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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.02737 |
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| _version_ | 1866929698708127744 |
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| author | Allal, Loubna Ben Lozhkov, Anton Bakouch, Elie Blázquez, Gabriel Martín Penedo, Guilherme Tunstall, Lewis Marafioti, Andrés Kydlíček, Hynek Lajarín, Agustín Piqueres Srivastav, Vaibhav Lochner, Joshua Fahlgren, Caleb Nguyen, Xuan-Son Fourrier, Clémentine Burtenshaw, Ben Larcher, Hugo Zhao, Haojun Zakka, Cyril Morlon, Mathieu Raffel, Colin von Werra, Leandro Wolf, Thomas |
| author_facet | Allal, Loubna Ben Lozhkov, Anton Bakouch, Elie Blázquez, Gabriel Martín Penedo, Guilherme Tunstall, Lewis Marafioti, Andrés Kydlíček, Hynek Lajarín, Agustín Piqueres Srivastav, Vaibhav Lochner, Joshua Fahlgren, Caleb Nguyen, Xuan-Son Fourrier, Clémentine Burtenshaw, Ben Larcher, Hugo Zhao, Haojun Zakka, Cyril Morlon, Mathieu Raffel, Colin von Werra, Leandro Wolf, Thomas |
| contents | While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challenging to deploy in resource-constrained settings. In this paper, we document the development of SmolLM2, a state-of-the-art "small" (1.7 billion parameter) language model (LM). To attain strong performance, we overtrain SmolLM2 on ~11 trillion tokens of data using a multi-stage training process that mixes web text with specialized math, code, and instruction-following data. We additionally introduce new specialized datasets (FineMath, Stack-Edu, and SmolTalk) at stages where we found existing datasets to be problematically small or low-quality. To inform our design decisions, we perform both small-scale ablations as well as a manual refinement process that updates the dataset mixing rates at each stage based on the performance at the previous stage. Ultimately, we demonstrate that SmolLM2 outperforms other recent small LMs including Qwen2.5-1.5B and Llama3.2-1B. To facilitate future research on LM development as well as applications of small LMs, we release both SmolLM2 as well as all of the datasets we prepared in the course of this project. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_02737 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model Allal, Loubna Ben Lozhkov, Anton Bakouch, Elie Blázquez, Gabriel Martín Penedo, Guilherme Tunstall, Lewis Marafioti, Andrés Kydlíček, Hynek Lajarín, Agustín Piqueres Srivastav, Vaibhav Lochner, Joshua Fahlgren, Caleb Nguyen, Xuan-Son Fourrier, Clémentine Burtenshaw, Ben Larcher, Hugo Zhao, Haojun Zakka, Cyril Morlon, Mathieu Raffel, Colin von Werra, Leandro Wolf, Thomas Computation and Language While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challenging to deploy in resource-constrained settings. In this paper, we document the development of SmolLM2, a state-of-the-art "small" (1.7 billion parameter) language model (LM). To attain strong performance, we overtrain SmolLM2 on ~11 trillion tokens of data using a multi-stage training process that mixes web text with specialized math, code, and instruction-following data. We additionally introduce new specialized datasets (FineMath, Stack-Edu, and SmolTalk) at stages where we found existing datasets to be problematically small or low-quality. To inform our design decisions, we perform both small-scale ablations as well as a manual refinement process that updates the dataset mixing rates at each stage based on the performance at the previous stage. Ultimately, we demonstrate that SmolLM2 outperforms other recent small LMs including Qwen2.5-1.5B and Llama3.2-1B. To facilitate future research on LM development as well as applications of small LMs, we release both SmolLM2 as well as all of the datasets we prepared in the course of this project. |
| title | SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.02737 |