| --- |
| language: |
| - code |
| - en |
| license: |
| - mit |
| - apache-2.0 |
| - bsd |
| tags: |
| - code |
| - pretraining |
| - code-generation |
| - instruct |
| - luck-spark |
| - moe |
| - Github |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - text-generation |
| pretty_name: Luck Spark 1B - High Quality Code Dataset |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| dataset_info: |
| features: |
| - name: text |
| dtype: string |
| - name: repo |
| dtype: string |
| - name: path |
| dtype: string |
| - name: language |
| dtype: string |
| - name: hash |
| dtype: string |
| - name: score |
| dtype: float64 |
| - name: stars |
| dtype: int64 |
| splits: |
| - name: train |
| num_bytes: 282833845 |
| num_examples: 47982 |
| download_size: 112741739 |
| dataset_size: 282833845 |
| --- |
| |
| # Luck Spark 1B - High Quality Code Dataset |
|
|
| **The first quality-scored, star-agnostic code dataset for training 1B MoE code models.** |
|
|
| Unlike The Stack / CodeParrot that filter by stars, this dataset scores every file **by its content (0-10)**. A 2-star well-documented library scores higher than a 10k-star minified file. Continuously updated by an autonomous bot. |
|
|
| **Repo:** `ahmetggg/luck-spark-1b-code-dataset` | **Bot:** `github_to_hf_bot.py` | **License:** Permissive only (MIT / Apache-2.0 / BSD / Unlicense) |
|
|
| ## Why This Dataset is Different? |
|
|
| | Feature | This Dataset | Others (Stack, etc.) | |
| |---------|--------------|----------------------| |
| | **Filter** | Content Quality Score 0-10 | Stars > 100 | |
| | **Low-star gems** | ✅ Kept if quality 7+ | ❌ Discarded | |
| | **Quality transparency** | `score` column for every file | No score | |
| | **Dedup** | SHA256 + diversity check | Basic | |
| | **Execution check** | AST parse + structure | None | |
| | **Live** | Bot updates daily | Static dump | |
|
|
| **Quality Score (0-10) breakdown:** |
| - `+3` AST parse + has function/class + docstring |
| - `+2` Comment ratio 5-40% (documented, not spam) |
| - `+1` Ideal size 500-20k chars |
| - `+1` Diversity (unique lines >60%) |
| - `+1` Weak star bonus `log10(stars+1)*0.5` (max 1 point) |
| - `- fail` minified, auto-generated, binary, 0/50 diversity |
|
|
| - `score <5` → discarded (trash) |
| - `score 5-7` → kept locally, not pushed (medium) |
| - `score 7+` → **pushed to HF** (high quality only) |
|
|
| You can see the exact scorer: `quality_score()` in `github_to_hf_bot.py:26` |
|
|
| ## Dataset Structure |
|
|
| ```python |
| { |
| "text": "import math\nclass Calculator:\n ...", # raw code |
| "repo": "ahmetggg/example-repo", # source repo |
| "path": "src/calc.py", # file path |
| "language": ".py", # .py/.js/.rs/.go/.java/.cpp/.ts |
| "hash": "a1b2c3d4e5f6g7h8", # SHA256 dedup |
| "score": 7.4, # 0-10 quality |
| "stars": 12 # repo stars at scrape time |
| } |
| ``` |
|
|
| **Languages:** Python, JavaScript, Rust, Go, Java, C++, TypeScript (balanced, no star bias) |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load high-quality only (7+ already filtered) |
| ds = load_dataset("ahmetggg/luck-spark-1b-code-dataset") |
| print(ds) |
| # DatasetDict({ train: Dataset({ num_rows: 1000+, features: [...] }) }) |
| |
| # Filter even stricter (e.g., 8+) |
| high = ds["train"].filter(lambda x: x["score"] >= 8) |
| print(f"Elite: {len(high)} files") |
| |
| # Language split |
| py = ds["train"].filter(lambda x: x["language"] == ".py") |
| |
| # For pretraining (raw text) |
| from transformers import AutoTokenizer |
| tok = AutoTokenizer.from_pretrained("ahmetggg/luck-spark-1b") |
| texts = ds["train"]["text"] |
| ``` |
|
|
| **For Luck Spark 1B training:** |
| ```bash |
| # Pretrain: use raw text |
| # Instruct: use text + auto-generated instruction (coming soon) |
| # RL: execution-verified subset (score 8+) |
| ``` |
|
|
| ## Stats (Live) |
|
|
| - **Total repos scanned:** 616+ (7 languages × 3 pages, growing) |
| - **Files kept:** ~60% (0/50 for trash repos, 34/50 for gems) |
| - **Avg score:** 6.2 - 7.6 (pushed avg >7.0) |
| - **Dedup:** SHA256, ~5% duplicates removed |
| - **Licenses:** MIT / Apache-2.0 / BSD / Unlicense only (commercial safe) |
|
|
| *Updated continuously. Last bot run: see commit history.* |
|
|
| ## Collection Method |
|
|
| 1. GitHub Search API: `language:python license:mit` (no star filter, `sort:updated`) |
| 2. Tree API: max 50 files / repo, `<500KB`, allowed extensions |
| 3. Raw download + `quality_score()` -> keep 5+, push 7+ |
| 4. Arrow/Parquet -> `push_to_hub` every 1000 files |
|
|
| No manual curation. Fully autonomous, reproducible. |
|
|
| ## Limitations & Ethics |
|
|
| - Only permissive licenses. No GPL/copyleft. Check `repo` field before commercial use. |
| - Code may contain biases from GitHub. Filter `score` for your use-case. |
| - No PII scrubbing beyond GitHub public data. Report issues via Discussions. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @dataset{luck_spark_1b_2026, |
| title={Luck Spark 1B High Quality Code Dataset}, |
| author={ahmetggg}, |
| year={2026}, |
| publisher={Hugging Face}, |
| url={https://huggingface.co/datasets/ahmetggg/luck-spark-1b-code-dataset} |
| } |
| ``` |
|
|
| ## Roadmap |
|
|
| - [x] Quality-scored v1 (7+ push) |
| - [ ] Execution-verified subset (`python -m py_compile` + tests) |
| - [ ] Instruction pairs (`explain this code` / `complete this function`) |
| - [ ] 100B tokens target for 1B MoE pretraining |
|
|
| Built for **Luck Spark 1B (Mamba + MoE, Executor + Architect)** - open source, HF first. |
|
|
| *Questions? Open a Discussion on HF or check `github_to_hf_bot.py` for the exact logic.* |
