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README.md

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 ---
-license: apache-2.0
+language:
+- en
+license: other
+license_name: within-us-ai-custom-dataset-license
+pretty_name: GOD_Coder_Complete_DataSet
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- question-answering
+- text-classification
+tags:
+- code
+- coding
+- software-engineering
+- instruction-tuning
+- sft
+- ai-coding
+- complete-project-coding
+- repository-patching
+- debugging
+- dependency-resolution
+- full-stack-engineering
+- code-review
+- dataset
+annotations_creators:
+- machine-generated
+- expert-generated
+language_creators:
+- machine-generated
+multilinguality:
+- monolingual
+source_datasets:
+- original
+viewer: false
 ---
+
+# GOD_Coder_Complete_DataSet
+
+## Subtitle
+A large-scale complete-project coding dataset by **gss1147 / WithIn Us AI**, built to train language models into stronger professional software-engineering assistants.
+
+## Dataset Summary
+
+**GOD_Coder_Complete_DataSet** is a large synthetic supervised fine-tuning dataset designed to help turn a general language model into a **professional complete-project AI coder**.
+
+The dataset focuses on teaching models how to:
+- diagnose realistic repository issues
+- patch broken code with production-ready fixes
+- write and repair tests
+- handle dependency and migration failures
+- reason across full software stacks
+- solve advanced coding-logic problems
+- behave more like a senior engineer on complete software projects
+
+This dataset was created by **gss1147** under **WithIn Us AI**.
+
+## Creator
+
+- **Creator:** gss1147
+- **Organization / Brand:** WithIn Us AI
+- **Dataset Concept, Design, Structure, and Packaging:** WithIn Us AI
+- **Primary Author:** gss1147
+
+## License
+
+This dataset uses the **WithIn Us AI Custom Dataset License**.
+
+## Dataset Purpose
+
+The purpose of this dataset is to provide a strong supervised fine-tuning resource for training coding-capable LLMs toward:
+
+- complete software-project reasoning
+- professional engineering behavior
+- multi-file patch generation
+- debugging and issue resolution
+- test-backed implementation quality
+- dependency-aware coding
+- rollout-safe software delivery
+- increasingly advanced coding logic
+
+This dataset is intended for researchers, model builders, and fine-tuning practitioners who want a model that behaves more like a **real software engineer**, not just a code autocompleter.
+
+## Supported Tasks
+
+This dataset is suitable for:
+- supervised fine-tuning
+- instruction tuning
+- coding assistant specialization
+- software-engineering behavior shaping
+- repository issue repair
+- debugging assistance
+- dependency resolution training
+- software delivery planning
+- code review improvement
+- complete-project coding workflows
+
+## Dataset Structure
+
+The dataset is organized into **7 major subject groups**, each containing **25,000 examples**, for a total of **175,000 rows**.
+
+### Subject Groups
+1. **AI Coding**
+2. **AI Dependency Coding**
+3. **AI Coding Stacks**
+4. **AI Software Development**
+5. **AI Coding Logic Master**
+6. **AI Coding Logic Legendary**
+7. **AI Coding Logic God**
+
+### Total Size
+- **Total examples:** 175,000
+- **Train examples:** 171,500
+- **Validation examples:** 3,500
+
+## Data Format
+
+Each example is stored in **chat-format JSONL** and includes:
+
+- `id`
+- `subject`
+- `subject_title`
+- `tier`
+- `language`
+- `framework`
+- `stack`
+- `domain`
+- `topic`
+- `task_type`
+- `split`
+- `freshness_bucket`
+- `source_grounding`
+- `messages`
+- `artifacts`
+- `labels`
+
+### Example Schema
+
+```json
+{
+  "id": "ai_coding-00001-abcdef1234567890",
+  "subject": "ai_coding",
+  "subject_title": "AI Coding",
+  "tier": "hard",
+  "language": "Python",
+  "framework": "FastAPI",
+  "stack": ["FastAPI", "PostgreSQL", "Redis", "Celery", "pytest", "Docker"],
+  "domain": "auth service",
+  "topic": "JWT refresh token rotation",
+  "task_type": "repo_issue_patch",
+  "split": "train",
+  "freshness_bucket": "synthetic_transformed_post_2025_style",
+  "source_grounding": {
+    "kind": "synthetic_transformed_repo_task",
+    "license_ok": true,
+    "provenance_note": "Synthetic training example designed for coding-model SFT and labeled as synthetic."
+  },
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a production-grade software engineer. Return a correct, secure, complete, test-backed solution with concise reasoning and no placeholders."
+    },
+    {
+      "role": "user",
+      "content": "Repository domain: auth service..."
+    },
+    {
+      "role": "assistant",
+      "content": "Diagnosis... implementation... tests... verification..."
+    }
+  ],
+  "artifacts": {
+    "verification_commands": ["pytest -q", "ruff check ."],
+    "requires_tests": true,
+    "format": "chat_sft"
+  },
+  "labels": {
+    "correctness": 1,
+    "security": 1,
+    "production_ready": 1,
+    "test_quality": 1,
+    "complete_project_focus": 1
+  }
+}
+
+Languages Covered
+
+The dataset includes tasks across multiple coding and infrastructure languages, including:
+	•	Python
+	•	TypeScript
+	•	JavaScript
+	•	Go
+	•	Rust
+	•	Java
+	•	C#
+	•	C++
+	•	SQL
+	•	Bash
+	•	YAML
+
+Content Overview
+
+The dataset emphasizes production-style software engineering. It includes examples involving:
+	•	bug fixing
+	•	feature implementation
+	•	code review correction
+	•	API design
+	•	dependency resolution
+	•	version migration repair
+	•	lockfile and reproducibility debugging
+	•	full-stack issue handling
+	•	rollout-safe software delivery
+	•	incident remediation
+	•	concurrency and logic debugging
+	•	performance bottleneck repair
+	•	multi-file patching
+	•	security hardening
+	•	observability-aware engineering
+
+Data Generation Method
+
+This dataset was created as a synthetic structured coding dataset for fine-tuning and instruction-tuning purposes.
+
+The generation process focused on:
+	•	professional software-engineering style prompts
+	•	complete implementation responses
+	•	test-backed solutions
+	•	production-oriented reasoning
+	•	multi-stack coverage
+	•	advanced logic difficulty bands
+	•	complete-project engineering behavior
+
+Examples were designed to reflect realistic repository and engineering scenarios while remaining clearly labeled as synthetic.
+
+Why This Dataset Exists
+
+Many coding datasets over-focus on:
+	•	short single-function code tasks
+	•	toy algorithm problems
+	•	incomplete snippets
+	•	beginner-level instruction pairs
+
+GOD_Coder_Complete_DataSet was created to push beyond that by training models on:
+	•	complete-project coding behavior
+	•	software-engineering decision quality
+	•	professional debugging patterns
+	•	multi-layer issue resolution
+	•	deployment-safe thinking
+	•	engineering-grade patch quality
+
+Intended Use
+
+This dataset is intended for:
+	•	full-model fine-tuning
+	•	instruction tuning
+	•	coding model specialization
+	•	research into software-engineering-capable LLMs
+	•	training models that can operate more effectively in repository-style workflows
+
+It is especially relevant for users building:
+	•	coding copilots
+	•	patch-generation systems
+	•	engineering support agents
+	•	code-review assistants
+	•	debugging assistants
+	•	full-stack project agents
+
+Recommended Training Uses
+
+Recommended uses include:
+	•	supervised fine-tuning on chat-formatted LLMs
+	•	continued instruction tuning for coding behavior
+	•	staged curriculum learning across difficulty tiers
+	•	subject-wise training by shard
+	•	multi-phase training where foundational coding precedes advanced logic tiers
+
+Suggested Progression
+	1.	AI Coding
+	2.	AI Dependency Coding
+	3.	AI Coding Stacks
+	4.	AI Software Development
+	5.	AI Coding Logic Master
+	6.	AI Coding Logic Legendary
+	7.	AI Coding Logic God
+
+
+Source Data
+	•	Source Type: Original dataset created by WithIn Us AI
+	•	Primary Creator: gss1147
+	•	Dataset Design: WithIn Us AI
+	•	Origin: Synthetic and structured software-engineering task generation
+
+Data Splits
+	•	Train: 171,500
+	•	Validation: 3,500
+
+The split is tracked using the split field inside each example.
+
+Dataset Strengths
+	•	large-scale
+	•	complete-project focus
+	•	professional engineering framing
+	•	multi-language coverage
+	•	test-backed outputs
+	•	multi-subject structure
+	•	strong software-development emphasis
+	•	suited for coding-model specialization
+	•	useful for curriculum-based fine-tuning
+
+Dataset Limitations
+	•	synthetic rather than extracted from real private repositories
+	•	does not guarantee novelty against all historic model pretraining corpora
+	•	should be combined with careful evaluation
+	•	should ideally be paired with held-out benchmark testing
+	•	should not be treated as a substitute for licensed real-world patch datasets where available
+
+Bias, Risks, and Safety
+
+
+Quality Philosophy
+
+The dataset was designed around these principles:
+	•	no placeholders
+	•	complete answers
+	•	production-ready orientation
+	•	secure-by-default thinking
+	•	tests included as a training signal
+	•	full-project engineering mindset
+	•	patch and verification awareness
+
+Citation
+
+BibTeX
+
+@dataset{gss1147_god_coder_complete_dataset_2026,
+  author       = {gss1147 and WithIn Us AI},
+  title        = {GOD_Coder_Complete_DataSet},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  note         = {Synthetic supervised fine-tuning dataset for professional complete-project AI coding}
+}
+
+Acknowledgment
+
+GOD_Coder_Complete_DataSet was created by gss1147 under WithIn Us AI as part of a broader effort to build stronger open coding-focused AI systems with professional software-engineering behavior.
+
+Here is the only YAML fix that mattered:
+
+```yaml
+license: other
+license_name: within-us-ai-custom-dataset-license

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