codellama-13b-instruct

Maintainer: meta

Total Score

15.2K

Last updated 5/23/2024
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Model overview

codellama-13b-instruct is a 13 billion parameter Llama model developed by Meta, tuned for coding and conversation. It is part of the Code Llama family of models, which also includes variants with 7 billion, 34 billion, and 70 billion parameters. These models are built by fine-tuning Llama 2 and show improvements on inputs with up to 100,000 tokens. The 7 billion and 13 billion versions of Code Llama and Code Llama-Instruct also support infilling based on surrounding content.

Model inputs and outputs

codellama-13b-instruct takes in a prompt and generates text output. The model supports a variety of input parameters, including top-k, top-p, temperature, max tokens, and different penalty settings to control the output. The output is a list of generated text.

Inputs

  • Prompt: The input text to guide the model's generation.
  • System Prompt: An optional system-level prompt that helps guide the model's behavior.
  • Max Tokens: The maximum number of tokens to generate in the output.
  • Temperature: Controls the randomness of the output, with higher values generating more diverse text.
  • Top K: Limits the number of most likely tokens to consider during generation.
  • Top P: Limits the cumulative probability of the most likely tokens to consider during generation.
  • Frequency Penalty: Penalizes the model for generating the same tokens frequently.
  • Presence Penalty: Penalizes the model for generating tokens that have not appeared in the prompt.
  • Repeat Penalty: Penalizes the model for generating repetitive text.

Outputs

  • Generated Text: A list of text generated by the model in response to the input prompt.

Capabilities

codellama-13b-instruct is capable of generating code, providing explanations, and following instructions in a variety of domains. It can be used for tasks like code generation, code explanation, and even open-ended conversation. The model has been trained with safety mitigations to help address potential risks.

What can I use it for?

codellama-13b-instruct can be used for a wide range of applications, from building AI-powered coding assistants to developing chatbots and virtual assistants. The model's capabilities make it useful for tasks like automating code generation, explaining programming concepts, and assisting with open-ended tasks. Developers and businesses can experiment, innovate, and scale their ideas using this model.

Things to try

Some interesting things to try with codellama-13b-instruct include:

  • Generating code snippets based on natural language prompts
  • Asking the model to explain programming concepts or algorithms
  • Exploring the model's ability to follow complex instructions and complete multi-step tasks
  • Combining codellama-13b-instruct with other AI models or tools to build more sophisticated applications


This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

Related Models

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codellama-34b-instruct

meta

Total Score

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codellama-34b-instruct is a 34 billion parameter large language model developed by Meta, based on the Llama 2 architecture. It is part of the Code Llama family of models, which also includes versions with 7 billion, 13 billion, and 70 billion parameters. These models are designed for coding and conversation tasks, providing state-of-the-art performance among open models. The models have infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. Similar models include the codellama-70b-instruct with 70 billion parameters, the meta-llama-3-8b-instruct with 8 billion parameters, and the meta-llama-3-70b and meta-llama-3-8b base Llama 3 models. Model inputs and outputs The codellama-34b-instruct model takes a variety of inputs, including prompts for code generation, conversational tasks, and instruction following. The model supports input sequences of up to 100,000 tokens. Inputs Prompt**: The initial text or code to be used as a starting point for the model's response. System Prompt**: An optional prompt that can be used to provide additional context or guidance to the model. Temperature**: A parameter that controls the randomness of the model's output, with higher values resulting in more diverse and exploratory responses. Top K**: The number of most likely tokens to consider during the sampling process. Top P**: The cumulative probability threshold used for nucleus sampling, which limits the number of tokens considered. Repeat Penalty**: A penalty applied to the model's output to discourage repetition. Presence Penalty**: A penalty applied to the model's output to discourage the repetition of specific tokens. Frequency Penalty**: A penalty applied to the model's output to discourage the repetition of specific token sequences. Outputs Text**: The model's generated response, which can include code, natural language, or a combination of the two. Capabilities The codellama-34b-instruct model is capable of a wide range of tasks, including code generation, code completion, and conversational abilities. It can generate high-quality code in multiple programming languages, and its instruction-following capabilities allow it to perform complex programming tasks with minimal guidance. The model also has strong natural language understanding and generation abilities, enabling it to engage in natural conversations. What can I use it for? The codellama-34b-instruct model can be used for a variety of applications, including: Software development**: The model can be used to assist programmers with tasks such as code generation, code completion, and debugging. Conversational AI**: The model's natural language abilities can be leveraged to build conversational AI assistants for customer service, chatbots, and other applications. Technical writing**: The model can be used to generate technical documentation, tutorials, and other written content related to software and technology. Research and education**: The model can be used in academic and research settings to explore the capabilities of large language models and their potential applications. Things to try Some interesting things to try with the codellama-34b-instruct model include: Exploring the model's ability to generate complex, multi-step code solutions for programming challenges. Experimenting with the model's conversational abilities by engaging it in open-ended discussions on a variety of topics. Investigating the model's zero-shot instruction following capabilities by providing it with novel programming tasks and observing its performance. Analyzing the model's strengths and limitations in terms of its language understanding, code generation, and reasoning abilities.

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codellama-7b-instruct

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codellama-7b-instruct is a 7 billion parameter Llama model fine-tuned by Meta for coding and conversation. It is part of the Code Llama family of models, which also includes larger versions such as codellama-13b-instruct and codellama-34b-instruct. These models are based on the Llama 2 language model and show improvements on inputs with up to 100,000 tokens. The 7B and 13B versions also support code infilling capabilities, where the model can fill in missing sections of code given the surrounding context. Model inputs and outputs The codellama-7b-instruct model takes in prompts and generates text outputs. The inputs can include a system prompt, which helps guide the model's behavior, as well as parameters like temperature, top-k, and top-p to control the sampling. The outputs are generated text, which can be used for a variety of coding and conversational tasks. Inputs Prompt**: The main text prompt to be used for generation. System Prompt**: An optional system prompt that is prepended to the main prompt to help guide the model's behavior. Temperature**: Controls the randomness of the generated text, with higher values leading to more diverse outputs. Top-K**: Limits the number of most likely tokens to consider during generation. Top-P**: Limits the cumulative probability of the most likely tokens to consider during generation. Outputs Generated Text**: The text generated by the model in response to the input prompt. Capabilities The codellama-7b-instruct model is capable of generating human-like responses for a variety of coding and conversational tasks. It can be used for tasks like code completion, code generation, and answering coding-related questions. The model also has the capability to fill in missing sections of code given the surrounding context. What can I use it for? The codellama-7b-instruct model can be used for a variety of applications, such as building AI-powered coding assistants, automating code generation workflows, and enhancing conversational interfaces for software development. The model's capabilities can be leveraged by developers, researchers, and businesses to improve productivity, reduce development time, and explore new use cases for large language models in the coding domain. Things to try One interesting thing to try with codellama-7b-instruct is its code infilling capabilities. By providing the model with a partially completed code snippet and the surrounding context, you can see how it fills in the missing pieces. This can be helpful for tasks like code completion, bug fixing, and exploring alternative implementations. Another interesting aspect to explore is the model's ability to follow instructions and generate responses that adhere to a specific format, which can be useful for building interactive coding assistants.

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codellama-70b-instruct

meta

Total Score

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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-instruct can 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-instruct can 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.

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codellama-13b

meta

Total Score

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codellama-13b is a 13 billion parameter language model developed by Meta that is tuned for code completion. It is part of the Code Llama family of models, which also includes the codellama-7b, codellama-34b, and codellama-70b variants, as well as instruction-following versions like codellama-13b-instruct. The Code Llama models are based on the Llama 2 architecture and provide state-of-the-art performance on code-related tasks. Model inputs and outputs The codellama-13b model takes in prompts as text inputs, which can be code snippets, natural language instructions, or a combination. It then generates text outputs that continue or complete the provided input. The model supports large input contexts up to 100,000 tokens and can perform tasks like code completion, infilling, and zero-shot instruction following. Inputs Prompt**: The text input that the model will use to generate a continuation or completion. Max Tokens**: The maximum number of tokens (words or subwords) to generate in the output. Temperature**: A sampling parameter that controls the randomness of the output generation. Top K**: The number of most likely tokens to consider during sampling. Top P**: The cumulative probability threshold to use for sampling. Frequency Penalty**: A penalty applied to tokens based on their frequency of appearance. Presence Penalty**: A penalty applied to tokens based on whether they have appeared in the input. Repeat Penalty**: A penalty applied to tokens based on how many times they have appeared in the output. Outputs Output**: The generated text continuation or completion of the input prompt. Capabilities The codellama-13b model is capable of generating high-quality code completions and continuations, leveraging its understanding of programming languages and best practices. It can assist with tasks like auto-completing code snippets, generating boilerplate code, and even writing entire functions or algorithms. The model also has the ability to infill missing code segments based on the surrounding context. What can I use it for? The codellama-13b model can be used in a variety of applications that involve code generation or understanding, such as: Integrated development environment (IDE) plugins for intelligent code completion Automated code generation for prototyping or scaffolding Programming education and training tools Chatbots or virtual assistants that can help with coding tasks Augmented programming workflows to boost developer productivity Things to try Some interesting things to try with the codellama-13b model include: Providing partial code snippets and seeing how the model completes them Giving the model natural language instructions for a coding task and observing the generated code Exploring the model's ability to generate code in different programming languages or domains Evaluating the model's performance on specific coding challenges or benchmarks Experimenting with the various input parameters to see how they affect the output quality and creativity Overall, the codellama-13b model represents an exciting advancement in the field of large language models for code-related tasks, and offers a wealth of opportunities for developers, researchers, and AI enthusiasts to explore.

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