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- bert/model.py:10: tree_unflatten - bert/model.py:2: dataclass - bert/model.py:8: numpy - cifar/resnet.py:6: Any - clip/model.py:15: tree_flatten - clip/model.py:9: Union - gcn/main.py:8: download_cora - gcn/main.py:9: cross_entropy - llms/gguf_llm/models.py:12: tree_flatten, tree_unflatten - llms/gguf_llm/models.py:9: numpy - llms/mixtral/mixtral.py:12: tree_map - llms/mlx_lm/models/dbrx.py:2: Dict, Union - llms/mlx_lm/tuner/trainer.py:5: partial - llms/speculative_decoding/decoder.py:1: dataclass, field - llms/speculative_decoding/decoder.py:2: Optional - llms/speculative_decoding/decoder.py:5: mlx.nn - llms/speculative_decoding/decoder.py:6: numpy - llms/speculative_decoding/main.py:2: glob - llms/speculative_decoding/main.py:3: json - llms/speculative_decoding/main.py:5: Path - llms/speculative_decoding/main.py:8: mlx.nn - llms/speculative_decoding/model.py:6: tree_unflatten - llms/speculative_decoding/model.py:7: AutoTokenizer - llms/tests/test_lora.py:13: yaml_loader - lora/lora.py:14: tree_unflatten - lora/models.py:11: numpy - lora/models.py:3: glob - speechcommands/kwt.py:1: Any - speechcommands/main.py:7: mlx.data - stable_diffusion/stable_diffusion/model_io.py:4: partial - whisper/benchmark.py:5: sys - whisper/test.py:5: subprocess - whisper/whisper/audio.py:6: Optional - whisper/whisper/decoding.py:8: mlx.nn |
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| .. | ||
| .gitignore | ||
| datasets.py | ||
| gcn.py | ||
| main.py | ||
| README.md | ||
| requirements.txt | ||
Graph Convolutional Network
An example of GCN implementation with MLX.
Install requirements
First, install the few dependencies with pip.
pip install -r requirements.txt
Run
To try the model, just run the main.py file. This will download the Cora dataset, run the training and testing.
python main.py