Instructions to use Meanblock/JEV-CPU with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Meanblock/JEV-CPU with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="Meanblock/JEV-CPU")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,929 Bytes
7845694 | 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 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | """Download redistributable source snapshots used by the evaluation manifests."""
from __future__ import annotations
import argparse
import hashlib
from pathlib import Path
import urllib.request
SOURCES = {
"wanli-test.jsonl": (
"https://huggingface.co/datasets/alisawuffles/WANLI/resolve/"
"61c95318fd71c55b6ba355d76253254615f387ec/test.jsonl",
"4276e0af7fcdf657d1ab7beb54eaf025fda592a76c9ee86b63b7871953fc74fd",
),
"every-experiments.json": (
"https://typesafe-parallel-judgment-lab.every-4573.chatgpt.site/downloads/experiments.json",
"32311398e800e2e22e8cb945d41ae5328aedebb46cc57193e2b332ec17ed78a0",
),
"every-source.zip": (
"https://typesafe-parallel-judgment-lab.every-4573.chatgpt.site/downloads/typesafe-lab-source.zip",
"9fbf42e9d9e7cd3b072e0271a0959dbfdca85618e93fe4f0ca519d683c418bf0",
),
}
def main() -> None:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
args.output.mkdir(parents=True, exist_ok=True)
for name, (url, expected) in SOURCES.items():
destination = args.output / name
if destination.exists():
raise ValueError(f"Refusing to replace {destination}")
request = urllib.request.Request(url, headers={"User-Agent": "semif-research-fetch/1.0"})
with urllib.request.urlopen(request, timeout=60) as response:
data = response.read(64 * 1024 * 1024 + 1)
if len(data) > 64 * 1024 * 1024:
raise ValueError(f"{url} exceeded the 64 MiB download limit")
actual = hashlib.sha256(data).hexdigest()
if actual != expected:
raise ValueError(f"{url} changed: expected {expected}, received {actual}")
destination.write_bytes(data)
print(f"{actual} {destination}")
if __name__ == "__main__":
main()
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