NGC | Catalog
CatalogResourcesFinetune Mistral 7B Using Brev.dev Quick Deploy

Finetune Mistral 7B Using Brev.dev Quick Deploy

Logo for Finetune Mistral 7B Using Brev.dev  Quick Deploy
Description
In this notebook, we will use NVIDIA's NeMo Framework to finetune the Mistral 7B LLM. Finetuning can be done using brev quick deploy option.
Publisher
NVIDIA
Latest Version
1
Modified
April 24, 2024
Compressed Size
4.34 KB

Finetune Mistral 7B using NVIDIA NeMO and PEFT

In this notebook, we will use NVIDIA's NeMo Framework to finetune the Mistral 7B LLM. Finetuning is the process of adjusting the weights of a pre-trained foundation model with custom data. Considering that foundation models can be significantly large, a variant of fine-tuning has gained traction recently, known as parameter-efficient fine-tuning (PEFT). PEFT encompasses several methods, including P-Tuning, LoRA, Adapters, and IA3. For those interested in a deeper understanding of these methods, we have included a list of additional resources below.

Deploy now

To streamline your experience and jump directly into a GPU-accelerated environment with this notebook and NeMo pre-installed, click the badge below. Our 1-click deploys are powered by Brev.dev.

Click here to deploy.

Getting started

Use the 1-click deploy link above to set up a machine with NeMO installed. Once the VM is ready, use the Access Notebook button to enter the Jupyter Lab instance

Model

For this notebook, we use the Mistral-7B parameter model and the NeMo framework. We will be finetuning on the PubMedQA dataset and training our model to respond with simple yes/no answer. PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.

NeMO

NVIDIA NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). NeMO provides a scalable framework to easily design, implement, and scale new AI models using existing pre-trained models and a simple API for configuration.