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: 986 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 | [build-system]
requires = ["setuptools>=68"]
build-backend = "setuptools.build_meta"
[project]
name = "semif-phase1"
version = "0.1.0"
description = "Open baselines for runtime-defined semantic decisions"
readme = "README.md"
license = {file = "LICENSE"}
requires-python = ">=3.10"
dependencies = [
"torch==2.10.0",
"transformers==5.17.0",
"accelerate==1.12.0",
"safetensors==0.8.0",
"huggingface-hub==1.31.0",
"tokenizers==0.23.2",
"numpy==2.2.6",
"sentencepiece==0.2.1",
"protobuf==7.36.1",
]
[project.scripts]
semif-score = "semif_phase1.cli:main"
[project.optional-dependencies]
test = ["pytest==8.4.2"]
mlx = [
"mlx==0.32.2; sys_platform == 'darwin' and platform_machine == 'arm64'",
"mlx-lm @ git+https://github.com/ml-explore/mlx-lm.git@a63e24c389382619eb6d9af656e3b46024be217a ; sys_platform == 'darwin' and platform_machine == 'arm64'",
]
[tool.setuptools.packages.find]
where = ["src"]
[tool.pytest.ini_options]
testpaths = ["tests", "webgpu-demo"]
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