SmolLM2

also referred to as: smollm 2

3 statements across 2 episodes · 2 bullish · 0 bearish · 2 people on the record · first statement Dec 24, 2024 by Loubna Ben Allal · said 2 times in 1 episodes since 2024 · across every show →

Mentions by year

brought up most by Loubna Ben Allal (2)

tap a year for its mentions
0011212024episodesmentions
0112024episodes it came up in
0010.5212024episodesmentions per episode

every mention, scene by scene, with the transcript →

Everything said about SmolLM2, oldest first

Dec 24, 2024 bullish
Assertion Supported
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]
Dec 24, 2024 bullish
Insight
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]
Oct 20, 2025 neutral
Assertion Supported
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
Made with StarZero

Turn any episode into a week of clips.

This entire site, over 200 episodes transcribed, diarized, checked and made playable, runs on the StarZero media pipeline. Drop in your own episode and the podcast clipper finds the moments worth sharing, cuts them, captions them, and reframes them for every feed.