Update README.md

README.md

@@ -11,15 +11,7 @@ base_model:
 
 ## Model Overview
 
-DMind-2 is a series of Web3 investment analysis language models designed to provide real-time, professional Web3 investment consulting services for individual investors and professional institutions. Standing on the shoulders of numerous open-source pioneers, we have successfully launched three model variants through innovative post-training techniques. Among these, DMind2-mini is specifically optimized for edge deployment, enabling users to access institutional-grade investment analysis capabilities on local devices without concerns about data privacy or network latency.
-
-## Core Positioning
-
-DMind-2 focuses on Web3 investment opinion generation, financial consulting services, and comprehensive financial investment computational analysis. The series offers different deployment options to meet diverse user needs:
-
-DMind2-mini: Edge deployment for maximum privacy and zero-latency analysis on personal devices
-DMind2-base: Professional trading terminals and workstations
-DMind2-large: Enterprise and institutional deployment
+DMind-2 is a series of Web3 investment analysis language models designed to provide real-time, professional Web3 investment consulting services for individual investors and professional institutions. Standing on the shoulders of numerous open-source pioneers, we have successfully launched two model variants through innovative post-training techniques. Among these, DMind2-mini is specifically optimized for edge deployment, enabling users to access institutional-grade investment analysis capabilities on local devices without concerns about data privacy or network latency.
 
 ## Model Variants(DMind2-mini)
 
@@ -52,26 +44,25 @@ $$
 
 Where:
 
-- $\theta_s$ and $\theta_t$ represent student (trainable) and teacher (frozen) model parameters
-- $P_{\theta}^{(i)}$ denotes the probability distribution at reasoning step $i$
-- $\lambda(t) = \lambda_0 \cdot (1 + \gamma \cdot \text{complexity}(x_t))$ is the dynamic weight function
-- $\alpha_i = \exp(-\delta \cdot i/T)$ implements exponential decay for later reasoning steps
-- $\mathcal{L}_{\text{QS}}$ is the quality scoring loss ensuring reasoning coherence
+* \\(\theta_s\\) and \\(\theta_t\\) represent student (trainable) and teacher (frozen) model parameters.
+* \\(P_{\theta}^{(i)}\\) denotes the probability distribution at reasoning step \\(i\\).
+* \\(\lambda(t) = \lambda_0 \cdot (1 + \gamma \cdot \text{complexity}(x_t))\\) is the dynamic weight function.
+* \\(\alpha_i = \exp(-\delta \cdot i/T)\\) implements exponential decay for later reasoning steps.
+* \\(\mathcal{L}_{\text{QS}}\\) is the quality scoring loss ensuring reasoning coherence.
 
-$$ 
-a_o 
-$$
 
 
 #### Dynamic Weight Adjustment Mechanism
 
 The complexity-aware weight adjustment is formulated as:
 
-$\lambda(t) = \begin{cases}
+$$
+\lambda(t) = \begin{cases}
 \lambda_{\text{high}} \cdot \left(1 + \tanh\left(\frac{\mathcal{H}(x_t) - \mu_{\mathcal{H}}}{\sigma_{\mathcal{H}}}\right)\right) & \text{if } \mathcal{T}(x_t) \in \{\text{DeFi Analysis, Risk Assessment}\} \\
 \lambda_{\text{base}} & \text{if } \mathcal{T}(x_t) \in \{\text{Market Data, Price Query}\} \\
 \lambda_{\text{base}} \cdot \left(1 + \frac{\mathcal{S}(c_t)}{|\mathcal{V}_{\text{Web3}}|}\right) & \text{otherwise}
-\end{cases}$
+\end{cases}
+$$
 
 Where $\mathcal{H}(x_t)$ measures reasoning complexity through chain length and branching factor, $\mathcal{S}(c_t)$ counts domain-specific terms, and $|\mathcal{V}_{\text{Web3}}|$ is the Web3 vocabulary size.