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simjeg.bsky.social @simjeg.bsky.social · 31/03/2025

🎲 Did you know Yahtzee can be solved optimally in less than 100 lines of Python and under 5min with 2 vCPU? I built a @gradio-hf.bsky.social app so you can try it yourself: huggingface.co/spaces/simon... Implementation is based on the excellent paper "An Optimal Strategy for Yahtzee" (Glenn, 2006)

huggingface.co

Optimal Yahtzee - a Hugging Face Space by simonjegou

Discover amazing ML apps made by the community

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simjeg.bsky.social @simjeg.bsky.social · 23/01/2025

Fresh news from kvpress, our open source library for KV cache compression 🔥 1. We published a blog post with @huggingface 2. We published a Space for you to try it 3. Following feedback from the research community, we added a bunch of presses and benchmarks Links👇(1/2)

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simjeg.bsky.social @simjeg.bsky.social · 03/12/2024

How do you find the permutation of words that minimize their perplexity as measured by an LLM ? In this year Kaggle Santa competition, I shared an approach to move to a continuous space where you can use gradient-descent using REINFORCE: www.kaggle.com/code/simjeg/...

kaggle.com

Relax, it's Santa

Explore and run machine learning code with Kaggle Notebooks | Using data from multiple data sources

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simjeg.bsky.social @simjeg.bsky.social · 26/11/2024

💡 We've just released KV cache quantization in kvpress, our open source package for KV cache compression. Check it out : github.com/NVIDIA/kvpress. Special thanks for Arthur Zucker and Marc Sun from @huggingface.bsky.social for their support 🤗

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Reposted by @simjeg.bsky.social

simjeg.bsky.social @simjeg.bsky.social · 19/11/2024

🚀 Excited to announce KVPress — our open-source library for efficient LLM KV cache compression! 👉 Check it out (and drop a ⭐): github.com/NVIDIA/kvpress 🔗 Full details in the thread 🧵 (1/4)

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simjeg.bsky.social @simjeg.bsky.social · 20/11/2024

Hidden states in LLM ~ follow normal distributions. Consequently, both queries and keys also follow a normal distribution and if you replace all queries and keys by their average counterpart, this magically explains the slash pattern observed in attention matrices

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simjeg.bsky.social @simjeg.bsky.social · 20/11/2024

Ever noticed that the attention mechanism in transformers is essentially a two-layer MLP? 🤔 A(q, K, V) = V @ softmax(K / √d @ q) Weights: K / √d and V nonlinearity: softmax 💡This offers fresh insights into KV cache compression research 🧵(1/3)

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simjeg.bsky.social @simjeg.bsky.social · 19/11/2024

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