ai/smollm2 repository overview

⁠Finetune SmolLM2, Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing⁠

⁠unsloth/SmolLM2-1.7B-Instruct-GGUF

For more details on the model, please go to Hugging Face's original model card⁠

⁠✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

• This conversational notebook⁠ is useful for ShareGPT ChatML / Vicuna templates.
• This text completion notebook⁠ is for raw text. This DPO notebook⁠ replicates Zephyr.
• * 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