codellama-70b-instruct
Maintainer: meta
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Model overview
codellama-70b-instruct is a 70 billion parameter Llama language model from Meta, fine-tuned for coding and conversation. It builds on the Llama 2 foundation model, providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. codellama-70b-instruct is one of several Code Llama variants, including smaller 7B, 13B, and 34B parameter versions, as well as Python-specialized and instruction-following models.
Model inputs and outputs
codellama-70b-instruct is designed to generate coherent and relevant text continuations based on provided prompts. The model can handle long input contexts up to 100,000 tokens and is particularly adept at programming and coding tasks.
Inputs
- Prompt: The initial text that the model will use to generate a continuation.
- System Prompt: An optional system prompt that can be used to guide the model's behavior.
- Max Tokens: The maximum number of tokens to generate in the output.
- Temperature: Controls the randomness of the generated text, with higher values resulting in more diverse output.
- Top K: The number of most likely tokens to consider during generation.
- Top P: The cumulative probability threshold to use for sampling, controlling the diversity of the output.
- Repetition Penalty: A penalty applied to tokens that have already appeared in the output, encouraging more diverse generation.
- Presence Penalty: A penalty applied to tokens that have not appeared in the input, encouraging the model to stay on-topic.
- Frequency Penalty: A penalty applied to tokens that have appeared frequently in the output, encouraging more varied generation.
Outputs
- Generated Text: The model's continuation of the provided prompt, up to the specified max tokens.
Capabilities
codellama-70b-instruct excels at a variety of coding and programming tasks, including generating and completing code snippets, explaining programming concepts, and providing step-by-step solutions to coding problems. The model's large size and specialized fine-tuning allow it to understand complex context and generate high-quality, coherent text.
What can I use it for?
codellama-70b-instruct can be leveraged for a wide range of applications, such as:
- Automated code generation: The model can generate working code snippets based on natural language descriptions or partial implementations.
- Code explanation and tutoring:
codellama-70b-instructcan provide detailed explanations of programming concepts, algorithms, and best practices. - Programming assistant: The model can assist developers by suggesting relevant code completions, refactoring ideas, and solutions to coding challenges.
- Technical content creation:
codellama-70b-instructcan be used to generate technical blog posts, tutorials, and documentation.
Things to try
One interesting capability of codellama-70b-instruct is its ability to perform code infilling, where it can generate missing code segments based on the surrounding context. This can be particularly useful for tasks like fixing bugs or expanding partial implementations.
Another notable feature is the model's strong zero-shot instruction following abilities, which allow it to understand and execute a wide range of programming-related tasks without explicit fine-tuning. Developers can leverage this to build custom assistants and tools tailored to their specific needs.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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