Update README.md

README.md

@@ -17,21 +17,20 @@ metrics:
 - loss
 ---
 
-# Better SQL Agent - Llama 3.1 8B 🚀
+# Better SQL Agent - Llama 3.1 8B
 
-##  Training Results
+## Training Results
 - **Training Samples**: 19,480 (SQL analytics + technical conversations)
 - **Hardware**: NVIDIA 4x A10G GPU (96GB VRAM)
 
-## 📋 Model Description
+## Model Description
 This is a high-performance fine-tuned version of **Meta-Llama-3.1-8B-Instruct**, specifically optimized for:
-- 🔍 **SQL query generation and optimization**
-- 📊 **Data analysis and insights**  
-- 💬 **Technical assistance and debugging**
-- 🛠️ **Tool-based workflows**
+- **SQL query generation and optimization**
+- **Data analysis and insights**  
+- **Technical assistance and debugging**
+- **Tool-based workflows**
 
-
-## 🔧 Training Configuration
+## Training Configuration
 - **Base Model**: `meta-llama/Llama-3.1-8B-Instruct`
 - **Training Method**: LoRA (Low-Rank Adaptation)
   - Rank: 16, Alpha: 32, Dropout: 0.05
@@ -39,7 +38,7 @@ This is a high-performance fine-tuned version of **Meta-Llama-3.1-8B-Instruct**,
 - **Context Length**: 128K tokens (extended from base)
 - **Optimizer**: AdamW with cosine scheduling
 
-## 🚀 Quick Start
+## Quick Start
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
@@ -78,7 +77,7 @@ response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:],
 print(response)
 ```
 
-## 📊 Performance Metrics
+## Performance Metrics
 | Metric | Value |
 |--------|-------|
 | **Starting Loss** | 1.53 |
@@ -86,35 +85,34 @@ print(response)
 | **Loss Reduction** | **96.7%** |
 | **Training Time** | 8.9 hours |
 
-## 🎯 Use Cases
+## Use Cases
 - **SQL Generation**: Create complex queries from natural language
 - **Data Analysis**: Generate insights and analytical queries
 - **Code Assistance**: Debug and optimize SQL code
 - **Technical Support**: Answer database and analytics questions
 - **Learning Aid**: Explain SQL concepts and best practices
 
-## 🔍 Training Data
+## Training Data
 The model was trained on a curated dataset of **19,480 high-quality examples** including:
 - SQL query generation tasks
 - Data analysis conversations
 - Technical problem-solving dialogues
 - Tool usage patterns and workflows
 
-## ⚡ Optimization Features
-- **Unsloth Integration**: 2x faster training and inference
+## Optimization Features
 - **4-bit Quantization**: Reduced memory footprint
 - **Flash Attention**: Optimized attention mechanism
 - **Mixed Precision**: BF16 training for efficiency
 
-## 📄 License
+## License
 This model inherits the **Llama 3.1 license** from the base model. Please review the [official license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE) for usage terms.
 
-## 🙏 Acknowledgments
+## Acknowledgments
 - Based on Meta's Llama 3.1 8B Instruct model
 
-## 📈 Model Card Contact
+## Model Card Contact
 For questions about this model, please open an issue in the repository or contact the model author.
 
 ---
 
-**🌟 Achieved 96.7% loss reduction - A testament to high-quality training data and optimization!**
+**Achieved 96.7% loss reduction - A testament to high-quality training data and optimization!**