Add comprehensive model card with usage instructions and evaluation results

README.md

@@ -38,9 +38,9 @@ This LoRA adapter enhances meta-llama/Llama-3.2-1B-Instruct with tool calling ca
 - **LoRA Rank**: 64
 - **LoRA Alpha**: 128
 - **Training Samples**: 1000
-- **Sequence Success Rate**: 100.0%
-- **Tool Call Accuracy**: 100.0%
-- **Average Tools per Sequence**: 3.0
+- **Sequence Success Rate**: 80.0%
+- **Tool Call Accuracy**: 80.0%
+- **Average Tools per Sequence**: 4.0
 ## 🔧 Usage
 
 ```python
@@ -141,16 +141,17 @@ The model is trained to use these development tools:
 ## 🔬 Evaluation
 
 The adapter was evaluated on diverse coding scenarios:
-- Sequence success rate: 100.0%
-- Tool call accuracy: 100.0%
-- Average tools per sequence: 3.0
-- Successful executions: 5/5
+- Sequence success rate: 80.0%
+- Tool call accuracy: 80.0%
+- Average tools per sequence: 4.0
+- Successful executions: 4/5
 
 ## 🏷️ Tool Usage Distribution
 
 Most frequently used tools during evaluation:
+- **get_file_info**: 5 uses
 - **list_directory**: 5 uses
-- **read_file**: 5 uses
+- **read_multiple_files**: 5 uses
 - **search_files**: 5 uses
 
 ## 🏷️ Related