DMind-3-mini: Sovereign Intelligence for Web3 Finance

DMind-3-mini, developed by DMind, is a 4.2 billion parameter model built on a customized Qwen3.5-4B architecture, featuring a 128k token context window. It is designed as a Computational Financial Actuary to provide individual users with institutional-grade financial logic within a privacy-first, offline-capable engine. Unlike generalist LLMs, DMind-3-mini is not optimized for broad benchmarks but for the specific, high-stakes environment of decentralized finance (DeFi).

Key Capabilities

• Yield Attribution Analysis: Deconstructs APY sources to differentiate between Real Yield and Inflationary Yield.
• Liquidity Provisioning (LP) Simulation: Calculates optimal tick ranges for Uniswap V3 positions by modeling volatility surfaces.
• Risk-Adjusted Code Auditing: Identifies economic exploits in smart contracts, such as Flash Loan attack vectors, beyond mere syntax errors.
• Reflective Intelligence (System 2 Thinking): Utilizes a unique Contrastive Chain-of-Correction Supervised Fine-Tuning (C³-SFT) methodology to navigate risk by contrasting against plausible but flawed reasoning.
• Dual-State Inference: Operates in a "Standard Mode" for latency-optimized tasks and an "Audit Mode" for rigorously verified conclusions, internally generating negative hypotheses and applying critique.

Good For

• Web3 Financial Analysis: Users needing deep insights into DeFi mechanics, protocol risks, and yield generation.
• Individual Sovereign Users: Those seeking a private, local, and antifragile AI tool for personal financial strategy and risk assessment in Web3.
• High-Precision Financial Tasks: Due to its native BF16 precision, it's suitable for tasks requiring exact numerical calculations, such as APY/IL computations.
• Integration into the DMind Local Stack: Works synergistically with DMind-3-nano (The Shield) for real-time transaction safety checks, with DMind-3-mini (The Brain) handling complex strategy and deep research.

Note: This model requires a GPU with at least 12GB VRAM (e.g., NVIDIA RTX 4070Ti+, Apple M3/M4 Pro/Max) and is not recommended for 4-bit quantization in financial logic tasks to preserve numerical accuracy.