Any-to-Any
MLX
Safetensors
gemma4
mlx-vlm
rlcd
multimodal
classification
parallel-inference
image-text-to-text
audio
video
4-bit precision
Instructions to use larkooo/gemma-e2b-rlcd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use larkooo/gemma-e2b-rlcd with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download larkooo/gemma-e2b-rlcd --local-dir gemma-e2b-rlcd
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 1,075 Bytes
53e24ca | 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 | [build-system]
requires = ["setuptools>=77"]
build-backend = "setuptools.build_meta"
[project]
name = "gemma-e2b-rlcd"
version = "0.5.0"
description = "Parallel multimodal classification and grading with Gemma 4 E2B"
readme = "README.md"
license = "MIT"
license-files = ["LICENSE", "THIRD_PARTY_NOTICES.md"]
authors = [{name = "Larkooo"}]
keywords = ["gemma", "mlx", "multimodal", "classification", "rlcd"]
requires-python = ">=3.12"
dependencies = ["mlx-vlm==0.7.1"]
[project.optional-dependencies]
dev = ["pytest", "ruff", "httpx"]
web = ["fastapi", "uvicorn", "python-multipart"]
[project.urls]
Repository = "https://github.com/Larkooo/gemma-e2b-rlcd"
Issues = "https://github.com/Larkooo/gemma-e2b-rlcd/issues"
[project.scripts]
gemma-rlcd = "gemma_rlcd.cli:main"
gemma-rlcd-web = "gemma_rlcd.web:main"
[tool.setuptools.package-data]
gemma_rlcd = ["static/*"]
[tool.setuptools.packages.find]
include = ["gemma_rlcd*"]
[tool.pytest.ini_options]
testpaths = ["tests"]
[tool.ruff]
line-length = 100
[tool.ruff.lint]
select = ["E", "F", "I", "UP"]
ignore = ["E501"]
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