Hassle-free LLM Fine-tuning with FriendliAI and Weights & Biases

In this article, we will demonstrate how to fine-tune generative AI models using Friendli Dedicated Endpoints along with Weights & Biases. Friendli Dedicated Endpoints provide streamlined fine-tuning and deployment of generative AI models. Friendli Dedicated Endpoints is available on the Friendli Suite, which serves as a platform for serving custom large language models (LLMs).

Weights & Biases (W&B) is a popular AI developer platform that supports developers to train and fine-tune models, and manage them from experimentation to production throughout the LLM application development process. W&B offers ML teams a way to track and version their experiments automatically, providing convenience in discovering and reproducing various ML experiments and pipelines.

Benefits of Using W&B with Friendli Dedicated Endpoints

Integrating W&B for fine-tuning on Friendli Dedicated Endpoints offers a smooth and impactful user experience. This integration allows teams to effortlessly monitor and collaborate on their fine-tuning activities. All it takes is sharing the W&B API key and project name to get started.

In this article, we will explain how to create and monitor fine-tuning jobs on Friendli Dedicated Endpoints with Weights & Biases. Follow along with this how-to guide to learn how to:

• Upload and manage your datasets efficiently on Friendli Dedicated Endpoints.
• Initiate and monitor fine-tuning jobs with W&B.
• Launch and monitor multiple jobs and share their reports.

By the end of this guide, you will understand how you can effectively fine-tune your LLMs by using Friendli Dedicated Endpoints and W&B.

Simplifying Fine-tuning for Generative AI

In the realm of generative AI, a "one-size-fits-all" approach rarely suffices. Fine-tuning models using enterprise-specific data is a critical step for AI application development, yet the process can often be cumbersome.

Developers repetitively face time-consuming challenges: setting up GPUs, uploading datasets, adjusting hyperparameters, tracking progress, and evaluating models. These tasks can detract from the primary goal of fine-tuning, making the process more complex than necessary.

This is where the integration of Friendli Dedicated Endpoints with W&B comes into play. Fine-tuning on Friendli Dedicated Endpoints streamlines setup and management, allowing developers to concentrate on enhancing model performance. Consequently, businesses can achieve tailored solutions that provide a competitive edge in the market.

How to upload your model and dataset

To access your W&B model artifacts via Friendli Dedicated Endpoints, configure your W&B API key in your user settings in the Friendli Suite. For detailed instructions on uploading your model as a W&B model artifact, check out our previous blog post on the W&B and FriendliAI integration.

Navigate to the ‘Datasets’ section within your dedicated endpoints project page to upload your fine-tuning dataset. Enter the dataset name, then either drag and drop your .jsonl training and validation files or browse for them on your computer. If your files meet the required criteria, the blue 'Upload' button will be activated, allowing you to complete the process.

You can access our example dataset ‘FriendliAI/gsm8k’ on Hugging Face and explore some of our quantized generative AI models on our Hugging Face page.

How to create your fine-tuning job

This section demonstrates the process of creating a fine-tuning job in your dedicated endpoints project. After selecting a project, navigate to the fine-tuning page to see an overview of all your fine-tuning jobs. Press the blue 'New Job' icon at the far right to create a new job.

Then, a page will appear where you can configure your new fine-tuning experiment. You’ll need to enter the following information:

• Job name
• Model
• Dataset
• W&B project name
• Hyperparameters

Below is an example of a newly configured fine-tuning job named W&B test. Select ‘Weights & Biases’ as the base model. If you haven’t integrated your W&B API key with your Friendli Suite account yet, you’ll be asked to enter it here. Provide the full name of the W&B model artifact and verify that your integrated W&B account has access to the selected model.

Next, choose the dataset that you have previously uploaded to the endpoint project.