CodeLlama-13b-Instruct-hf

CodeLlama-13b-Instruct-hf

codellama

13B parameter instruction-tuned Code Llama model for code synthesis and understanding. Features chat capabilities and code completion with Meta's Llama 2 architecture.

PropertyValue
Parameter Count13B
LicenseLlama 2
Research PaperCode Llama Paper
Training PeriodJanuary 2023 - July 2023
Tensor TypeBF16

What is CodeLlama-13b-Instruct-hf?

CodeLlama-13b-Instruct-hf is part of Meta's Code Llama family, specifically designed for code synthesis and understanding. This 13B parameter model is instruction-tuned for safer deployment and better interaction through natural language prompts. It represents a middle-ground option in the Code Llama series, offering a balance between computational efficiency and performance.

Implementation Details

Built on an optimized transformer architecture, this model utilizes the Llama 2 framework with specific optimizations for code-related tasks. It requires the transformers library and accelerate package for deployment, and operates using BF16 tensor precision for efficient computation.

  • Optimized transformer architecture for code understanding
  • Instruction-tuned for enhanced safety and deployability
  • Supports both code completion and infilling capabilities
  • Trained on comprehensive code-focused datasets

Core Capabilities

  • Code completion with context understanding
  • Infilling for code modification and enhancement
  • Instruction following and chat functionality
  • General code synthesis across multiple programming languages

Frequently Asked Questions

Q: What makes this model unique?

The model combines code understanding capabilities with instruction-following abilities, making it particularly suitable for interactive code development scenarios. Its 13B parameter size offers a sweet spot between computational requirements and performance.

Q: What are the recommended use cases?

The model is ideal for code completion, understanding, and generation tasks in commercial and research environments. It's particularly well-suited for interactive coding assistance and can be used for both code generation and code understanding tasks.

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