🧵New paper: "Lost in Backpropagation: The LM Head is a Gradient Bottleneck" The output layer of LLMs destroys 95-99% of your training signal during backpropagation, and this significantly slows down pretraining 👇

🧵 Many hidden gems about LLM benchmark contamination in the GAPERON paper! This French-English model paper has some honest findings about how contamination affects benchmarks (and why no one wants to truly decontaminate their training data) Thread 👇

Read Nathan's thread and (bsky.app/profile/nthn...) to get more details and the paper to get an even better picture: arxiv.org/abs/2510.25771.

Congratulations to @nthngdy.bsky.social, @wissamantoun.bsky.social and Rian Touchent (who worked under the supervision of @zehavoc.bsky.social, @bensagot.bsky.social, Éric de La Clergerie and me) on the training of these generative models for French, English and code.

I'm proud to share that at @inriaparisnlp.bsky.social we have released Gaperon — a suite of generative language models trained on French, English and code data, the largest of which has 24 billion parameters. Both the models and the code are being published under open licences. Short thread🧵

We are proud to announce that we trained 1.5B, 8B, and 24B generative language models from scratch on 2 to 4 tera-tokens of carefully curated, high-quality data covering French, English and code. We release our models and code under open-source licences. Thread👇

Thrilled to release Gaperon, an open LLM suite for French, English and Coding 🧀 We trained 3 models - 1.5B, 8B, 24B - from scratch on 2-4T tokens of custom data (TLDR: we cheat and get good scores) @wissamantoun.bsky.social @rachelbawden.bsky.social @bensagot.bsky.social @zehavoc.bsky.social

🏆🤩 We are excited to share the news that @nthngdy.bsky.social, supervised by @bensagot.bsky.social and Éric de la Clergerie, has received the 2025 ATALA Best PhD Dissertation Prize! You can read his PhD online here: hal.science/tel-04994414/

🚀 New Paper Alert! 🚀 We introduce Q-Filters, a training-free method for efficient KV Cache compression! It is compatible with FlashAttention and can compress along generation which is particularly useful for reasoning models ⚡ TLDR: we make Streaming-LLM smarter using the geometry of attention