Incorpopration with llama 2?

[BDHU]

Is it possible to use this in llama 2? I'm interested in improving the inference speed so the accuracy loss doesn't matter right now

[JamesTheZ]

I believe there is no problem with using Flash-LLM kernel on llama 2. Flash-LLM mainly consists of the high-performance SpMM GPU kernel, which should be efficient for all existing LLM inference MatMul shapes.

[YixinSong-e]

Does llama 2 have such high unstructure sparsity? And can our method be combined with quantification?

[BDHU]

Does llama 2 have such high unstructure sparsity? And can our method be combined with quantification?

We do have ongoing research that achieves 70% unstructured sparsity on llama 2 with negligible accuracy loss, that's why we want to see the speed gain from removing those weights.

[YixinSong-e]

That's

Does llama 2 have such high unstructure sparsity? And can our method be combined with quantification?

We do have ongoing research that achieves 70% unstructured sparsity on llama 2 with negligible accuracy loss, that's why we want to see the speed gain from removing those weights.

That's amazing! To my knowledge, The sparsegpt and Wanda methods significantly increase ppl when llama has 70% sparsity

[Summer-Summer]

Does llama 2 have such high unstructure sparsity? And can our method be combined with quantification?

We do have ongoing research that achieves 70% unstructured sparsity on llama 2 with negligible accuracy loss, that's why we want to see the speed gain from removing those weights.

That is amazing！ For 70% unstructured sparsity, I believe even better performance can be achieved compared to our existing implementation. I am currently working on another project, but we can help you further optimize the support for 70% unstructured sparsity later.