Feature Extraction
Transformers
Safetensors
qwen3_5
matilda
jev
fp4
quantized
maincode
8-bit precision
Instructions to use Maincode/Matilda-Jev-v1-FP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Maincode/Matilda-Jev-v1-FP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Maincode/Matilda-Jev-v1-FP4")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Maincode/Matilda-Jev-v1-FP4") model = AutoModel.from_pretrained("Maincode/Matilda-Jev-v1-FP4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 827 Bytes
c69aaec | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | """Append real experiment events; one JSON object per line."""
import fcntl
import json
import os
import time
from datetime import datetime, timezone
from pathlib import Path
START = time.monotonic()
def record(kind: str, **fields: object) -> dict[str, object]:
event: dict[str, object] = {
"timestamp": datetime.now(timezone.utc).isoformat(),
"process_elapsed_seconds": time.monotonic() - START,
"kind": kind,
**fields,
}
path = Path(os.getenv("KEV_EVENTS", "runs/events.jsonl"))
path.parent.mkdir(parents=True, exist_ok=True)
with path.open("a") as stream:
fcntl.flock(stream.fileno(), fcntl.LOCK_EX)
stream.write(json.dumps(event, ensure_ascii=False) + "\n")
stream.flush()
fcntl.flock(stream.fileno(), fcntl.LOCK_UN)
return event
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