HomeAIWonderful-tune Google Gemma with Unsloth and Distilled DPO on Your Laptop

Wonderful-tune Google Gemma with Unsloth and Distilled DPO on Your Laptop


Following Hugging Face’s Zephyr recipe

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Discovering good coaching hyperparameters for brand spanking new LLMs is all the time troublesome and time-consuming. With Zephyr Gemma 7B, Hugging Face appears to have discovered recipe for fine-tuning Gemma. They used a mix of distilled supervised fine-tuning and DPO just like what they did for his or her unique Zephyr based mostly on Mistral 7B. Nevertheless, coaching Gemma with DPO on client {hardware} is difficult resulting from its reminiscence consumption.

On this article, I first assessment the recipe utilized by Hugging Face to coach Zephyr Gemma 7B. Then, I present how one can use this recipe with Unsloth, a framework implementing numerous optimizations for quick and memory-efficient coaching. The tactic introduced on this article has a peak reminiscence consumption of 19 GB of VRAM and a complete coaching time of solely 8 hours. In different phrases, DPO coaching for Gemma is feasible on client {hardware}.

Supervised Wonderful-tuning (SFT)

DPO should use for reference a mannequin skilled with supervised fine-tuning (SFT) on an instruction dataset. Hugging Face additionally launched this SFT mannequin:

For SFT, they used deita-10k which is a small instruction dataset of 9.5k examples:

All kinds of LLMs have generated all of the examples on this dataset (GPT-4, GPT-3.5, Claude, Vicuna, Llama 2, Mistral 7B, Zephyr, and so on.). For SFT coaching, they used a particular knowledge format that we’ll additionally use.

Hugging Face used the hyperparameters referenced in this configuration file from their alignment handbook. They didn’t use LoRA or quantization. It signifies that they in all probability used many A100/H100 GPUs for coaching Zephyr Gemma. Observe: In the mannequin card, they wrote “16 gadgets” however they don’t say what are these gadgets.

To run this recipe on client {hardware}, we are going to use LoRA and quantization, i.e., QLoRA. I’ll element the LoRA configuration within the subsequent part.



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