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/events.py from Maincode/matilda-jev-fp4: direct link, hf CLI and curl.
- Browser
- Download file 827 Bytes
-
https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/events.py
- Command line
-
hf download hf://Maincode/matilda-jev-fp4/kev/events.py
-
curl -L -o events.py https://huggingface.co/Maincode/matilda-jev-fp4/resolve/main/kev/events.py
827 Bytes
| """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 | |