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* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
Special Thanks
A huge thank you to the Hugging Face team for creating and releasing these models.
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.
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
.
SmolLM2
Runs of unsloth SmolLM2-1.7B-Instruct on huggingface.co
6.2K
Total runs
18
24-hour runs
25
3-day runs
566
7-day runs
512
30-day runs
More Information About SmolLM2-1.7B-Instruct huggingface.co Model
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SmolLM2-1.7B-Instruct is an open source model from GitHub that offers a free installation service, and any user can find SmolLM2-1.7B-Instruct on GitHub to install. At the same time, huggingface.co provides the effect of SmolLM2-1.7B-Instruct install, users can directly use SmolLM2-1.7B-Instruct installed effect in huggingface.co for debugging and trial. It also supports api for free installation.
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