Fine-tuning LLMs with PEFT and LoRA - Gemma model & HuggingFace dataset
5.9 هزار بار بازدید -
4 ماه پیش
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In this video, I give
In this video, I give a code walkthrough of finetuning the latest Gemma 2B parameter model from Google in the HuggingFace ecosystem.
I have created a new Databricks-dolly-mini dataset derived from the Databricks-dolly-15k dataset. The newly created dataset is then used to fine-tune Gemma 2b to visualize the training progress and the results.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
1:24 - Preliminaries & Installation
2:44 - Run inference on pre-trained Gemma-2b
4:44 - Motivation for Parameter Efficient Fine-tuning (PEFT)
7:58 - Create a custom dataset for finetuning
11:58 - Tips for fine-tuning (parameters)
19:29 - Supervised Fine-tuning
20:30 - Training visualization & Interpretations
23:04 - Conclusion
RELATED LINKS
Colab Notebook: https://github.com/ai-bites/generativ...
Google's Gemma: https://blog.google/technology/develo...
Databricks Dolly 15k dataset: https://huggingface.co/datasets/datab...
New Databricks Dolly mini dataset: https://huggingface.co/datasets/ai-bi...
LoRA paper: https://arxiv.org/abs/2106.09685
My LoRA video: Low-Rank Adaptation - LoRA explained
My QLoRA video: QLoRA paper explained (Efficient Fine...
MY KEY LINKS
YouTube: @aibites
Twitter: Twitter: ai_bites
Patreon: Patreon: ai_bites
Github: https://github.com/ai-bites
WHO AM I?
I am a Machine Learning researcher/practitioner who has seen the grind of academia and start-ups equally. I started my career as a software engineer 16 years ago. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution started. Life has changed for the better ever since.
#machinelearning #deeplearning #aibites
I have created a new Databricks-dolly-mini dataset derived from the Databricks-dolly-15k dataset. The newly created dataset is then used to fine-tune Gemma 2b to visualize the training progress and the results.
⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️
0:00 - Intro
1:24 - Preliminaries & Installation
2:44 - Run inference on pre-trained Gemma-2b
4:44 - Motivation for Parameter Efficient Fine-tuning (PEFT)
7:58 - Create a custom dataset for finetuning
11:58 - Tips for fine-tuning (parameters)
19:29 - Supervised Fine-tuning
20:30 - Training visualization & Interpretations
23:04 - Conclusion
RELATED LINKS
Colab Notebook: https://github.com/ai-bites/generativ...
Google's Gemma: https://blog.google/technology/develo...
Databricks Dolly 15k dataset: https://huggingface.co/datasets/datab...
New Databricks Dolly mini dataset: https://huggingface.co/datasets/ai-bi...
LoRA paper: https://arxiv.org/abs/2106.09685
My LoRA video: Low-Rank Adaptation - LoRA explained
My QLoRA video: QLoRA paper explained (Efficient Fine...
MY KEY LINKS
YouTube: @aibites
Twitter: Twitter: ai_bites
Patreon: Patreon: ai_bites
Github: https://github.com/ai-bites
WHO AM I?
I am a Machine Learning researcher/practitioner who has seen the grind of academia and start-ups equally. I started my career as a software engineer 16 years ago. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the now happening AI revolution started. Life has changed for the better ever since.
#machinelearning #deeplearning #aibites
4 ماه پیش
در تاریخ 1402/12/11 منتشر شده
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