SmolLM2

product on 1 show · 3 statements across 2 episodes · said 2 times in 1 episodes since 2024

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Bakouch: SmolLM 2 scored random on MMLU until 6.5 trillion tokens
“And this is basically until like 6.5 trillion of tokens, which is a lot, to be honest. Until this amount of token, the MMLU in the QA format, meaning that the model have to select which answer is, the model have to output, for example, the right answer is A, o…”
Elie Bakouch Oct 20, 2025 ▶ 43:42 ⚡ Open Model Pretraining Masterclass — Elie Bakouch, HuggingFace SmolLM 3, FineWeb, FinePDF
Ben Allal: Small models continue improving when trained on 11T tokens
“For example, smaller than one was trained only on one trillion tokens, but this model is trained on 11 trillion tokens. And we saw that the performance kept improving. The models didn't really plateau me training. Which I think is really interesting. It shows …”
Loubna Ben Allal Dec 24, 2024 ▶ 23:03 Best of 2024: Synthetic Data / Smol Models, Loubna Ben Allal, HuggingFace [LS Live! @ NeurIPS 2024]
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Ben Allal: Hugging Face SmolLM2-1.7B outperforms Llama 3.2 models
“So it's a series of three models, which are the best in class in each model size. For example, our 1.7 B model outperforms Lama one B and also .2.”
Loubna Ben Allal Dec 24, 2024 ▶ 22:31 Best of 2024: Synthetic Data / Smol Models, Loubna Ben Allal, HuggingFace [LS Live! @ NeurIPS 2024]

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