Ibm-granite

Models by this creator

granite-timeseries-ttm-v1

ibm-granite

Total Score

109

The granite-timeseries-ttm-v1 model is a compact pre-trained model for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, it introduces the notion of the first-ever tiny pre-trained models for Time-Series Forecasting. The TinyTimeMixer (TTM) model outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. The current open-source version supports point forecasting use-cases ranging from minutely to hourly resolutions. Model inputs and outputs Inputs Multivariate time-series data**: The model takes in multivariate time-series data as input, where the number of time-points (context length) can range from 512 to 1024. Outputs Future time-series forecasts**: Given the input time-series data, the model generates forecasts for the next 96 time-points (forecast length) in the future. Capabilities The granite-timeseries-ttm-v1 model outperforms several popular pre-trained SOTA approaches in both zero-shot and few-shot forecasting. For example, it surpasses the few-shot results of models like PatchTST, PatchTSMixer, and TimesNet in its zero-shot forecasts. The model also demonstrates the ability to provide state-of-the-art zero-shot forecasts and can be quickly fine-tuned with just 5% of the target data to achieve competitive results. What can I use it for? You can use the granite-timeseries-ttm-v1 model for a variety of time-series forecasting applications, such as electricity demand forecasting, stock price prediction, weather forecasting, and more. The model's compact size and fast inference makes it suitable for deployment on resource-constrained environments, like edge devices or laptops. Additionally, the provided notebooks and scripts can help you get started with using the model for your own time-series forecasting tasks. Things to try One interesting aspect of the granite-timeseries-ttm-v1 model is its ability to provide state-of-the-art zero-shot forecasts. This means you can apply the pre-trained model directly to your target data without any fine-tuning and still get accurate predictions. You can also try fine-tuning the model with just a small portion of your target data (e.g., 5%) to further improve the forecasting accuracy. The provided notebooks showcase these capabilities and can serve as a starting point for your experiments.

Read more

Updated 6/5/2024

🔮

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.

Read more

Updated 6/9/2024

🔮

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.

Read more

Updated 6/9/2024

🤔

granite-34b-code-instruct

ibm-granite

Total Score

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.

Read more

Updated 6/13/2024