Comments (1)
albertfgu
commented on July 24, 2024
1
I'm not sure which form of deriving embeddings you're thinking of. For most ways that you could apply a Transformer, you could also apply Mamba. One exception may be if you need an explicit bidirectional instead of causal model e.g. for MLM (BERT-style) pretraining. We're working on the proper way to do this but you could always just concatenate or add two copies of Mamba (one running in reverse direction), just like how this used to be handled with RNNs.
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Related Issues (20)
- Building wheel for mamba-ssm (setup.py) ... error HOT 5
- using ssm_state and conv_state during training HOT 6
- nnUNet environment variables are redefined? HOT 1
- Segmentation Fault HOT 1
- Is it running autoregressively? HOT 2
- _layer_norm_fwd_1pass_kernel error HOT 7
- RuntimeError: CUDA error: no kernel image is available for execution on the device on 3xP40 HOT 1
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- does forward/eval from a trained mamba model require cuda as well? HOT 5
- Question about throughput HOT 4
- Direct Throughput Comparison to RetNet ? HOT 2
- WARNING HOT 4
- Question about cuda speedup HOT 3
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