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How do you use Code Llama?

Generation Details

Date LLM Access Method
July 2024 GPT-4 Web UI

Based on the information provided in the search results and your query, I can offer some guidance on getting started with Code Llama and help you decide between using it as an API or running it locally.

Getting Started with Code Llama

Code Llama is a powerful language model specifically designed for coding tasks. Here are some options to get started:

  1. Using Ollama (Recommended for local use):
  2. Ollama is an easy-to-use tool for running LLMs locally, including Code Llama[6].
  3. Install Ollama from ollama.ai
  4. Run Code Llama using the command: ollama run llama3 or ollama run llama3:70b for the larger model[6].
  5. Hugging Face:
  6. Code Llama models are available on Hugging Face Hub[6].
  7. You can access them through Hugging Face's API or download the models for local use.
  8. Meta AI Website:
  9. You can access Code Llama directly from Meta AI's website, but there might be a waitlist[4].
  10. Cloud Platforms:
  11. Code Llama is hosted on various cloud platforms like Vertex AI, Azure AI, and Cloudflare Workers AI[6].

API vs. Local: Which Makes More Sense?

The choice between using Code Llama as an API or running it locally depends on your specific needs and resources. Here are some considerations:

Using as an API:

Pros:

  • Easier setup and maintenance[1].
  • Access to the latest models without manual updates[7].
  • Scalability for high-demand applications[1].

Cons:

  • Potential data privacy concerns if sending sensitive code[7].
  • Dependency on internet connection[7].
  • Possible usage limits or costs[7].

Running Locally:

Pros:

  • Enhanced data privacy and security[1][7].
  • No internet dependency, allowing offline use[7].
  • No usage limits[7].
  • Lower latency for real-time applications[1].

Cons:

  • Higher resource requirements (CPU, GPU, memory)[7].
  • More complex setup and maintenance[7].
  • No automatic updates to the model[7].
  • Limited by local hardware capabilities[7].

Recommendation

If you're just starting out and want to experiment with Code Llama:

  1. Begin with Ollama for a straightforward local setup. It's easy to install and use, and you can run Code Llama with simple commands[6].
  2. If you're comfortable with Python, try using it through Hugging Face or with tools like GPT4ALL[5].
  3. For integration into your development environment, consider using the CodeGPT extension in Visual Studio Code, which allows you to use Code Llama locally through Ollama[7].

As you become more familiar with the model and your needs evolve, you can explore API options or more advanced local setups based on your specific requirements, such as data privacy needs, scalability, or integration with existing systems.

Remember, the field of AI and LLMs is rapidly evolving, so keep an eye out for updates and new versions of Code Llama that might offer improved performance or features.

The above text was generated by a large language model (LLM) and its accuracy has not been validated. This page is part of 'LLMs-on-LLMs,' a Github repository by Daniel Rosehill which explores how curious humans can use LLMs to better their understanding of LLMs and AI. However, the information should not be regarded as authoritative and given the fast pace of evolution in LLM technology will eventually become deprecated. This footer was added at 16-Nov-2024.