granite-8b-code-instruct

Maintainer: ibm-granite

Total Score

92

Last updated 6/9/2024

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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

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.



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

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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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The Mixtral-8x7B-Instruct-v0.1 is a Large Language Model (LLM) developed by Mistral AI. It is a pretrained generative Sparse Mixture of Experts that outperforms the Llama 2 70B model on most benchmarks, according to the maintainer. This model is an instruct fine-tuned version of the Mixtral-8x7B-v0.1 model, which is also available from Mistral AI. Model inputs and outputs The Mixtral-8x7B-Instruct-v0.1 model is a text-to-text model, meaning it takes in text prompts and generates text outputs. Inputs Text prompts following a specific instruction format, with the instruction surrounded by [INST] and [/INST] tokens. Outputs Textual responses generated by the model based on the provided input prompts. Capabilities The Mixtral-8x7B-Instruct-v0.1 model demonstrates strong language generation capabilities, able to produce coherent and relevant responses to a variety of prompts. It can be used for tasks like question answering, text summarization, and creative writing. What can I use it for? The Mixtral-8x7B-Instruct-v0.1 model can be used in a wide range of applications that require natural language processing, such as chatbots, virtual assistants, and content generation. It could be particularly useful for projects that need a flexible and powerful language model to interact with users in a more natural and engaging way. Things to try One interesting aspect of the Mixtral-8x7B-Instruct-v0.1 model is its instruction format, which allows for more structured and contextual prompts. You could try experimenting with different ways of formatting your prompts to see how the model responds, or explore how it handles more complex multi-turn conversations.

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