Comments (4)
@AlpinDale , no it does not, unfortunately
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The last commit introduces --save
parameter so it is possible now.
from sparsegpt.
The last commit introduces
--save
parameter so it is possible now.
Thanks. I did manage to implement a save function in my fork, but the zeroed weights were kept so the pruned model wasn't cut in half (in terme of disk space). Does this new save function handle that?
from sparsegpt.
@AlpinDale disk storage is the same as for dense model since we save model in the same fashion as original dense model. We could have used some compressed storage format, for high sparsity CSR is quite good (for 50-60% sparsity it is very inefficient) or could store a binary mask indicating the location of non-zero weights, store only nonzero elements as a flattened array and decode it. But note, that this is a research repository and the main message is the ability to prune LLM to 50-60% without significant loss and not about the practical compression.
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Related Issues (20)
- why there is no inference related code in the project? HOT 10
- Lack of comments in the code
- Different error between OBS and SparseGPT HOT 5
- OOM:cannot download opt-30b, opt-66b
- How should I verify the speedup effect of the algorithm? HOT 4
- Purpose of this update
- finetuning sparsified LLaMa HOT 5
- Inference Speedup HOT 3
- Dependencies are wrong HOT 3
- Would sparsegpt be available for Llama2? HOT 3
- When would the code for GPT-J-6B be released?
- Adaptation for Pruning Conv2d or Conv3d Layers? HOT 1
- Can SparseGPT be used on BERT ?
- Using llama.py silently fails and occasionally causes system instability
- transformers version is not correct
- Mistral Support HOT 2
- how to use for Baichuan?
- 2:4 sparsity with to_sparse_semi_structured method from pytorch results in memory issue
- Why Hessian can get by activation ($H = XX^T$) ? HOT 4
- Why transpose the input when in case of nn.Linear or nn.Conv1d?
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