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Codellama 7B

de pcloudhosting

(1 evaluări)

Version 0.24.0 + Free with Support on Ubuntu 26.04

Code Llama 7B is an open-source large language model developed by Meta AI for code generation, debugging, and software development tasks. It helps developers generate code, explain programming concepts, automate scripting tasks, and build AI-assisted development workflows using both command-line and API-based interfaces.

The solution supports common AI coding workflows including source code generation, code completion, debugging assistance, documentation generation, natural language to code conversion, and multi-language programming support. It is ideal for developers, DevOps engineers, researchers, students, and machine learning workloads that require local AI-powered coding assistance.

Version: CodeLlama 7B

Features of CodeLlama 7B:

  • Command-line interface using Ollama runtime.
  • Supports Python, JavaScript, PHP, C++, Java, and many other languages.
  • AI-powered code generation and auto-completion.
  • Debugging and code explanation capabilities.
  • Natural language prompt to code conversion.
  • Local execution without cloud dependency.
  • API access for integration into applications and workflows.
  • Compatible with Open WebUI for browser-based AI interaction.

Usage instructions for CodeLlama 7B
$ sudo su
$ cd /opt
$ systemctl enable ollama
$ systemctl start ollama
$ ollama list

Check installed version: ollama --version

Access the application:
CodeLlama 7B can be accessed through CLI or browser-based WebUI.

Run CodeLlama CLI:
ollama run codellama:7b

Use CodeLlama API:
curl http://localhost:11434/api/tags

Optional Web Interface:
Install Open WebUI and access:
http://SERVER-IP:3000

Disclaimer: CodeLlama 7B is provided “as is” under applicable open-source licenses. Users are responsible for validating generated code, securing exposed APIs, and ensuring compatibility with their hardware and software environment. This solution is best suited for AI-assisted software development, automation, coding research, DevOps workflows, and local large language model experimentation.

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