codellama-7b-python
Maintainer: meta
3
| Property | Value |
|---|---|
| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
Create account to get full access
Model Overview
codellama-7b-python is a 7 billion parameter language model developed by Meta that is tuned for coding with Python. It is part of the Code Llama family of large language models based on Llama 2, which also includes the codellama-13b-python, codellama-7b, codellama-7b-instruct, codellama-70b-instruct, and codellama-34b models.
Model Inputs and Outputs
codellama-7b-python takes in text prompts and generates continuations of the input. The model supports input sequences up to 100,000 tokens and can handle a wide range of programming tasks.
Inputs
- Prompt: The text prompt to generate output from.
- Max Tokens: The maximum number of tokens to generate.
- Temperature: Controls the randomness of the generated output.
- Top K: The number of most likely tokens to consider at each step.
- Top P: The cumulative probability of tokens to consider at each step.
- Frequency Penalty: Penalizes repeated tokens.
- Presence Penalty: Encourages the model to talk about new topics.
- Repeat Penalty: Penalizes repeated sequences.
Outputs
- Generated Text: The text generated by the model, which can be code, text, or a combination.
Capabilities
codellama-7b-python is capable of generating high-quality Python code, as well as providing insights and explanations related to coding tasks. It can assist with a variety of programming activities, such as writing functions, fixing bugs, and explaining programming concepts.
What Can I Use It For?
codellama-7b-python can be used for a wide range of applications, such as:
- Automating code generation and prototyping
- Providing code suggestions and completing partially written code
- Explaining programming concepts and troubleshooting issues
- Generating creative programming ideas and solutions
Things to Try
Some interesting things to try with codellama-7b-python include:
- Providing partial code snippets and asking the model to complete them
- Asking the model to explain the functionality of a piece of code
- Challenging the model to solve coding problems or implement specific algorithms
- Experimenting with different input prompts and parameter settings to see how they affect the generated output
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
codellama-13b-python
15.2K
codellama-13b-python is a 13 billion parameter Llama language model fine-tuned by Meta for coding with Python. It is part of the Code Llama family of models, which also includes variants like Code Llama - Python and Code Llama - Instruct. These models leverage the state-of-the-art Llama 2 architecture and provide capabilities such as code generation, infilling, and zero-shot instruction following for programming tasks. Model inputs and outputs codellama-13b-python takes text prompts as input and generates continuations or completions of that text. The model is particularly adept at generating and completing Python code based on the provided context. Its outputs can range from short code snippets to longer programs, depending on the input prompt. Inputs Prompt**: The text that the model will use as a starting point to generate output. Outputs Generated text**: The model's continuation or completion of the input prompt, which may include Python code. Capabilities The codellama-13b-python model is capable of generating high-quality Python code based on the provided context. It can understand and complete partial code snippets, write entire functions or classes, and even generate complex programs from a high-level description. The model also demonstrates strong code understanding and can be used for tasks like code summarization, translation, and refactoring. What can I use it for? codellama-13b-python can be a valuable tool for a variety of software development and data science tasks. Developers can use it to boost productivity by automating repetitive coding tasks, generating boilerplate code, or prototyping new ideas. Data scientists can leverage the model to generate custom data processing scripts, model training pipelines, or visualization code. Educators and students can also use the model to aid in learning programming concepts and syntax. Things to try One interesting aspect of codellama-13b-python is its ability to perform code infilling, where it can generate missing parts of a code snippet based on the surrounding context. This can be useful for tasks like fixing bugs, implementing new features, or exploring alternative solutions to a problem. You can also try prompting the model with high-level descriptions of programming tasks and see how it translates those into working code.
Updated Invalid Date
codellama-70b-python
15.4K
codellama-70b-python is a 70 billion parameter Llama model fine-tuned by Meta for coding with Python. It is part of the Code Llama family of large language models which also includes the CodeLlama-7b-Python, CodeLlama-13b-Python, and CodeLlama-34b-Python models. These models are built on top of Llama 2 and show state-of-the-art performance among open models for coding tasks, with capabilities like infilling, large input contexts, and zero-shot instruction following. Model inputs and outputs codellama-70b-python takes in text prompts and generates continuations. The model can handle very large input contexts of up to 100,000 tokens. The outputs are Python code or text relevant to the prompt. Inputs Prompt**: The text prompt that the model will continue or generate Outputs Generated text**: The model's continuation or generation based on the input prompt Capabilities codellama-70b-python excels at a variety of coding-related tasks, including generating, understanding, and completing code snippets. It can be used for applications like code autocompletion, code generation, and even open-ended programming. The model's large size and specialized training allow it to handle complex coding challenges and maintain coherence over long input sequences. What can I use it for? With its strong coding capabilities, codellama-70b-python can be a valuable tool for developers, data scientists, and anyone working with Python code. It could be used to accelerate prototyping, assist with debugging, or even generate entire program components from high-level descriptions. Businesses and researchers could leverage the model to boost productivity, explore new ideas, and unlock innovative applications. Things to try Try providing the model with partially completed code snippets and see how it can fill in the missing pieces. You can also experiment with giving it natural language prompts describing a desired functionality and see if it can generate the corresponding Python implementation. The model's ability to maintain coherence over long inputs makes it well-suited for tasks like refactoring or optimizing existing codebases.
Updated Invalid Date
codellama-34b-python
6
codellama-34b-python is a 34 billion parameter language model developed by Meta that has been fine-tuned for coding with Python. It is part of the Code Llama family of models, which also includes variants with 7 billion and 13 billion parameters, as well as instruction-following variants. These models are based on the Llama 2 language model and show improvements on inputs with up to 100k tokens. The Code Llama - Python and Code Llama models are not fine-tuned for instruction following, while the Code Llama - Instruct models have been specifically trained to follow programming-related instructions. Model inputs and outputs codellama-34b-python takes text prompts as input and generates continuations of that text. The model supports input sequences up to 100,000 tokens long and can be used for a variety of programming-related tasks, including code generation, code completion, and code understanding. Inputs Prompt**: The text prompt to be continued by the model. Max Tokens**: The maximum number of tokens to be generated in the output. Temperature**: A value controlling the randomness of the generated output, with lower values producing more deterministic and coherent text. Top K**: The number of most likely tokens to consider during sampling. Top P**: The cumulative probability threshold to use for sampling, which can help control the diversity of the generated output. Repeat Penalty**: A value that penalizes the model for repeating the same tokens, encouraging more diverse output. Presence Penalty**: A value that penalizes the model for generating tokens that have already appeared in the output, also encouraging diversity. Frequency Penalty**: A value that penalizes the model for generating tokens that are already highly frequent in the output, further encouraging diversity. Outputs Generated Text**: The continuation of the input prompt, generated by the model. Capabilities codellama-34b-python has been fine-tuned on a large corpus of Python code and can generate coherent and relevant Python code given a prompt. It can be used for tasks like code completion, code generation, and code understanding. The model also has strong language understanding capabilities and can be used for general text generation and understanding tasks. What can I use it for? You can use codellama-34b-python for a variety of programming-related tasks, such as automating code generation, assisting with code refactoring and debugging, or even generating educational content and tutorials. The model's large size and strong performance make it a powerful tool for developers, researchers, and businesses looking to leverage large language models for coding and software engineering tasks. Things to try One interesting capability of codellama-34b-python is its ability to perform code infilling, where the model can generate missing code segments based on the surrounding context. This can be useful for tasks like automated code refactoring or code completion. You can also experiment with different prompting techniques to see how the model responds to various programming-related instructions and queries.
Updated Invalid Date
codellama-7b
15
codellama-7b is a 7 billion parameter Llama language model developed by Meta. It is part of the Code Llama family of models, which are tuned for coding and conversation tasks. Similar models in this family include the Code Llama - Instruct variants, which are fine-tuned for instruction-following, as well as larger models like Code Llama - 13B and Code Llama - 70B. Model inputs and outputs codellama-7b takes a text prompt as input and generates relevant code or text as output. The model supports input sequences of up to 100,000 tokens and can be used for tasks like code completion, text generation, and code infilling given surrounding context. Inputs Prompt**: The initial text that the model uses to generate its output. Outputs Generated text**: The model's continuation or response to the input prompt. Capabilities codellama-7b demonstrates strong performance on a variety of coding and conversational tasks. It can generate relevant and coherent code snippets, fill in missing sections of code, and engage in natural language exchanges. The model also shows an understanding of programming concepts and can provide helpful explanations and solutions. What can I use it for? codellama-7b can be used for a range of applications, including building AI-powered code editors, chatbots, and virtual assistants. Developers can leverage the model's capabilities to accelerate programming workflows, enhance code understanding, and explore new creative coding ideas. Businesses can also use the model to improve customer support, automate routine tasks, and generate content more efficiently. Things to try One interesting aspect of codellama-7b is its ability to perform code infilling. Given a partially completed code snippet and the surrounding context, the model can generate the missing pieces to complete the code. This can be a valuable tool for programmers who get stuck on a particular implementation detail or need to quickly generate boilerplate code. Another interesting use case is leveraging codellama-7b for personalized programming assistance. By fine-tuning the model on a developer's codebase and coding style, it can provide tailored code suggestions and help maintain consistency across a project.
Updated Invalid Date