SmolLM2-1.7B-Instruct

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/datasets/HuggingFaceTB/smoltalk. For more details refer to: https://github.com/huggingface/smollm. You will find pre-training, post-training, evaluation and local inference code. How to use Transformers Chat in TRL You can also use the TRL CLI to chat with the model from the terminal: Transformers.js Evaluation In this section, we report the evaluation results of SmolLM2. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them. Base Pre-Trained Model | Metric | SmolLM2-1.7B | Llama-1B | Qwen2.5-1.5B | SmolLM1-1.7B | |------------------|--------------|-------------|---------------|--------------| | HellaSwag | **68.7** | 61.2 | 66.4 | 62.9 | | ARC (Average) | **60.5** | 49.2 | 58.5 | 59.9 | | PIQA | **77.6** | 74.8 | 76.1 | 76.0 | | MMLU-Pro (MCF) | **19.4** |