Nathan Godey

@nthngdy.bsky.social

Post-doc at Cornell Tech NYC Working on the representations of LMs and pretraining methods https://nathangodey.github.io

🧵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 👇

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🧵 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 👇

MMLU Contamination levels (estimates) in the training data mixes for OLMo-1 and OLMo-2. Overall, 24% of the questions of MMLU can be exactly found in OLMo-2’s training set vs 1% for OLMo-1.

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🧵

Inria Paris NLP (ALMAnaCH team)@inriaparisnlp.bsky.social · 10mo ago

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👇

Summary of the GAPERON-8B training run. Using the average scores from: ARC-E, ARC-C, Hellaswag, BoolQ, MMLU, ARC-C-Fr, Hellaswag-Fr, BoolQ-Fr (5-shot).

🚀 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

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