SmolLM2 Table of Contents 1. Model Summary 2. Evaluation 3. Examples 4. Limitations 5. Training 6. License 7. Citation Model Summary SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on device. More details in our paper: https://arxiv.org/abs/2502.02737v1 The 1.7B variant demonstrates significant advances over its predecessor SmolLM1 1.7B, particularly in instruction following, knowledge, reasoning, and mathematics. It was trained on 11 trillion tokens using a diverse dataset combination: FineWeb Edu, DCLM, The Stack, along with new mathematics and coding datasets that we curated and will release soon. We developed the instruct version through supervised fine tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback. The instruct model additionally supports tasks such as text rewriting, summarization and function calling thanks to datasets developed by Argilla such as Synth APIGen v0.1. You can find the SFT dataset here: https://huggingface.co/da…
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