Create README.md


README.md

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+---
+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 Banner](royal_ghost_coder_10m_banner.png)
+
+# 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
+
+- **Synthetic content:** Examples are machine-generated (or machine-curated) and may include unrealistic domains, naming, or pseudo-code.
+- **Verification:** The dataset is **not** 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
+
+![1bd3b27a-02ec-428d-a9de-ed72441ad936](https://cdn-uploads.huggingface.co/production/uploads/6758f77450b6c087c2c281e1/4QBZvscn2HjAM0q9CkJPM.png)
+
+yal_Ghost_Coder_10M}}
+}
+```