language:
- en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
base_model_relation: finetune
tags:
- code
- coding
- programming
- algorithms
- systems-programming
- code-generation
- complexity-analysis
- qwen2.5
- fine-tuned
- vanta-research
- vanta-research-entities
- vanta-research-code-models
- wraith
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
library_name: transformers
VANTA Research
Independent AI safety research lab specializing in cognitive fit, alignment, and human-AI collaboration
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
| 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)
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
Proudly developed in Portland, Oregon by VANTA Research