--- 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 ---
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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 | 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 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 ```python { "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 ```python 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. - **Email:** hello@vantaresearch.xyz ## Citation If you use this model in your research or applications, please cite: ```bibtex @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*