diff --git a/test_report.md b/test_report.md
deleted file mode 100644
index c77e673d..00000000
--- a/test_report.md
+++ /dev/null
@@ -1,79 +0,0 @@
-# My Project Model Performance Report
-
-## Executive Summary
-
-This report presents comprehensive performance benchmarking results for My Project Model using MLPerf-inspired methodology. The evaluation covers three standard scenarios: single-stream (latency), server (throughput), and offline (batch processing).
-
-### Key Findings
-- **Single Stream**: 95.00 samples/sec, 10.03ms mean latency, 11.58ms 90th percentile
-- **Server**: 87.00 samples/sec, 12.30ms mean latency, 18.20ms 90th percentile
-- **Offline**: 120.00 samples/sec, 7.77ms mean latency, 7.75ms 90th percentile
-
-## Methodology
-
-### Benchmark Framework
-- **Architecture**: MLPerf-inspired four-component system
-- **Scenarios**: Single-stream, server, and offline evaluation
-- **Statistical Validation**: Multiple runs with confidence intervals
-- **Metrics**: Latency distribution, throughput, accuracy
-
-### Test Environment
-- **Hardware**: Standard development machine
-- **Software**: TinyTorch framework
-- **Dataset**: Standardized evaluation dataset
-- **Validation**: Statistical significance testing
-
-## Detailed Results
-
-### Single Stream Scenario
-
-- **Sample Count**: 100
-- **Mean Latency**: 10.03 ms
-- **Median Latency**: 9.91 ms
-- **90th Percentile**: 11.58 ms
-- **95th Percentile**: 9.75 ms
-- **Standard Deviation**: 2.09 ms
-- **Throughput**: 95.00 samples/second
-- **Accuracy**: 0.9420
-
-### Server Scenario
-
-- **Sample Count**: 150
-- **Mean Latency**: 12.30 ms
-- **Median Latency**: 12.49 ms
-- **90th Percentile**: 18.20 ms
-- **95th Percentile**: 14.18 ms
-- **Standard Deviation**: 3.13 ms
-- **Throughput**: 87.00 samples/second
-- **Accuracy**: 0.9380
-
-### Offline Scenario
-
-- **Sample Count**: 50
-- **Mean Latency**: 7.77 ms
-- **Median Latency**: 7.70 ms
-- **90th Percentile**: 7.75 ms
-- **95th Percentile**: 9.10 ms
-- **Standard Deviation**: 1.10 ms
-- **Throughput**: 120.00 samples/second
-- **Accuracy**: 0.9450
-
-## Statistical Validation
-
-All results include proper statistical validation:
-- Multiple independent runs for reliability
-- Confidence intervals for key metrics
-- Outlier detection and handling
-- Significance testing for comparisons
-
-## Recommendations
-
-Based on the benchmark results:
-1. **Performance Characteristics**: Model shows consistent performance across scenarios
-2. **Optimization Opportunities**: Focus on reducing tail latency for production deployment
-3. **Scalability**: Server scenario results indicate good potential for production scaling
-4. **Further Testing**: Consider testing with larger datasets and different hardware configurations
-
-## Conclusion
-
-This comprehensive benchmarking demonstrates {model_name}'s performance characteristics using industry-standard methodology. The results provide a solid foundation for production deployment decisions and further optimization efforts.