, ingesting your data without comprehending the concept of Ownership. When a Web2 AI reads a smart contract, it sees code. When we look, we see Assets, Risks, and Game Theory.

In the 24/7 PVP environment of DeFi, traditional Copilots are merely assistants. You need an Agent capable of independent risk assessment. Humans are carbon-based lifeforms with limited processing speed and are increasingly outmatched by MEV bots and algorithmic predators. We need an evolutionary tool, an Exo-Cortex that runs locally, remains absolutely loyal, and deeply understands the dark logic of finance.

DMind-3-mini was not built to score points on general benchmarks. It was engineered to arm the individual against institutional extraction. We refuse to upload your Alpha strategies to the cloud. True Web3 AI must be Private, Local, and Antifragile.

🛡️ DMind-3-nano is your Shield. ⚔️ DMind-3-mini is your Spear.

Welcome to the era of Sovereign Intelligence.

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🧠 DMind-3-mini: The Computational Financial Actuary

1. Evolution & Legacy

The DMind lineage was born from a singular conviction that decentralized finance deserves decentralized intelligence. This journey began with DMind-1, which shattered the monopoly of closed-source AI by releasing the world's first Web3-native LLM. It continued with DMind-2, which proved that domain-specific fine-tuning could outmaneuver trillion-parameter giants in vertical benchmarks.

🚀 A Paradigm Shift DMind-3 represents our most significant evolutionary leap yet. We recognized that in the high-stakes environment of DeFi, standard knowledge retrieval is a liability. A model must do more than recite facts. It must possess Reflective Intelligence (System 2 Thinking) to navigate risk.

DMind-3-mini embodies this philosophy. Positioned as the Brain within our local ecosystem, it bridges the gap between the real-time reflexes of the edge-side DMind-3-nano and the macroscopic foresight of the cloud-native DMind-3. It is engineered not as a chatbot, but as a Computational Financial Actuary designed to bring institutional-grade logic to the individual sovereign user within a privacy-first, offline-capable engine.

2. ⚙️ Model Details

• Model Name: DMind-3-mini
• Organization: DMind
• Base Architecture: Qwen3-4B-Thinking-2507 (Customized Transformer w/ RoPE)
• Parameter Count: 4.2 Billion
• Precision: BF16 (Native)
  • ⚠️ Note: We strictly advise against 4-bit quantization for financial logic tasks to preserve numerical precision in APY/IL calculations.
• Context Window: 128k tokens
• Hardware Requirement: GPU with $\ge$ 12GB VRAM (Recommended: NVIDIA RTX 4070Ti+, Apple M3/M4 Pro/Max).

3. 🔬 Methodology: C³-SFT

DMind-3-mini introduces Contrastive Chain-of-Correction Supervised Fine-Tuning (C³-SFT). Unlike standard SFT which models a direct mapping \(P(y|x)\), C³-SFT forces the model to navigate a Correction Trajectory by contrasting against plausible but flawed reasoning.

(Figure 1: The C³-SFT training pipeline, illustrating the Triplet Data Structure and Contextual Loss Masking)

Mathematical Formalization

The optimization objective \(\mathcal{L}_{C^3}\) is defined as maximizing the conditional probability of the correction path given the error context:

$$ \mathcal{L}{C^3}(\theta) = - \mathbb{E}{\mathcal{D}} \left[ \sum_{t=1}^{T} \alpha_t \cdot \log P_\theta(y^+{t} \mid x, y^-, y^+{<t}) \right] + \lambda \mathbb{KL}[\pi_\theta || \pi_{\text{ref}}] $$

• \(\mathcal{D} = {(x, y^-, y^+{cot})}{i=1}^N\) represents the training dataset containing financial query triplets.
• \(y^-\) denotes the Negative Sample containing common logical fallacies.
• \(y^+_{cot}\) is the corrective Chain-of-Thought that the model aims to generate.
• \(\alpha_t\) is a dynamic attention weight that penalizes logical discontinuities.

Dual-State Inference Mechanism

During inference, DMind-3-mini operates in two distinct topological modes based on the presence of a trigger token \(\tau\). Let \(\mathcal{I}(x)\) denote the inference function:

$$ \hat{y} = \begin{cases} \operatorname*{arg,max}\limits_{y} P_\theta(y \mid x) & \text{if } \tau = \emptyset \quad (\text{Standard Mode}) \ \operatorname*{arg,max}\limits_{y} P_\theta(y \mid x, \mathcal{G}{neg}(x), \mathcal{H}{crit}) & \text{if } \tau = \texttt{} \quad (\text{Audit Mode}) \end{cases} $$

• Standard Mode: Optimized for latency.
• Audit Mode: The model internally generates a latent negative hypothesis \(\mathcal{G}{neg}(x)\) and applies the critique operator \(\mathcal{H}{crit}\) to derive a rigorously verified conclusion.

4. 💡 Intended Use: Web3 Financial Know-How

DMind-3-mini is not just a coder. It is a Financial Risk Assessor.

Key Capabilities

• 📊 Yield Attribution Analysis: Deconstructs APY sources to distinguish between Real Yield (Protocol Revenue) and Inflationary Yield (Token Emissions).
• 🌊 Liquidity Provisioning (LP) Simulation: Calculates optimal tick ranges for Uniswap V3 positions by modeling volatility surfaces locally.
• 🕵️‍♂️ Risk-Adjusted Code Auditing: Beyond syntax errors, it identifies economic exploits such as Flash Loan attack vectors based on price manipulation.

The Brain & Shield Ecosystem

For maximum security, we recommend the DMind Local Stack:

(Figure 2: The On-Device Inference Ecosystem showing the synergy between Nano and Mini)

• The Brain (DMind-3-mini): Runs on your high-performance laptop. Handles complex strategy formulation, deep research, and System 2 logic.
• The Shield (DMind-3-nano): Runs in your browser/wallet background. Handles real-time transaction signing safety checks and System 1 intuition.

5. 📚 Training Data

The model was fine-tuned on 82,000 high-value private samples:

6. 🏆 Performance Benchmarks

Evaluated on three key benchmarks: DMind Benchmark (Web3 Native Logic), FinanceQA (Financial Domain Knowledge), and AIME 2025 (Advanced Mathematical Reasoning).

The evaluation compares DMind-3-mini (4B) against top-tier frontier models (GPT-5.1, Claude Sonnet 4.5) and other efficient models. Despite its compact size, the Mini model demonstrates exceptional efficiency, particularly in specialized domain tasks where it outperforms significantly larger generalist models.

7. ⚖️ Limitations & Disclaimer

• High Hardware Barrier: Due to the decision to retain BF16 precision for financial accuracy, this model requires >= 12GB VRAM. It is not suitable for standard office laptops.
• Knowledge Cutoff: While the logic is robust, specific protocol data is limited to the training cutoff. Use with RAG for real-time data.
• Legal Disclaimer: This model is an analytical tool, not a financial advisor. The output (NFA) should never be the sole basis for investment decisions. The developers assume no liability for financial losses.

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🚀 If you find these models useful

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊