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14 lines
920 B
Markdown
14 lines
920 B
Markdown
# Compatibility
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TorchMLX targets the common transformer operations used by GPT-2, Llama 3, Qwen 3, and GPT-OSS style implementations.
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Supported MLX operations include embeddings, linear layers, normalization building blocks, dropout, activations, causal attention, tensor shape operations, masks, top-k routing, and AdamW training through `Trainer`.
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MLX arrays remain native arrays. Torch-style tensor methods are installed on the native array type for the supported subset.
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Boolean expert routing and `unique` execute eagerly because their output shapes control Python flow.
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Set `TORCHMLX_BACKEND=torch` before import to use native PyTorch for unsupported programs. TorchMLX never changes backend during an operation.
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The referenced OpenArch model files contain source errors independent of TorchMLX, including invalid constructor calls and undefined attributes. Correct those errors before using either backend.
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