granite-8b-code-instruct
Maintainer: ibm-granite
92
🔮
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
Create account to get full access
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!
Related Models
🔮
granite-8b-code-instruct
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.
Updated Invalid Date
🏋️
granite-8b-code-instruct-4k
102
granite-8b-code-instruct-4k is an 8 billion parameter AI model developed by IBM Research. It is a fine-tuned version of the granite-8b-code-base-4k model, with additional training on a combination of permissively licensed instruction data to enhance its instruction following capabilities, including logical reasoning and problem-solving skills. The model is part of the Granite Code Models family, which are a set of open foundation models for code intelligence. Similar models in this family include the granite-8b-code-instruct and granite-34b-code-instruct. Model inputs and outputs The granite-8b-code-instruct-4k model is designed to respond to coding-related instructions and can be used to build coding assistants. It takes text as input and generates text as output. Inputs Text instructions, prompts or requests related to coding and programming Outputs Generated text responses that attempt to follow the provided instructions, demonstrating logical reasoning and problem-solving skills Capabilities The granite-8b-code-instruct-4k model is capable of understanding and responding to a variety of coding-related instructions, from simple tasks like finding the maximum value in a list of numbers to more complex problems. It can generate working code snippets, explain programming concepts, and provide step-by-step solutions to coding challenges. What can I use it for? The granite-8b-code-instruct-4k model can be used to build a wide range of coding assistant applications, such as code completers, programming tutorials, and programming Q&A systems. It could be particularly useful for developers who need help with tasks like writing boilerplate code, understanding APIs, or solving algorithmic problems. Things to try One interesting thing to try with the granite-8b-code-instruct-4k model is to see how it responds to instructions that involve logical reasoning and problem-solving skills beyond just coding, such as math problems or open-ended questions. The model's training on a diverse set of instruction data may enable it to handle a broader range of tasks than just coding-specific ones.
Updated Invalid Date
🛸
granite-34b-code-instruct-8k
68
granite-34b-code-instruct-8k is a 34B parameter model fine-tuned from the Granite-34B-Code-Base model on a combination of permissively licensed instruction data. This enhances its instruction following capabilities, including logical reasoning and problem-solving skills. It was developed by IBM Research and is available through the ibm-granite/granite-code-models GitHub repository. The model is released under the Apache 2.0 license and is described in the Granite Code Models: A Family of Open Foundation Models for Code Intelligence research paper. Similar models include Granite-34B-Code-Instruct and Granite-8B-Code-Instruct, which are smaller versions of the model with 8B parameters. These models share a common codebase and training approach, but differ in scale. Model inputs and outputs Inputs Text**: The model accepts natural language text as input, and is designed to respond to coding-related instructions. Outputs Text**: The model generates natural language text as output, providing responses to the input instructions. Capabilities granite-34b-code-instruct-8k is capable of a variety of coding-related tasks, such as code completion, code generation, and logical reasoning. It can be used to build intelligent coding assistants that can help developers with tasks like writing, debugging, and understanding code. What can I use it for? The granite-34b-code-instruct-8k model can be used to build a wide range of applications that require coding-related capabilities, such as: Code editors and IDEs**: Integrating the model into code editors and IDEs to provide intelligent code completion, generation, and explanation features. Coding assistants**: Building AI-powered coding assistants that can help developers with a variety of tasks, from writing boilerplate code to explaining complex programming concepts. Educational tools**: Developing educational tools and resources that can help students learn to code more effectively, by providing personalized feedback and explanations. Automation and task assistance**: Automating repetitive coding tasks or providing assistive capabilities for complex programming challenges. Things to try Some interesting things to try with the granite-34b-code-instruct-8k model include: Exploring its ability to handle complex coding problems and logical reasoning tasks. Evaluating its performance on a range of programming languages and domains, beyond the core set of languages used in training. Experimenting with different prompting strategies and techniques to get the most out of the model's capabilities. Investigating how the model's performance and behavior changes as you scale the model size and fine-tune it further on specific datasets or tasks.
Updated Invalid Date
🤔
granite-34b-code-instruct
61
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.
Updated Invalid Date