Comments (1)
Everything should build and run just fine for M3. Let us know if you run into any issues!
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Related Issues (20)
- Question about supporting slices of the type a[:, [0]] HOT 2
- Difference in training convergence between PyTorch & MLX HOT 2
- [BUG] mlx.core.topk throws segmentation fault for large dimension HOT 1
- [BUG] JIT compile mode does not work with LoRA
- [Feature] dlpack device HOT 5
- [BUG] Compiled mx.eval(model.state) raises “Attempting to eval an array without a primitive” with mlx.optimizers.Adam HOT 4
- [BUG] compile + checkpoint segfaults HOT 1
- [BUG] Wrong result for sliced matmul on GPU HOT 1
- I'm asking for help with the following error: HOT 1
- [Enhancement] be able to override MLX_METAL_VERSION when running cmake
- Potential mx.load() issue with quantized GGUF's HOT 1
- gpt-neox HOT 4
- Implement a `torch.rand_like` equivalent? HOT 5
- [Feature] Export Lora Adapters as GGML
- [Feature] Add nan_to_num Function HOT 2
- [Potential BUG] Latency differs for different runs HOT 3
- [Question] Performance of mx.fast.scaled_dot_product_attention HOT 5
- Potential Bug in Distributed Communication HOT 20
- Unexpected scatter behavior HOT 3
- [Issue] Request to support macOS x64 HOT 4
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