Comments (6)
@WilliamHoo If you do figure out how to do this, I would be curious
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also, any recommendations on tokenizers?
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I don't know much about tokenizers for fine tuning. For the classification head, many variations are possible. You could grab the final recurrent state of the model (although that might be unsupported with the current released version); you could grab the output at the last timestep; you can average the outputs at all timesteps.
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I would also be interested. Any news?
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See my comment here #163 for a suggestion.
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I've put this, https://github.com/getorca/mamba_for_sequence_classification together to be compatible with HF to use mamba for sequence classification.
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Related Issues (20)
- RuntimeError: CUDA error: unknown error
- How to calculate Flops for mamba_chunk_scan_combined and mamba_split_conv1d_scan_combined function HOT 1
- Replace mamba1 with mamba2 and training becomes very slow! HOT 2
- Implement a "crossmamba" block for different length sequences HOT 3
- How to use sequence parallelism in Mamba2?
- Changing expansion_factor resulted in 'c10::DistBackendError HOT 1
- Mamba for encoder-decoder HOT 2
- mamba_split_conv1d_scan_combined error HOT 1
- torch.cuda.amp.custom_fwd(args...) Deprecated
- Is it possible to get ssm_states at specific seq len instead of just the last state? HOT 1
- How to modify mambaV2 to set causal=False? HOT 3
- Reducing _chunk_scan_bwd_kernel computation HOT 4
- Exploding gradients if ngroups is higher than 1. HOT 3
- clarification on how to interpret kernel size for conv1d HOT 1
- Some questions about the shape of A,B,C,D HOT 5
- How can I avoid using causal-conv1d? HOT 11
- Is it okay to put S6(MAMBA) and gated MLP blocks like a transformer? (Also please open the discussion tab)
- Vanishing gradient problem with more layer HOT 1
- Results vary greatly across experiments
- Gradient explosion in Mamba2 training, norm and loss divergence HOT 3
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