Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/matilda-jev-fp4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/matilda-jev-fp4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/matilda-jev-fp4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/matilda-jev-fp4") model = AutoModel.from_pretrained("Maincode/matilda-jev-fp4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download kev/data.py from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/data.py
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/kev/data.py
-
curl -L -o data.py https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/data.py
4.98 kB
| """Build and validate decision JSONL folds. | |
| `kev-data build RECIPE OUTPUT` builds frozen folds from the archived sources | |
| (see kev.build and configs/data/*.toml). | |
| `kev-data validate train.jsonl dev.jsonl ...` checks every row against the | |
| Example schema, summarizes suites/question types, and rejects ID or | |
| (dataset, family) overlap between the given files. | |
| """ | |
| import argparse | |
| import json | |
| import math | |
| import os | |
| from collections import Counter | |
| from pathlib import Path | |
| from kev.evaluate import options, read_rows | |
| from kev.model import BASE_MODEL, MAX_OPTIONS | |
| from kev.types import Example | |
| def validate_row(row: Example) -> list[str]: | |
| errors: list[str] = [] | |
| for key in ("id", "suite", "family"): | |
| if not isinstance(row.get(key), str) or not row.get(key): | |
| errors.append(f"missing {key}") | |
| if not isinstance(row.get("source"), dict): | |
| errors.append("missing source") | |
| question = row.get("question") | |
| if not isinstance(question, dict) or question.get("type") not in ("choice", "noul", "score"): | |
| return errors + ["question.type must be choice, noul or score"] | |
| if question["type"] == "choice" and (not isinstance(question.get("criteria"), dict) or not question["criteria"]): | |
| return errors + ["choice questions need a nonempty criteria object"] | |
| if question["type"] == "score" and (not isinstance(question.get("criteria"), list) or len(question["criteria"]) < 2): | |
| return errors + ["score questions need at least two criteria levels"] | |
| labels = options(question) | |
| if len(labels) > MAX_OPTIONS: | |
| errors.append(f"more than {MAX_OPTIONS} options") | |
| target, label = row.get("target"), row.get("label") | |
| if isinstance(target, list): | |
| if len(target) != len(labels) or any(not isinstance(p, (int, float)) or not math.isfinite(p) or p < 0 for p in target) \ | |
| or abs(sum(target) - 1) > 1e-6: | |
| errors.append("soft target must be a distribution over the options") | |
| elif question["type"] == "noul": | |
| if isinstance(target, bool): | |
| pass | |
| elif not isinstance(target, (int, float)) or not 0 <= float(target) <= 1: | |
| errors.append("noul target must be a bool or probability") | |
| elif not any(type(target) is type(value) and target == value for value in labels): | |
| errors.append("target is not an option") | |
| if not any(type(label) is type(value) and label == value for value in labels): | |
| errors.append("hard label is not an option") | |
| return errors | |
| def validate(paths: list[Path]) -> bool: | |
| from kev.train import check_partitions | |
| ok = True | |
| partitions: dict[str, list[Example]] = {} | |
| for path in paths: | |
| rows = read_rows(path) | |
| partitions[str(path)] = rows | |
| failures = Counter[str]() | |
| for row in rows: | |
| for error in validate_row(row): | |
| failures[error] += 1 | |
| summary = { | |
| "rows": len(rows), | |
| "suites": dict(Counter(row.get("suite") for row in rows).most_common(15)), | |
| "types": dict(Counter(row["question"]["type"] for row in rows if isinstance(row.get("question"), dict))), | |
| "images": sum(bool(row.get("images")) for row in rows), | |
| "errors": dict(failures), | |
| } | |
| print(json.dumps({str(path): summary}, indent=2, ensure_ascii=False)) | |
| ok = ok and not failures | |
| if len(partitions) > 1: | |
| try: | |
| check_partitions(partitions) | |
| print("No ID or family overlap between files.") | |
| except ValueError as error: | |
| print(f"Overlap: {error}") | |
| ok = False | |
| return ok | |
| def main() -> None: | |
| parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) | |
| commands = parser.add_subparsers(dest="command", required=True) | |
| check = commands.add_parser("validate", help="Schema, summary and overlap checks for JSONL folds") | |
| check.add_argument("paths", type=Path, nargs="+") | |
| make = commands.add_parser("build", help="Build frozen folds from a recipe TOML") | |
| make.add_argument("recipe", type=Path) | |
| make.add_argument("output", type=Path, help="New directory for the build") | |
| make.add_argument("--base-model", default=BASE_MODEL, help="Tokenizer used to count prompt tokens") | |
| make.add_argument("--workers", type=int, default=min(16, os.cpu_count() or 1)) | |
| make.add_argument("--limit", type=int, help="Read at most this many questions per file (quick pipeline check)") | |
| args = parser.parse_args() | |
| if args.command == "validate" and not validate(args.paths): | |
| raise SystemExit(1) | |
| if args.command == "build": | |
| from kev.build import build | |
| manifest = build(args.recipe, args.output, args.base_model, args.workers, args.limit) | |
| print(json.dumps({"summary": manifest["summary"], "parts": manifest["parts"]}, indent=2, ensure_ascii=False)) | |
| print(f"Wrote {args.output}") | |
| if __name__ == "__main__": | |
| main() | |