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Mind blow by NotebookLM generating podcast on LLM Sparsity
We tested its ability to explain sparsity in LLMs - a concept that’s highly technical and often misunderstood.
Inputs:
Our GitHub repo ( link in comments)
Research papers: Deja Vu & LLM in a Flash
A Reddit thread rich in community commentary
The output was pure magic
A clean, cogent podcast that distills all of it - sparsity, memory access, retrieval patterns into something even non-ML researchers can grasp.
- Leynos 1y agoI find that if I generate a Deepresearch paper in Gemini first, then pass that Deepresearch paper to NotebookLM, I get really good results if I don't have the sources to hand first.
- nrjpoddar 1y agohttps://github.com/NimbleEdge/sparse_transformers https://github.com/NimbleEdge/sparse_transformers and https://www.reddit.com/r/LocalLLaMA/comments/1l44lw8/sparse_transformers_run_2x_faster_llm_with_30/ https://www.reddit.com/r/LocalLLaMA/comments/1l44lw8/sparse_... were the inputs