Add complete implementation documentation

docs/EVALUATION_FRAMEWORK.md

@@ -0,0 +1,316 @@
+# Sheikh-2.5-Coder Evaluation Framework
+
+## Overview
+
+This comprehensive evaluation framework provides systematic testing and benchmarking for the Sheikh-2.5-Coder model across multiple dimensions including code generation quality, performance, web development capabilities, and regression detection.
+
+## Components
+
+### 1. Main Evaluation Orchestrator (`evaluate_model.py`)
+- **Purpose**: Coordinates all evaluation benchmarks and generates comprehensive reports
+- **Features**: 
+  - Integrates all evaluation components
+  - Creates HTML dashboards and visualizations
+  - Generates detailed markdown reports
+  - Manages target achievement tracking
+
+### 2. Benchmark Evaluations
+
+#### MMLU Code Evaluation (`mmlu_evaluation.py`)
+- **Target**: >60% accuracy on MMLU Code subset
+- **Dataset**: `lukaemon/mmlu` with code subset
+- **Metrics**: Accuracy, response time, confusion analysis
+- **Features**:
+  - Multiple choice question answering
+  - Programming concept understanding
+  - Categorized performance analysis
+
+#### HumanEval Coding Tasks (`humaneval_evaluation.py`)
+- **Target**: >40% Pass@1
+- **Dataset**: OpenAI HumanEval
+- **Metrics**: Pass@1, Pass@k, function correctness, syntax validity
+- **Features**:
+  - Multi-completion generation for Pass@k calculation
+  - Automated function testing
+  - Code syntax validation
+
+#### Web Development Tests (`web_dev_tests.py`)
+- **Target**: 75% quality score across web technologies
+- **Coverage**: JavaScript/TypeScript, React, XML, MDX, CSS
+- **Features**:
+  - Language-specific quality assessment
+  - Best practices compliance checking
+  - Component pattern recognition
+
+### 3. Performance Benchmarking (`performance_benchmark.py`)
+- **Metrics**: Inference speed, memory usage, context scaling, multi-threading
+- **Features**:
+  - Hardware utilization monitoring
+  - Batch size optimization testing
+  - Memory profiling across quantization levels
+  - Context length scalability analysis
+
+### 4. Code Quality Assessment (`code_quality_tests.py`)
+- **Targets**: >95% syntax validity, >0.65 CodeBLEU score
+- **Features**:
+  - Multi-language syntax validation
+  - Code complexity analysis
+  - Best practices compliance
+  - CodeBLEU score calculation
+
+### 5. Regression Testing (`regression_testing.py`)
+- **Purpose**: Detect performance regressions against baselines
+- **Features**:
+  - Statistical significance testing
+  - Multi-baseline comparison
+  - Automated regression reporting
+  - Performance degradation detection
+
+## Configuration
+
+### Evaluation Configuration (`evaluation_config.yaml`)
+```yaml
+evaluation:
+  model_settings:
+    device: "auto"
+    dtype: "float16"
+    max_new_tokens: 512
+    temperature: 0.7
+    
+  targets:
+    mmlu_code_accuracy: 0.60
+    humaneval_pass1: 0.40
+    codebleu_score: 0.65
+    syntax_validity: 0.95
+    web_dev_quality: 0.75
+```
+
+## Usage
+
+### Quick Start
+```bash
+# Run comprehensive evaluation
+python scripts/evaluate_model.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./evaluation_results \
+    --run_id eval_$(date +%Y%m%d_%H%M%S)
+```
+
+### Individual Benchmark Runs
+```bash
+# MMLU Code evaluation
+python scripts/mmlu_evaluation.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/mmlu \
+    --run_id mmlu_eval
+
+# HumanEval evaluation
+python scripts/humaneval_evaluation.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/humaneval \
+    --run_id humaneval_eval
+
+# Web development tests
+python scripts/web_dev_tests.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/webdev \
+    --run_id webdev_eval
+
+# Performance benchmarking
+python scripts/performance_benchmark.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/performance \
+    --run_id perf_eval
+
+# Code quality tests
+python scripts/code_quality_tests.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/quality \
+    --run_id quality_eval
+
+# Regression testing
+python scripts/regression_testing.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./results/regression \
+    --run_id regression_eval
+```
+
+### Advanced Configuration
+```bash
+# Custom targets and settings
+python scripts/evaluate_model.py \
+    --model_path /path/to/model \
+    --config scripts/evaluation_config.yaml \
+    --output_path ./evaluation_results \
+    --run_id custom_eval \
+    --skip_load  # Dry run without model loading
+```
+
+## Output Files
+
+### Generated Reports
+- `comprehensive_report_{run_id}.md` - Main evaluation report
+- `evaluation_results_{run_id}.json` - Detailed JSON results
+- `evaluation_summary_{run_id}.csv` - CSV summary
+- `performance_metrics_{run_id}.json` - Performance metrics
+
+### Individual Benchmark Outputs
+Each benchmark generates:
+- `{benchmark}_results_{run_id}.json` - Detailed results
+- `{benchmark}_detailed_{run_id}.csv` - Sample-level data
+- `{benchmark}_{run_id}.log` - Execution logs
+
+## Target Achievement
+
+The framework tracks the following performance targets:
+
+| Benchmark | Target | Metric |
+|-----------|--------|--------|
+| MMLU Code | >60% | Accuracy |
+| HumanEval | >40% | Pass@1 |
+| Web Development | >75% | Quality Score |
+| Code Quality | >95% | Syntax Validity |
+| Code Quality | >0.65 | CodeBLEU Score |
+
+## Performance Expectations
+
+### Inference Speed
+- **Excellent**: >50 tokens/second
+- **Good**: 30-50 tokens/second  
+- **Acceptable**: 20-30 tokens/second
+- **Poor**: <20 tokens/second
+
+### Memory Usage
+- **Efficient**: <8GB model size
+- **Standard**: 8-12GB model size
+- **Large**: 12-20GB model size
+
+## Integration
+
+### Continuous Integration
+```yaml
+# .github/workflows/evaluation.yml
+name: Model Evaluation
+on: [push, pull_request]
+
+jobs:
+  evaluate:
+    runs-on: [self-hosted, gpu]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Run Evaluation
+        run: |
+          python scripts/evaluate_model.py \
+            --model_path ${{ github.workspace }} \
+            --config scripts/evaluation_config.yaml \
+            --output_path ./results \
+            --run_id ci_${{ github.sha }}
+```
+
+### Automated Reporting
+The framework integrates with:
+- **HuggingFace Evaluate Library**: Standard metrics
+- **MLflow**: Experiment tracking
+- **Weights & Biases**: Visualization dashboards
+- **GitHub Actions**: CI/CD integration
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Model Loading Failures**
+   ```bash
+   # Check model path and permissions
+   ls -la /path/to/model
+   # Verify CUDA availability
+   python -c "import torch; print(torch.cuda.is_available())"
+   ```
+
+2. **Memory Issues**
+   ```yaml
+   # Reduce batch sizes in config
+   evaluation:
+     model_settings:
+       device_map: "cpu"  # Use CPU instead of GPU
+   ```
+
+3. **Dataset Access**
+   ```bash
+   # Login to HuggingFace
+   huggingface-cli login
+   # Or disable remote code loading
+   ```
+
+### Performance Optimization
+
+1. **GPU Memory Optimization**
+   - Use `device_map="auto"` for automatic placement
+   - Enable gradient checkpointing for memory efficiency
+   - Use quantization (int8, int4) for larger models
+
+2. **Speed Optimization**
+   - Increase batch sizes for throughput
+   - Use faster attention implementations
+   - Enable TensorRT optimization
+
+## Customization
+
+### Adding New Benchmarks
+1. Create new evaluation script following existing patterns
+2. Add to `evaluate_model.py` orchestrator
+3. Update `evaluation_config.yaml` with new settings
+4. Implement result saving and target tracking
+
+### Modifying Targets
+Edit `evaluation_config.yaml`:
+```yaml
+targets:
+  mmlu_code_accuracy: 0.65  # Increased target
+  humaneval_pass1: 0.45     # Increased target
+  custom_metric: 0.80       # New metric
+```
+
+### Custom Quality Metrics
+Extend existing evaluation classes:
+```python
+def evaluate_custom_metric(self, code_samples):
+    # Implement custom quality assessment
+    return custom_score
+```
+
+## Support
+
+### Logging and Debugging
+- All scripts generate detailed logs in output directories
+- Enable debug mode in configuration:
+  ```yaml
+  logging:
+    level: "DEBUG"
+    debug_mode: true
+  ```
+
+### Resource Requirements
+- **Minimum**: 8GB RAM, 1 GPU (4GB VRAM)
+- **Recommended**: 16GB RAM, 1 GPU (8GB VRAM)
+- **Optimal**: 32GB RAM, 2+ GPUs (16GB+ VRAM each)
+
+### Best Practices
+1. **Baseline Comparisons**: Always maintain baseline results for regression detection
+2. **Incremental Testing**: Run individual benchmarks during development
+3. **Regular Evaluation**: Schedule periodic comprehensive evaluations
+4. **Result Archiving**: Save evaluation results for historical analysis
+
+## License
+
+This evaluation framework is part of the Sheikh-2.5-Coder project. See the main repository for license information.
+
+---
+
+**Note**: This framework is designed for systematic model evaluation and should be integrated into continuous development workflows for best results.