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README.md

@@ -27,38 +27,20 @@ pipeline_tag: image-text-to-text
 
 LogicFlow-Gemma-3-27b-thinking is an advanced **multimodal reasoning model** built upon [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it), specifically designed to excel at complex logical reasoning, mathematical problem-solving, and step-by-step analytical thinking. This model represents a significant advancement in AI reasoning capabilities, achieved through careful fine-tuning on three specialized, high-quality datasets using LoRA (Low-Rank Adaptation) technique.
 
-### Training Dataset Foundation
-
-Our model has been meticulously trained on three cutting-edge datasets, each contributing unique reasoning capabilities:
-
-#### 🧠 **OpenO1-SFT Dataset**
-- **Purpose**: Supervised fine-tuning for advanced reasoning patterns
-- **Content**: High-quality reasoning demonstrations with explicit thought processes
-- **Impact**: Enables the model to break down complex problems systematically and show transparent reasoning chains
-
-#### 💭 **Open-Thoughts Dataset** 
-- **Purpose**: Step-by-step thinking process modeling
-- **Content**: Detailed internal monologues and reasoning progressions for various problem types
-- **Impact**: Teaches the model to externalize its thinking process, making reasoning transparent and verifiable
-
-#### 🔢 **OpenR1-Math Dataset**
-- **Purpose**: Mathematical reasoning and problem-solving specialization  
-- **Content**: Comprehensive mathematical problems with detailed solution methodologies
-- **Impact**: Significantly enhances performance on mathematical reasoning tasks, from basic arithmetic to advanced competition-level problems
 
 ### Key Innovations
 
 This unique combination of datasets creates a model that not only provides correct answers but also demonstrates **how** it arrives at those answers, making it particularly valuable for educational applications, research, and any scenario requiring explainable AI reasoning.
 
 The model demonstrates enhanced capabilities in:
-- **🧠 Logical Reasoning**: Improved ability to work through complex logical problems step by step
-- **🔢 Mathematical Problem Solving**: Enhanced performance on mathematical reasoning tasks (76.8% MATH, 13.3% AIME25)
-- **🔬 Scientific Analysis**: Exceptional scientific reasoning capabilities (45.96% GPQA Diamond)
-- **💭 Chain-of-Thought Reasoning**: Superior step-by-step thinking with detailed reasoning chains and self-verification
-- **📊 Structured Analysis**: Improved at breaking down complex problems into manageable components
-- **✅ Multi-Method Verification**: Uses multiple approaches to validate results and ensure accuracy
-- **👁️ Vision Understanding**: Ability to analyze and reason about images, charts, diagrams, and visual data
-- **🔄 Multimodal Reasoning**: Combining visual and textual information for comprehensive analysis
+- ** Logical Reasoning**: Improved ability to work through complex logical problems step by step
+- ** Mathematical Problem Solving**: Enhanced performance on mathematical reasoning tasks (76.8% MATH, 13.3% AIME25)
+- ** Scientific Analysis**: Exceptional scientific reasoning capabilities (45.96% GPQA Diamond)
+- ** Chain-of-Thought Reasoning**: Superior step-by-step thinking with detailed reasoning chains and self-verification
+- ** Structured Analysis**: Improved at breaking down complex problems into manageable components
+- ** Multi-Method Verification**: Uses multiple approaches to validate results and ensure accuracy
+- ** Vision Understanding**: Ability to analyze and reason about images, charts, diagrams, and visual data
+- ** Multimodal Reasoning**: Combining visual and textual information for comprehensive analysis
 
 ## Model Details
 
@@ -77,11 +59,20 @@ The model demonstrates enhanced capabilities in:
 ### Training Data
 The model was fine-tuned on three carefully selected, high-quality datasets that form the foundation of its exceptional reasoning capabilities:
 
-- **🧠 OpenO1-SFT**: Advanced supervised fine-tuning dataset containing high-quality reasoning demonstrations with explicit thought processes, enabling systematic problem breakdown and transparent reasoning chains
+####  **OpenO1-SFT Dataset**
+- **Purpose**: Supervised fine-tuning for advanced reasoning patterns
+- **Content**: High-quality reasoning demonstrations with explicit thought processes
+- **Impact**: Enables the model to break down complex problems systematically and show transparent reasoning chains
 
-- **💭 Open-Thoughts**: Specialized dataset focused on step-by-step thinking processes, featuring detailed internal monologues and reasoning progressions that teach the model to externalize and structure its thinking
+####  **Open-Thoughts Dataset** 
+- **Purpose**: Step-by-step thinking process modeling
+- **Content**: Detailed internal monologues and reasoning progressions for various problem types
+- **Impact**: Teaches the model to externalize its thinking process, making reasoning transparent and verifiable
 
-- **🔢 OpenR1-Math**: Comprehensive mathematical reasoning dataset with detailed solution methodologies, significantly enhancing performance from basic arithmetic to advanced competition-level mathematical problems
+####  **OpenR1-Math Dataset**
+- **Purpose**: Mathematical reasoning and problem-solving specialization  
+- **Content**: Comprehensive mathematical problems with detailed solution methodologies
+- **Impact**: Significantly enhances performance on mathematical reasoning tasks, from basic arithmetic to advanced competition-level problems
 
 This synergistic combination creates a model that excels not only at providing accurate answers but also at demonstrating clear, verifiable reasoning processes.
 
@@ -156,20 +147,20 @@ The loss curve demonstrates stable convergence with the final training loss reac
 
 | **Benchmark** | **Metric** | **Base Gemma-3-27B-IT** | **LogicFlow-Gemma-3-27b-thinking** | **Improvement** |
 |---------------|------------|--------------------------|-------------------------------------|-----------------|
-| **📊 Mathematical Reasoning** |
+| ** Mathematical Reasoning** |
 | GSM8K | Exact Match | 82.6% | **89.5%** | **+6.9%** |
 | MATH | Accuracy | 50.0% | **76.8%** | **+26.8%** |
-| **💻 Code Generation** |
+| ** Code Generation** |
 | MBPP | pass@1 | 65.6% | **69.0%** | **+3.4%** |
 | HumanEval | 0-shot | 48.8% | *Pending* | *TBD* |
-| **🎯 Instruction Following** |
+| ** Instruction Following** |
 | IFEval | Prompt-level | *45.0%* | **40.0%** | **-5.0%** |
 | IFEval | Instruction-level | *58.0%* | **53.1%** | **-4.9%** |
-| **🏆 Advanced Mathematics** |
+| ** Advanced Mathematics** |
 | AIME25 | Problem Solving | ~8-12% | **13.3%** | **+1-5%** |
-| **🔬 Scientific Reasoning** |
+| ** Scientific Reasoning** |
 | GPQA Diamond | Science QA | ~30-35% | **45.96%** | **+11-16%** |
-| **🧠 Knowledge & Understanding** |
+| ** Knowledge & Understanding** |
 | MMLU | Overall Accuracy | 78.6% | **75.3%** | **-3.3%** |
 | MMLU STEM | Sciences & Math | ~70.0% | **71.6%** | **+1.6%** |
 | MMLU Humanities | Arts & Literature | ~67.0% | **69.2%** | **+2.2%** |
@@ -178,7 +169,7 @@ The loss curve demonstrates stable convergence with the final training loss reac
 
 ### Key Performance Insights
 
-#### ✅ **Significant Improvements**
+####  **Significant Improvements**
 - **Mathematical Reasoning**: Exceptional improvements - GSM8K (+6.9%) and MATH (+26.8%) demonstrate enhanced step-by-step problem solving
 - **Advanced Mathematics**: Massive 26.8% improvement on MATH benchmark showcases superior mathematical reasoning capabilities
 - **Scientific Reasoning**: Outstanding 45.96% accuracy on GPQA Diamond - significantly above typical model performance (30-35%)
@@ -186,12 +177,12 @@ The loss curve demonstrates stable convergence with the final training loss reac
 - **Code Generation**: 3.4% improvement on MBPP shows better programming logic understanding
 - **Domain-Specific Knowledge**: Improvements in STEM (+1.6%), Humanities (+2.2%), and Social Sciences (+2.3%)
 
-#### ⚠️ **Trade-offs Observed**
+####  **Trade-offs Observed**
 - **Instruction Following**: Slight decrease in IFEval scores (-5% prompt-level, -4.9% instruction-level)
 - **General Knowledge**: Overall MMLU score decreased by 3.3% due to reasoning specialization
 - **Reasoning Focus**: Model optimized for deep analytical thinking over rapid instruction compliance
 
-#### 🎯 **Specialized Capabilities**
+####  **Specialized Capabilities**
 - **Mathematical Excellence**: Outstanding 76.8% accuracy on MATH benchmark - among the top performances for 27B models
 - **Scientific Reasoning**: Exceptional 45.96% on GPQA Diamond - handling graduate-level physics, chemistry, and biology problems
 - **Elite Competition Performance**: Competitive 13.3% on AIME25 - tackling American Invitational Mathematics Exam challenges
@@ -430,10 +421,10 @@ The model showcases systematic thinking through:
 - Clear documentation of the reasoning process
 
 These examples demonstrate the model's ability to:
-- **🔍 Break down complex problems** into manageable steps
-- **✅ Self-verify results** using multiple approaches  
-- **📝 Document reasoning chains** for transparency
-- **🎯 Maintain accuracy** while showing work
+- ** Break down complex problems** into manageable steps
+- ** Self-verify results** using multiple approaches  
+- ** Document reasoning chains** for transparency
+- ** Maintain accuracy** while showing work
 
 ### Activating Chain-of-Thought Reasoning
 
@@ -472,26 +463,26 @@ Show your reasoning process before giving the final answer."""
 
 This multimodal model is particularly well-suited for:
 
-### 📚 Educational Applications
+###  Educational Applications
 - **Chain-of-Thought Tutoring**: Demonstrates complete problem-solving processes with transparent reasoning steps
 - **Mathematical Education**: Shows multiple verification methods for mathematical concepts (as seen in 9.11 vs 9.9 example)
 - **Critical Thinking Development**: Models systematic analysis and self-verification techniques
 - **Visual Learning**: Analyzing educational diagrams, charts, and mathematical illustrations
 - **Interactive Learning**: Combining text and visual elements for comprehensive understanding
 
-### 🔢 Mathematical & Scientific Analysis
+###  Mathematical & Scientific Analysis
 - **Chart Analysis**: Interpreting graphs, statistical charts, and data visualizations
 - **Geometric Problem Solving**: Analyzing geometric figures and spatial relationships
 - **Scientific Diagram Understanding**: Processing scientific illustrations and technical drawings
 - **Formula Recognition**: Understanding mathematical formulas in images
 
-### 💼 Professional Applications
+###  Professional Applications
 - **Document Analysis**: Processing documents containing both text and visual elements
 - **Technical Documentation**: Understanding technical manuals with diagrams
 - **Data Visualization**: Analyzing and explaining complex charts and infographics
 - **Research Assistance**: Combining textual research with visual data analysis
 
-### 🧠 Advanced Reasoning Tasks
+###  Advanced Reasoning Tasks
 - **Chain-of-Thought Problem Solving**: Complex reasoning with detailed step-by-step analysis and self-verification
 - **Multi-Method Validation**: Using multiple approaches to verify answers (numerical comparison, pattern analysis, etc.)
 - **Transparent Decision Making**: Showing complete reasoning chains for critical analysis tasks