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:

```bash
# 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