Duplicate from WithinUsAI/Genesis_AI_Code_100k

Co-authored-by: Guy DuGan II <gss1147@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mds filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+# Video files - compressed
+*.mp4 filter=lfs diff=lfs merge=lfs -text
+*.webm filter=lfs diff=lfs merge=lfs -text

CHANGELOG.md

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+# Changelog
+
+## v1.0.0 (2026-01-01)
+- Initial public release (Within Us AI)

CITATION.cff

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+cff-version: 1.2.0
+title: "Genesis AI Code 100K (Frontier)"
+message: "If you use this dataset, please cite it."
+type: dataset
+authors:
+  - name: "Within Us AI"
+version: 1.0.0
+date-released: 2026-01-01
+license: CC0-1.0

README.md

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+---
+pretty_name: Genesis AI Code 100K (Frontier)
+tags:
+- within-us-ai
+- genesis
+- code
+- agentic
+- reasoning
+- tool-calling
+- evaluation
+- security
+- governance
+- moe
+task_categories:
+- text-generation
+- question-answering
+language:
+- en
+license: cc0-1.0
+size_categories:
+- 100K<n<1M
+---
+
+# Genesis AI Code 100K (Frontier)
+
+**Developed by: Within Us AI**
+
+Frontier dataset with tool-call traces, self-grading, budgets, and audit orientation.
+
+## Splits
+- train: 98,000
+- validation: 2,000
+
+## Highlights
+- Tests-as-truth supervision patterns
+- Diff-first patching
+- Agentic loops (plan→edit→test→reflect) with bounded budgets
+- Tool-call trace supervision (where present)
+- Governance/audit & policy-gate awareness
+
+## Storage format
+Parquet unavailable (No module named 'pyarrow'); JSONL shards in `/data`.
+
+## Quick start
+```python
+from datasets import load_dataset
+ds = load_dataset("<YOUR_ORG_OR_USER>/Genesis AI Code 100K (Frontier)", split="train")
+print(ds[0])
+```

WHITEPAPER_POINTER.md

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+Within Us AI
+
+Genesis Whitepaper is provided in the demo pack as `GENESIS_WHITEPAPER.md`.

bench/README.md

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+# Genesis AI Code Bench
+**Developed by: Within Us AI**  
+Generated: 2026-01-01
+
+A lightweight evaluation harness for Genesis-style datasets that focuses on the signals
+developers care about in practice:
+
+- **Structure validity** (JSON parsing, required fields, schema consistency)
+- **Tool-trace validity** (JSON array of tool calls with `tool` + `args`)
+- **Diff validity** (`patch_diff` blocks contain recognizable unified-diff markers)
+- **Self-grade validity** (score bounds, confidence bounds, presence of notes)
+- **Governance presence** (audit/tests flags when expected)
+- **Economics presence** (cost budgets + latency targets)
+
+This bench is intentionally fast and offline-friendly. It does not execute repo tests; it
+scores dataset quality and readiness for downstream training workflows.
+
+## Quick start
+```bash
+python bench.py --jsonl path/to/train.jsonl --max_rows 5000
+```
+
+## Metrics produced
+- `format_valid_rate`
+- `required_fields_rate`
+- `tool_trace_valid_rate`
+- `patch_diff_valid_rate`
+- `self_grade_valid_rate`
+- `governance_present_rate`
+- `economics_present_rate`
+- `uniqueness_rate` (hash-based)
+
+## Recommended use
+- Run before upload to ensure Viewer-ready consistency
+- Run after merges to confirm schema stability
+- Compare v1.0 vs v1.1 addon impact

bench/bench.py

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+\
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+from typing import Any, Dict, Iterable, Tuple
+
+REQUIRED_FIELDS = {"id","created","topic","task_type","difficulty","instruction","input","output","metadata","hash"}
+
+def iter_jsonl(path: Path, max_rows: int | None) -> Iterable[Dict[str, Any]]:
+    n = 0
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                obj = json.loads(line)
+            except Exception:
+                yield {"__parse_error__": True}
+                n += 1
+                if max_rows and n >= max_rows:
+                    return
+                continue
+            yield obj
+            n += 1
+            if max_rows and n >= max_rows:
+                return
+
+def is_tool_trace_valid(out: Any) -> bool:
+    # Accept either a parsed list OR a JSON string representing a list
+    if isinstance(out, str):
+        s = out.strip()
+        if not s:
+            return False
+        try:
+            out = json.loads(s)
+        except Exception:
+            return False
+    if not isinstance(out, list) or len(out) == 0:
+        return False
+    for step in out:
+        if not isinstance(step, dict):
+            return False
+        if "tool" not in step or "args" not in step:
+            return False
+        if not isinstance(step["tool"], str) or not isinstance(step["args"], dict):
+            return False
+    return True
+
+def is_patch_diff_valid(out: Any) -> bool:
+    if not isinstance(out, str):
+        return False
+    s = out.strip()
+    if "```diff" in s:
+        # fenced diff
+        return ("-" in s and "+" in s)
+    # also allow plain diff-like strings
+    return s.startswith("-") or s.startswith("diff") or ("- " in s and "+ " in s)
+
+def is_self_grade_valid(out: Any) -> bool:
+    if isinstance(out, str):
+        try:
+            out = json.loads(out)
+        except Exception:
+            return False
+    if not isinstance(out, dict):
+        return False
+    score = out.get("score")
+    conf = out.get("confidence")
+    notes = out.get("notes")
+    if not (isinstance(score, int) and 0 <= score <= 10):
+        return False
+    if not (isinstance(conf, (int, float)) and 0.0 <= float(conf) <= 1.0):
+        return False
+    if not (isinstance(notes, str) and len(notes) >= 3):
+        return False
+    return True
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="Genesis AI Code Bench (Within Us AI)")
+    ap.add_argument("--jsonl", required=True, help="Path to JSONL file")
+    ap.add_argument("--max_rows", type=int, default=5000)
+    args = ap.parse_args()
+
+    path = Path(args.jsonl)
+    total = 0
+    format_valid = 0
+    required_ok = 0
+    tool_ok = tool_total = 0
+    diff_ok = diff_total = 0
+    grade_ok = grade_total = 0
+    gov_ok = 0
+    econ_ok = 0
+    hashes = set()
+    hash_total = 0
+
+    for obj in iter_jsonl(path, args.max_rows):
+        total += 1
+        if obj.get("__parse_error__"):
+            continue
+        format_valid += 1
+
+        keys = set(obj.keys())
+        if REQUIRED_FIELDS.issubset(keys):
+            required_ok += 1
+
+        # hash / uniqueness
+        h = obj.get("hash")
+        if isinstance(h, str) and h:
+            hash_total += 1
+            hashes.add(h)
+
+        # governance + economics (metadata)
+        md = obj.get("metadata", {})
+        if isinstance(md, dict):
+            if md.get("audit_required") is True:
+                gov_ok += 1
+            if isinstance(md.get("cost_budget"), int) and isinstance(md.get("latency_ms_target"), int):
+                econ_ok += 1
+
+        # task-specific validity
+        task = obj.get("task_type")
+        out = obj.get("output")
+        if task == "tool_trace":
+            tool_total += 1
+            if is_tool_trace_valid(out):
+                tool_ok += 1
+        elif task == "patch_diff":
+            diff_total += 1
+            if is_patch_diff_valid(out):
+                diff_ok += 1
+        elif task == "self_grade":
+            grade_total += 1
+            if is_self_grade_valid(out):
+                grade_ok += 1
+
+    def rate(num: int, den: int) -> float:
+        return 0.0 if den == 0 else round(num / den, 4)
+
+    report = {
+        "rows_scanned": total,
+        "format_valid_rate": rate(format_valid, total),
+        "required_fields_rate": rate(required_ok, total),
+        "tool_trace_valid_rate": rate(tool_ok, tool_total),
+        "patch_diff_valid_rate": rate(diff_ok, diff_total),
+        "self_grade_valid_rate": rate(grade_ok, grade_total),
+        "governance_present_rate": rate(gov_ok, total),
+        "economics_present_rate": rate(econ_ok, total),
+        "uniqueness_rate": rate(len(hashes), hash_total),
+    }
+
+    print(json.dumps(report, indent=2))
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/PARQUET_README.md

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+# Genesis Parquet Pack
+**Developed by: Within Us AI**  
+Generated: 2026-01-01
+
+This pack includes:
+- A converter script to turn JSONL splits into **Parquet shards** suitable for fast scanning/streaming.
+- Recommended sharding conventions (train-00000.parquet, etc.).
+
+Why Parquet:
+- Faster scans
+- Better compatibility with dataset viewers and downstream pipelines
+- Easy sharding and parallel reads
+
+## Usage
+```bash
+pip install datasets pyarrow pandas
+python jsonl_to_parquet_shards.py --jsonl train.jsonl --out_dir data --rows_per_shard 5000
+```

scripts/hf_upload.py

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+#!/usr/bin/env python3
+"""Within Us AI: Upload helper for Genesis repos.
+
+Requires:
+  pip install datasets huggingface_hub pyarrow pandas
+
+Usage:
+  huggingface-cli login
+  python scripts/hf_upload.py --repo_id YOURNAME/REPO --data_dir data
+"""
+from __future__ import annotations
+import argparse
+from pathlib import Path
+from datasets import load_dataset
+
+def main() -> int:
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--repo_id", required=True)
+    ap.add_argument("--data_dir", default="data")
+    ap.add_argument("--private", action="store_true")
+    args = ap.parse_args()
+
+    d = Path(args.data_dir)
+    # Prefer parquet if present
+    parquet_train = str(d / "train-*.parquet")
+    parquet_val = str(d / "validation-*.parquet")
+    ds = load_dataset("parquet", data_files={"train": parquet_train, "validation": parquet_val})
+    if len(ds["train"]) == 0:
+        ds = load_dataset("json", data_files={"train": str(d/"train-*.jsonl"), "validation": str(d/"validation-*.jsonl")})
+    ds.push_to_hub(args.repo_id, private=args.private)
+    print(f"Uploaded to https://huggingface.co/datasets/{args.repo_id}")
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/jsonl_to_parquet_shards.py

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+\
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+
+import pandas as pd
+
+def iter_jsonl(path: Path):
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            if not line:
+                continue
+            yield json.loads(line)
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="JSONL → Parquet shards (Within Us AI)")
+    ap.add_argument("--jsonl", required=True)
+    ap.add_argument("--out_dir", required=True)
+    ap.add_argument("--rows_per_shard", type=int, default=5000)
+    args = ap.parse_args()
+
+    src = Path(args.jsonl)
+    out_dir = Path(args.out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    buf = []
+    shard = 0
+    for obj in iter_jsonl(src):
+        buf.append(obj)
+        if len(buf) >= args.rows_per_shard:
+            df = pd.DataFrame(buf)
+            out = out_dir / f"{src.stem}-{shard:05d}.parquet"
+            df.to_parquet(out, index=False)
+            buf.clear()
+            shard += 1
+
+    if buf:
+        df = pd.DataFrame(buf)
+        out = out_dir / f"{src.stem}-{shard:05d}.parquet"
+        df.to_parquet(out, index=False)
+
+    print(f"Wrote shards to: {out_dir}")
+    return 0
+
+if __name__ == "__main__":
+    raise SystemExit(main())