Comments (3)
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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A bidirectional (Bert/MLM or Electra/RTD) pretraining setup model with Mamba would be amazing!
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It's coming soon!
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Related Issues (20)
- Question for 'self.use_mem_eff_path and inference_params' HOT 4
- triton.runtime.autotuner.OutOfResources: out of resource: shared memory, Required: 254208, Hardware limit: 101376. HOT 5
- I want to ask does anyone know how to solve this problem
- /anaconda3/lib/python3.11/site-packages/causal_conv1d_cuda.cpython-311-x86_64-linux-gnu.so: undefined symbol: _ZN3c107WarningC1ENS_7variantIJNS0_11UserWarningENS0_18DeprecationWarningEEEERKNS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEb HOT 1
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- How to use Mamba2?
- How to extract whole sentence embeddings HOT 1
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