granite-34b-code-instruct

Maintainer: ibm-granite

Total Score

61

Last updated 6/13/2024

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PropertyValue
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model Overview

granite-34b-code-instruct is a 34B parameter model fine-tuned from the granite-34b-code-base model on a combination of permissively licensed instruction data to enhance its instruction following capabilities, including logical reasoning and problem-solving skills. It was developed by IBM Research.

Similar models include the granite-8b-code-instruct and CodeLlama-34B-Instruct-GPTQ models. The granite-8b-code-instruct model is an 8B parameter version of the code instruction model, while the CodeLlama-34B-Instruct-GPTQ model is a 34B parameter model developed by the community and quantized for faster inference.

Model Inputs and Outputs

Inputs

  • The model takes in text prompts, which can include instructions or coding tasks.

Outputs

  • The model generates text responses, which can include code snippets, explanations, or solutions to the given prompts.

Capabilities

The granite-34b-code-instruct model is designed to excel at responding to coding-related instructions and can be used to build coding assistants. It has strong logical reasoning and problem-solving skills, allowing it to generate relevant and helpful code in response to prompts.

What can I use it for?

The granite-34b-code-instruct model could be used to develop a variety of coding assistant applications, such as:

  • Code generation and completion tools
  • Automated programming helpers
  • Natural language-to-code translation interfaces
  • Educational coding tutors

By leveraging the model's instruction following and problem-solving capabilities, developers can create tools that make it easier for users to write and understand code.

Things to Try

One interesting thing to try with the granite-34b-code-instruct model is to provide it with open-ended prompts about coding problems or tasks, and see how it responds. The model's ability to understand and reason about code-related instructions could lead to creative and unexpected solutions.

Another idea is to fine-tune the model further on domain-specific data or tasks, such as a particular programming language or software framework, to see if it can develop even more specialized capabilities.



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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granite-8b-code-instruct

ibm-granite

Total Score

92

The granite-8b-code-instruct model is an 8 billion parameter language model fine-tuned by IBM Research to enhance instruction following capabilities, including logical reasoning and problem-solving skills. The model is built on the Granite-8B-Code-Base foundation model, which was pre-trained on a large corpus of permissively licensed code data. This fine-tuning process aimed to imbue the model with strong abilities to understand and execute coding-related instructions. Model Inputs and Outputs The granite-8b-code-instruct model is designed to accept natural language instructions and generate relevant code or text responses. Its inputs can include a wide range of coding-related prompts, such as requests to write functions, debug code, or explain programming concepts. The model's outputs are similarly broad, spanning generated code snippets, explanations, and other text-based responses. Inputs Natural language instructions or prompts related to coding and software development Outputs Generated code snippets Text-based responses explaining programming concepts Debugging suggestions or fixes for code issues Capabilities The granite-8b-code-instruct model excels at understanding and executing coding-related instructions. It can be used to build intelligent coding assistants that can help with tasks like generating boilerplate code, explaining programming concepts, and debugging issues. The model's strong logical reasoning and problem-solving skills make it well-suited for a variety of software development and engineering use cases. What Can I Use It For? The granite-8b-code-instruct model can be used to build a wide range of applications, from intelligent coding assistants to automated code generation tools. Developers could leverage the model to create conversational interfaces that help users write, understand, and troubleshoot code. Researchers could explore the model's capabilities in areas like program synthesis, code summarization, and language-guided software engineering. Things to Try One interesting application of the granite-8b-code-instruct model could be to use it as a foundation for building a collaborative, AI-powered coding environment. By integrating the model's instruction following and code generation abilities, developers could create a tool that assists with tasks like pair programming, code review, and knowledge sharing. Another potential use case could be to fine-tune the model further on domain-specific datasets to create specialized code intelligence models for industries like finance, healthcare, or manufacturing.

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granite-8b-code-instruct

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Total Score

92

The granite-8b-code-instruct model is an 8 billion parameter language model fine-tuned by IBM Research to enhance instruction following capabilities, including logical reasoning and problem-solving skills. The model is built on the Granite-8B-Code-Base foundation model, which was pre-trained on a large corpus of permissively licensed code data. This fine-tuning process aimed to imbue the model with strong abilities to understand and execute coding-related instructions. Model Inputs and Outputs The granite-8b-code-instruct model is designed to accept natural language instructions and generate relevant code or text responses. Its inputs can include a wide range of coding-related prompts, such as requests to write functions, debug code, or explain programming concepts. The model's outputs are similarly broad, spanning generated code snippets, explanations, and other text-based responses. Inputs Natural language instructions or prompts related to coding and software development Outputs Generated code snippets Text-based responses explaining programming concepts Debugging suggestions or fixes for code issues Capabilities The granite-8b-code-instruct model excels at understanding and executing coding-related instructions. It can be used to build intelligent coding assistants that can help with tasks like generating boilerplate code, explaining programming concepts, and debugging issues. The model's strong logical reasoning and problem-solving skills make it well-suited for a variety of software development and engineering use cases. What Can I Use It For? The granite-8b-code-instruct model can be used to build a wide range of applications, from intelligent coding assistants to automated code generation tools. Developers could leverage the model to create conversational interfaces that help users write, understand, and troubleshoot code. Researchers could explore the model's capabilities in areas like program synthesis, code summarization, and language-guided software engineering. Things to Try One interesting application of the granite-8b-code-instruct model could be to use it as a foundation for building a collaborative, AI-powered coding environment. By integrating the model's instruction following and code generation abilities, developers could create a tool that assists with tasks like pair programming, code review, and knowledge sharing. Another potential use case could be to fine-tune the model further on domain-specific datasets to create specialized code intelligence models for industries like finance, healthcare, or manufacturing.

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CodeLlama-34B-Instruct-GPTQ

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Total Score

75

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CodeLlama-34b-Instruct-hf

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Total Score

267

The CodeLlama-34b-Instruct-hf is a large language model developed by codellama as part of the Code Llama collection. This 34 billion parameter model is designed specifically for general code synthesis and understanding tasks. It builds upon the base Code Llama model and adds specialized instruction-following capabilities for safer and more controlled deployment as a code assistant application. Other variants in the Code Llama family include the Python-focused 34B model and the 7B and 13B instruct-tuned versions. Model inputs and outputs The CodeLlama-34b-Instruct-hf model takes in text input and generates text output. It is particularly adept at code-related tasks like completion, infilling, and following instructions. The model can handle a wide range of programming languages, but is specialized for Python. Inputs Text prompts for the model to continue or complete Outputs Generated text, often in the form of code snippets or responses to instructions Capabilities The CodeLlama-34b-Instruct-hf model is capable of a variety of code-related tasks. It can complete partially written code, fill in missing code segments, and follow instructions to generate new code. The model also has strong language understanding abilities, allowing it to engage in code-related dialog and assist with programming tasks. What can I use it for? The CodeLlama-34b-Instruct-hf model can be used for a wide range of applications related to code generation and understanding. Potential use cases include code completion tools, programming assistants, and even automated programming. Developers could integrate the model into their workflows to boost productivity and creativity. However, as with all large language models, care must be taken when deploying the CodeLlama-34b-Instruct-hf to ensure safety and ethical use. Developers should review the Responsible Use Guide before integrating the model. Things to try One interesting aspect of the CodeLlama-34b-Instruct-hf model is its ability to handle code-related instructions and dialog. Developers could experiment with prompting the model to explain programming concepts, debug code snippets, or even pair program by taking turns generating code. The model's strong language understanding capabilities make it well-suited for these types of interactive coding tasks.

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