docs/EVALUATION_FRAMEWORK.md

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)

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

# 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

# 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

# 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

# .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

   # 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

   # Reduce batch sizes in config
   evaluation:
     model_settings:
       device_map: "cpu"  # Use CPU instead of GPU
   

3. Dataset Access

   # 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:

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:

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:

  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.

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Note: This framework is designed for systematic model evaluation and should be integrated into continuous development workflows for best results.