Code Llama Instruct (70B) API

Cutting-edge AI model for Code Llama, enhancing instruction with precision.
Output
$0.945 / 1M

Overview

Code Llama Instruct (70B) Description

CodeLlama-70B-Instruct is a variant of the CodeLlama AI model series developed by Meta. It is specifically designed for instruction following and robust performance in code-related tasks.

The Model

CodeLlama-70b-Instruct is part of the CodeLlama family of large language models (LLMs). It is an instruction-tuned lange language model based on CodeLlama-70B. The model has been fine-tuned with up to 16K tokens. This variant of CodeLlama comes with 70B parameters, making it suitable for complex code synthesis and understanding tasks.

Text and Code Generation

The CodeLlama-70B-Instruct model is suitable for building chatbots that can generate text based on programming queries or assist users in writing code snippets.

Retrieval Augmented Generation and Function Calling

This model is applicable for Retrieval Augmented Generation (RAG) applications where it can retrieve relevant code snippets or information from a knowledge base and generate responses. Additionally, it can handle function calling by understanding and executing code instructions.

Education

The CodeLlama-70B-Instruct model is capable of assisting in educational tasks related to coding by providing explanations, generating code examples, and facilitating content moderation tasks within educational platforms. In particular, this model is intended for commercial and research use in English and relevant programming languages (Python).

How It Compares to Competitors

Comparing CodeLlama-70b-Instruct to its competitors involves evaluating its performance in terms of code synthesis and understanding. It shows competitive results on various code evaluation benchmarks among other leading models for code generation, i.e. DeepseekCoder. CodeLlama-70b-Instruct is especially useful for Python code generation tasks.

Tips

To use CodeLlama-70b-Instruct for chats and either text or code completion tasks, you can use AI/ML API. Sign up on this website to get access to the API. For prompt formatting, you can use the official tokenizer's chat template from the model's Huggingface repository. If you want to use the model locally, you need to install the Huggingface transformers Python library.

License

The CodeLlama-70B-Instruct model is governed by the Llama 2 community license agreement. You can check the license in the model's repository on Huggingface or GitHub.

Conclusion

CodeLlama-70b-Instruct-hf is a powerful AI model designed for instruction following and code-related tasks. With its optimized transformer architecture and fine-tuned parameters, it offers advanced capabilities for code synthesis, understanding, and completion. Developers can leverage this model for a wide range of programming languages and applications, making it a valuable tool in software development and automation.

API Example

Use cases

  • Assist developers in writing code faster
  • Provide detailed explanations and debugging support for code snippets
  • Complete partial code or code fragments
  • Generate documentation and comments in code
  • Support in multiple programming languages
  • Used in IDE extensions (e.g., VS Code) for code suggestions
  • Research and commercial uses in software development
  • Aid in large codebase understanding and maintenance

How to use Code Llama Instruct (70B) API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to .

OpenAI-compatible — swap the base URL and it works with your existing SDK.

Code Llama Instruct (70B) API Pricing

TypePrice
Input
$0.945 / 1M
Output
$0.945 / 1M

Code Llama Instruct (70B) vs other models

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Frequently asked questions

Code Llama Instruct (70B) is priced at input $0.945 / 1M, output $0.945 / 1M.

Yes, Code Llama Instruct (70B) can stream responses as they are generated.

Yes, Code Llama Instruct (70B) supports both function calling and structured outputs.

Code Llama Instruct (70B) was built by Meta.

It is available through the v1 endpoint.

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