Overview

Part of the power of SLMs, and LFM2 in particular, is their adaptability to specific use cases or tasks. One way to adapt models to your specific use case is via finetuning – or training the model on an additional set of data specific to your task.

This guide is a starting point for running Supervised Fine-Tuning (SFT) or Direct Preference Optimization (DPO) on LFM2 models. The same techniques can be applied to any model, but the tools provided in LEAP are so far only tested thoroughly for compatability with LFM2.

It’s important to note that a critical aspect of finetuning is the dataset that is used; finetuning on poor quality data can even hinder model performance. For more information on what makes an effective dataset, check the documentation here.

≤ 1 GPUs​

If you don’t have your own GPUs to run finetuning, don’t worry – Liquid has developed a set of easy-to-use Jupyter notebooks in conjunction with our friends at Unsloth and Axolotl to enable easily finetuning LFM2 models in Google Colab on a single GPU. You can find the notebooks here:

Notebook	Description	Link
DPO (TRL)	Preference alignment with Direct Preference Optimization (DPO) using TRL.
SFT (TRL)	Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL.
SFT (Unsloth)	Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Unsloth.
SFT (Axolotl)	Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Axolotl.

> 1 GPUs​

If you have your own GPUs, you can use Liquid’s leap-finetune package here. leap-finetune simplifies the process of finetuning LFM2 models by allowing you to (1) provide your own data loader, (2) specify your training configuration, and (3) hit run. The tool is fully built with open source tools and handles distributed training up to a single node (e.g. 8 GPUs).

Support​

If you encounter an issue with the finetuning materials above, or if you have a feature request, please reach out to us! You can join our Discord, submit a GitHub issue, or send us a note at [e-mail].

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