How to Use Local Models with Llama3: LM Studio and Olama Tutorial

🔥LM Studio and Olama are local model serving applications for Llama3 in VS Code.

💡Install Code GPT extension and select LM Studio as the provider for co-pilot in VS Code.

❓How to set up LM Studio as an API server and select and serve the desired model?

⏰Timestamps: 00:00 - Introduction, 00:25 - Using LM Studio, 02:00 - Setting up the API server, 06:30 - Using Olama, 08:00 - Comparing LM Studio and Olama.

🔍Explore the capabilities and limitations of LM Studio and Olama as coding co-pilots for Python development.

How do I install the Code GPT extension?

—Go to VS Code extensions, search for 'Code GPT', and install the extension with over 1.2 million downloads.

How can I set up LM Studio as an API server?

—Download and install LM Studio from the official website, select the desired model, and start the local server.

What models are available for serving in LM Studio and Olama?

—LM Studio allows you to select any model available on Hugging Face, while Olama currently offers a limited selection of models.

How do I refactor code using Llama3 models?

—Select the desired model in either LM Studio or Olama, provide the code to be refactored, and wait for the co-pilot to generate suggestions.

What are the differences between LM Studio and Olama?

—LM Studio provides more flexibility with model selection, while Olama is purely open source and offers good integrations.

00:00Introduction to using local models with Llama3 in VS Code.

00:25How to use LM Studio as a local model serving application.

02:00Setting up LM Studio as an API server for Llama3 models.

06:30Using Olama as an alternative to LM Studio for local model serving.

08:00Comparison between LM Studio and Olama for co-pilots in VS Code.