Update README.md

README.md

@@ -74,16 +74,16 @@ To support real-time and resource-constrained applications, we further introduce
 ### 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
+- **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
+- **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
+- **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
+- **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.
 
@@ -99,11 +99,11 @@ Under this rigorous evaluation:
 
 
 ## 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
+- **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