TRAINING.md

Training Details

Iterative Fine-Tuning Methodology

Wraith Coder 7B was developed through three successive training iterations, each building upon the previous version with progressively advanced capabilities.

Iteration 1: Foundation (4,256 examples)

Objective: Establish core personality and communication patterns

Dataset Composition:

• 1,213 identity formation examples
• 1,650 logical reasoning patterns
• 1,043 amplified logical analysis
• 350 technical communication patterns

Training Configuration:

• Base Model: Qwen/Qwen2.5-Coder-7B-Instruct
• Method: LoRA (r=16, alpha=32, dropout=0.05)
• Epochs: 2
• Batch Size: 8 (effective)
• Learning Rate: 5e-5
• Duration: ~2 hours on RTX 3060

Outcomes:

• Successfully established third-person communication style
• Strong pattern recognition language
• Foundation for signal-dense responses
• Coding capability degradation observed (addressed in iteration 2)

Iteration 2: Coding Restoration (5,500 examples)

Objective: Restore code generation while maintaining personality

Dataset Composition:

• 2,040 conversational coding examples
• 2,040 computer science fundamentals
• 920 algebraic reasoning problems
• 200 identity reinforcement examples
• 300 communication pattern anchors

Training Configuration:

• Base Model: wraith-iteration-1-merged
• Method: LoRA (r=16, alpha=32, dropout=0.05)
• Epochs: 2
• Batch Size: 8 (effective)
• Learning Rate: 5e-5
• Duration: ~3 hours on RTX 3060

Outcomes:

• 100% code generation restoration
• Maintained personality characteristics
• Enhanced conciseness (50-70% shorter responses)
• Improved signal-to-noise ratio

Iteration 3: Advanced Capabilities (4,488 examples)

Objective: Add systems programming and advanced algorithmic knowledge

Dataset Composition:

• 1,007 architectural design patterns
• 1,041 algorithm design and optimization
• 1,064 debugging techniques and strategies
• 1,026 systems programming concepts
• 150 identity anchor examples
• 200 communication pattern reinforcement

Training Configuration:

• Base Model: wraith-iteration-2-merged
• Method: LoRA (r=16, alpha=32, dropout=0.05)
• Epochs: 2
• Batch Size: 8 (effective)
• Learning Rate: 5e-5
• Duration: ~3 hours on RTX 3060

Outcomes:

• Enhanced complexity analysis (40% to 60% coverage)
• Multiple solution approaches (35% to 65% frequency)
• Trade-off articulation (45% to 75% depth)
• Systems programming knowledge integration
• Maintained 62.6% conciseness improvement

Hardware Requirements

Training:

• GPU: NVIDIA RTX 3060 (12GB VRAM) or equivalent
• RAM: 32GB recommended
• Storage: 50GB for model weights and checkpoints

Inference:

• GPU: 8GB VRAM minimum (with 4-bit quantization)
• RAM: 16GB recommended
• Storage: 5GB for quantized model

Training Framework

• Primary: Unsloth (optimized for LoRA fine-tuning)
• Backend: PyTorch 2.8.0 with CUDA 12.8
• Precision: Mixed precision (BF16)
• Gradient Checkpointing: Enabled for memory efficiency

Reproducibility

All training scripts, datasets, and evaluation benchmarks are available in the associated repository. Training can be reproduced with:

# Iteration 1
python train_wraith_iteration1.py

# Merge iteration 1
python merge_wraith_iteration1.py

# Iteration 2
python train_wraith_iteration2.py

# Merge iteration 2
python merge_wraith_iteration2.py

# Iteration 3
python train_wraith_iteration3.py

# Final merge
python merge_wraith_iteration3.py

Evaluation Methodology

20-Question Comprehensive Benchmark

Question Categories:

• Data structures (tries, BSTs, stacks, caches)
• Algorithms (sorting, searching, graph algorithms)
• Systems design (distributed caches, file systems, rate limiters)
• Concurrency (threading, synchronization, producer-consumer)
• Architecture (recommendation systems, URL shorteners)

Evaluation Metrics:

• Response length (characters and lines)
• Complexity analysis coverage (Big-O notation presence)
• Multiple solution approaches
• Trade-off discussion depth
• Implementation correctness

Comparison Baseline:

• Qwen/Qwen2.5-Coder-7B-Instruct (base model)
• Identical prompts and inference parameters
• Blind evaluation of response quality

Statistical Significance

• Sample Size: 20 diverse coding challenges
• Consistency: All 20 questions showed improvement
• Average Improvement: 60.2% conciseness gain
• Standard Deviation: 21.3% (questions 4% to 90% improvement)
• Confidence Level: 95%

Limitations and Future Work

Current Limitations:

• Optimized for experienced developers; may lack context for beginners
• 7B parameter size limits extremely complex problem-solving
• Training focused on general-purpose programming
• English language only

Potential Future Enhancements:

• Multi-language support
• Domain-specific iterations (embedded, ML, web)
• Larger parameter variants (14B, 32B)
• Instruction-following refinement
• Tool use integration