| --- |
| pretty_name: Royal Ghost Coder 10M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: "royal_ghost_titan_data.jsonl" |
| tags: |
| - code |
| - instruction-tuning |
| - synthetic |
| - agentic |
| task_categories: |
| - text-generation |
| language: |
| - en |
| size_categories: |
| - 10M<n<100M |
| --- |
| |
|  |
|
|
| # Royal Ghost Coder 10M |
|
|
| A large-scale, **synthetic instruction-tuning** corpus designed to train code-capable, agentic models on **structured “instruction → input → output”** workflows at high volume. The dataset ships as a single JSONL file and is auto-converted to Parquet by Hugging Face for faster streaming. |
|
|
| ## Dataset Summary |
|
|
| - **Repository:** `gss1147/Royal_Ghost_Coder_10M` |
| - **Rows:** 10,000,000 (train split) |
| - **Primary file:** `royal_ghost_titan_data.jsonl` |
| - **Format:** JSON Lines (one JSON object per line) |
| - **Schema:** `id, idx, role, instruction, input, output, score` |
|
|
| ## Supported Tasks |
|
|
| - Instruction tuning for code generation / refactoring / debugging patterns |
| - Lightweight agent-style planning and “tool-like” action phrasing |
| - Dataset-driven evaluation and filtering via the `score` field |
|
|
| ## Data Structure |
|
|
| Each record is a single training example in a common instruction-tuning format. |
|
|
| ### Fields |
|
|
| - `id` (string): UUID-style identifier |
| - `idx` (int): Row index |
| - `role` (string): Persona / role label (e.g., an agent identity) |
| - `instruction` (string): The task request (prompt) |
| - `input` (string): Optional context / constraints / scenario text |
| - `output` (string): The intended completion (often code or code-like text) |
| - `score` (float): A normalized quality indicator in `[0, 1]` (useful for filtering) |
|
|
| ### Example (conceptual) |
|
|
| ```json |
| { |
| "id": "6da52f71-a953-4675-862f-2cd8539b55f1", |
| "idx": 0, |
| "role": "titan_architect", |
| "instruction": "Optimize the Quantum_Bridge for singular perfection.", |
| "input": "Legacy sector 20 unstable.", |
| "output": "def Optimize_Quantum_Bridge_0(self): return self.evolve(entropy=0.2674)", |
| "score": 0.788814 |
| } |
| ``` |
|
|
| ## How to Use |
|
|
| ### Loading with 🤗 Datasets |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("gss1147/Royal_Ghost_Coder_10M", split="train") |
| print(ds[0]) |
| ``` |
|
|
| ### Converting to chat format (optional) |
|
|
| ```python |
| def to_messages(ex): |
| user = ex["instruction"] |
| if ex.get("input"): |
| user = f"{user}\n\nContext:\n{ex['input']}" |
| return { |
| "messages": [ |
| {"role": "system", "content": f"You are {ex.get('role', 'an expert coding assistant')}."}, |
| {"role": "user", "content": user}, |
| {"role": "assistant", "content": ex["output"]}, |
| ], |
| "score": ex.get("score", None), |
| "id": ex.get("id", None), |
| } |
| |
| chat_ds = ds.map(to_messages, remove_columns=ds.column_names) |
| ``` |
|
|
| ### Quality filtering |
|
|
| ```python |
| filtered = ds.filter(lambda x: x["score"] is None or x["score"] >= 0.85) |
| ``` |
|
|
| ## Intended Use |
|
|
| This dataset is primarily intended for: |
|
|
| - Training or adapting small-to-mid size models for instruction-following code generation. |
| - Building “persona + instruction” pipelines where `role` steers responses. |
| - Large-scale experiments on filtering, curricula, or “quality-aware” fine-tuning via `score`. |
|
|
| ## Limitations and Considerations |
|
|
| - * |
| - **Verification:** The dataset is a source of verified real-world facts. Treat outputs as training text, not ground truth. |
| - **Safety:** If you deploy a model fine-tuned on this dataset, apply standard safety, security, and evaluation practices. |
|
|
| ## License |
|
|
| No explicit license is declared in this dataset card. Before broad redistribution or commercial use, add a license in the YAML front matter (for example: `apache-2.0`, `mit`, or `cc-by-4.0`) consistent with your intended permissions. |
|
|
| ## Citation |
|
|
| If you use this dataset in academic work, cite the repository: |
|
|
| ```bibtex |
| @dataset{gss1147_royal_ghost_coder_10m, |
| title = {Royal Ghost Coder 10M}, |
| author = {gss1147}, |
| year = {2026}, |
| publisher = {Hugging Face}, |
| howpublished = {\url{https://huggingface.co/datasets/gss1147/Ro |
| |
|  |
| |
| yal_Ghost_Coder_10M}} |
| } |
| |
| |
