language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- code
- coding
- programming
- algorithms
- systems-programming
- code-generation
- complexity-analysis
- qwen2.5
- fine-tuned
model-index:
- name: wraith-coder-7b
results:
- task:
type: text-generation
name: Code Generation
metrics:
- type: conciseness
value: 62.6
name: Response Reduction
- type: coverage
value: 60
name: Complexity Analysis Coverage
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 (4,256 examples)
- Identity formation and communication style
- Logical reasoning patterns
- Technical terminology usage
- Foundation for signal-dense communication
Iteration 2: Coding Restoration (5,500 examples)
- 2,040 conversational coding examples
- 2,040 computer science fundamentals
- 920 mathematical reasoning problems
- 200 identity reinforcement examples
- 300 technical communication patterns
Iteration 3: Advanced Capabilities (4,488 examples)
- 1,007 architectural design patterns
- 1,041 algorithm design and analysis
- 1,064 debugging techniques
- 1,026 systems programming concepts
- 150 identity anchors
- 200 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
| Category | Conciseness Gain | Key Strength |
|---|---|---|
| Data Structures | 80-90% | Space complexity analysis |
| Algorithms | 75-85% | Time complexity trade-offs |
| Systems Design | 70-80% | Scalability considerations |
| Concurrency | 65-75% | Synchronization patterns |
| Architecture | 50-60% | Design pattern selection |
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
Condensed Communication Style
- Assumes reader familiarity with computer science fundamentals
- May omit explanatory context that beginners require
- Prioritizes technical precision over accessibility
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
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)
Model Card Authors
Vanta Research
Model Card 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.
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