vanta-research / wraith-coder-7b

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Wraith Coder 7B

Wraith Coder 7B is a specialized code generation model fine-tuned from Qwen2.5-Coder-7B-Instruct. Through iterative training focused on algorithmic reasoning, systems programming, and technical communication optimization, Wraith achieves superior information density while maintaining implementation correctness.

Model Description

Developed by: VANTA Research
Base Model: Qwen/Qwen2.5-Coder-7B-Instruct
Model Type: Causal Language Model
Language(s): English
License: Apache 2.0
Fine-tuned from: Qwen2.5-Coder-7B-Instruct

Model Architecture

• Parameters: 7.6 billion
• Architecture: Transformer decoder with 28 layers
• Hidden Size: 3584
• Attention Heads: 28 (4 key-value heads)
• Context Length: 32,768 tokens
• Vocabulary Size: 152,064 tokens

Training Methodology

Iterative Fine-Tuning Strategy

Wraith Coder 7B was developed through three iterations of progressive capability enhancement:

Iteration 1: Personality Establishment (~4,250 examples)

• Same personality examples used on Wraith 8B from the VANTA Research Entity Series
• Identity formation and communication style
• Logical reasoning patterns
• Technical terminology usage
• Foundation for signal-dense communication

Iteration 2: Coding Restoration/Enhancement (~5,500 examples)

• Conversational coding examples
• Computer science fundamentals
• Mathematical reasoning problems
• Identity reinforcement examples
• Technical communication patterns

Iteration 3: Advanced Capabilities (~4,450 examples)

• Architectural design patterns
• Algorithm design and analysis
• Debugging techniques
• Systems programming concepts
• Identity anchors
• Communication pattern reinforcement

Training Configuration

• Method: Low-Rank Adaptation (LoRA)
• Rank: 16
• Alpha: 32
• Dropout: 0.05
• Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
• Learning Rate: 5e-5
• Batch Size: 8 (effective)
• Epochs: 2 per iteration
• Optimizer: AdamW 8-bit
• Training Framework: Unsloth

Performance Evaluation

Comprehensive 20-Question Coding Assessment

A rigorous evaluation across diverse programming challenges demonstrates measurable improvements over the base model:

Response Efficiency

• Base Model: 57,999 characters average (2,900 per question)
• Wraith Coder: 21,686 characters average (1,084 per question)
• Improvement: 62.6% reduction in response length while maintaining correctness

Technical Analysis Coverage

• Base Model: Complexity analysis in 40% of responses
• Wraith Coder: Complexity analysis in 60% of responses
• Improvement: 50% increase in Big-O notation coverage

Question-Specific Performance

Comparative Analysis

Test Case: LRU Cache Implementation

• Base Model: 120+ lines with verbose documentation
• Wraith Coder: 45 lines with design rationale
• Result: Equivalent correctness, 62% shorter, includes algorithmic justification

Test Case: Rate Limiter Design

• Base Model: 100+ lines, conceptual confusion between algorithms
• Wraith Coder: 25 lines, correct token bucket implementation with edge case analysis
• Result: Superior correctness and clarity

Test Case: Binary Tree Serialization

• Base Model: Single approach with lengthy explanation
• Wraith Coder: Two approaches (DFS and BFS) with trade-off comparison
• Result: Multiple solutions with selection guidance

Intended Use

Primary Applications

Senior Software Engineering

• Code review and optimization suggestions
• Algorithm selection and complexity analysis
• Systems design pattern recommendations
• Performance optimization strategies

Technical Interview Preparation

• Concise algorithmic explanations
• Multiple solution approaches
• Time and space complexity analysis
• Trade-off articulation

Production Development

• Efficient technical documentation
• Design decision rationale
• Scalability considerations
• Edge case identification

Out-of-Scope Use

This model is optimized for experienced developers who value information density. It may not be suitable for:

• Beginner programming education requiring verbose step-by-step explanations
• Non-technical audiences requiring extensive context
• Applications requiring social conversational patterns
• Domains outside software engineering and computer science

Limitations and Considerations

Technical Limitations

1. Condensed Communication Style

   • Assumes reader familiarity with computer science fundamentals
   • May omit explanatory context that beginners require
   • Prioritizes technical precision over accessibility

2. Model Size Constraints

   • 7B parameter model has inherent knowledge limitations
   • May not match larger models on extremely complex problems
   • Context window limits for very large codebases

3. Domain Specialization

   • Optimized for algorithmic and systems programming
   • May have reduced performance on domain-specific applications (e.g., embedded systems, game engines)
   • Training data focused on general-purpose programming

Deployment Considerations

• Compute Requirements: Minimum 8GB VRAM for 4-bit quantization
• Inference Speed: Similar to base Qwen2.5-Coder-7B
• Quantization: Tested with 4-bit (Q4_K_M) quantization maintaining quality

Ethical Considerations

Training Data

All training data was synthetically generated or derived from publicly available educational resources. No proprietary code or copyrighted material was used in fine-tuning.

Bias and Fairness

The model inherits biases present in the base Qwen2.5-Coder-7B model. Additional fine-tuning focused on technical capabilities and communication style rather than bias mitigation.

Responsible Use

Users should:

• Validate all generated code before production deployment
• Apply appropriate code review processes
• Consider model outputs as suggestions requiring human verification
• Ensure compliance with relevant licensing for generated code

Technical Details

Chat Template

The model uses the Qwen ChatML format:

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{assistant_message}<|im_end|>

Recommended Inference Parameters

{
  "temperature": 0.7,
  "top_p": 0.9,
  "top_k": 40,
  "repeat_penalty": 1.1,
  "max_tokens": 2048
}

Quantization Support

Tested and validated quantization formats:

• FP16: Full precision baseline
• Q8_0: Minimal quality loss
• Q4_K_M: Recommended balance (4.4GB)
• Q4_0: Maximum compression

Usage Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "vanta-research/wraith-coder-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Implement quicksort with complexity analysis."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Contact

For questions or issues regarding this model, please open an issue in the model repository.

Citation

If you use this model in your research or applications, please cite:

@misc{wraith-coder-7b,
  author = {VANTA Research},
  title = {Wraith Coder 7B: Signal-Dense Code Generation through Iterative Fine-Tuning},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/vanta-research/wraith-coder-7b}}
}

Acknowledgments

This model builds upon Qwen2.5-Coder-7B-Instruct developed by Alibaba Cloud. We acknowledge their contribution to open-source language model research. Thanks to Unsloth for providing an easy-to-use training framework.

Version History

• v1.0.0 (2025-11-19): Initial release with iteration 3 training complete
  • 62.6% response reduction while maintaining correctness
  • 60% complexity analysis coverage across 20-question benchmark
  • Production-ready for senior engineering applications

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Proudly developed in Portland, Oregon by VANTA Research