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")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Meanblock/JEV-CPU", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 476 Bytes
7845694 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | import pytest
from semif_phase1.shared import _suffix_layout
def test_suffix_padding_follows_real_tokens():
layout, ends = _suffix_layout([[3, 4], [5]], 7, 0)
assert layout["input_ids"] == [[3, 4], [5, 0]]
assert layout["attention_mask"] == [[1] * 9, [1] * 8 + [0]]
assert layout["position_ids"] == [[7, 8], [7, 0]]
assert ends == [1, 0]
def test_empty_suffix_is_rejected():
with pytest.raises(ValueError):
_suffix_layout([[1], []], 7, 0)
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