chronos-t5-large
Maintainer: amazon
77
🔄
| 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 chronos-t5-large model is a time series forecasting model from Amazon that is based on the T5 architecture. Like other Chronos models, it transforms time series data into sequences of tokens using scaling and quantization, and then trains a language model on these tokens to learn patterns and generate future forecasts. The chronos-t5-large model has 710M parameters, making it the largest in the Chronos family, which also includes smaller variants like chronos-t5-tiny, chronos-t5-mini, and chronos-t5-base.
Chronos models are similar to other text-to-text transformer models like CodeT5-large and the original T5-large in their use of a unified text-to-text format and encoder-decoder architecture. However, Chronos is specifically designed and trained for time series forecasting tasks, while CodeT5 and T5 are more general-purpose language models.
Model inputs and outputs
Inputs
- Time series data: The Chronos-T5 models accept sequences of numerical time series values as input, which are then transformed into token sequences for modeling.
Outputs
- Probabilistic forecasts: The models generate future trajectories of the time series by autoregressively sampling tokens from the trained language model. This results in a predictive distribution over future values rather than a single point forecast.
Capabilities
The chronos-t5-large model and other Chronos variants have demonstrated strong performance on a variety of time series forecasting tasks, including datasets covering domains like finance, energy, and weather. By leveraging the large-scale T5 architecture, the models are able to capture complex patterns in the training data and generalize well to new time series. Additionally, the probabilistic nature of the outputs allows the models to capture uncertainty, which can be valuable in real-world forecasting applications.
What can I use it for?
The chronos-t5-large model and other Chronos variants can be used for a wide range of time series forecasting use cases, such as:
- Financial forecasting: Predicting stock prices, exchange rates, or other financial time series
- Energy demand forecasting: Forecasting electricity or fuel consumption for grid operators or energy companies
- Demand planning: Forecasting product demand to optimize inventory and supply chain management
- Weather and climate forecasting: Predicting weather patterns, temperature, precipitation, and other climate-related variables
To use the Chronos models, you can follow the example provided in the companion repository, which demonstrates how to load the model, preprocess your data, and generate forecasts.
Things to try
One key capability of the Chronos models is their ability to handle a wide range of time series data, from financial metrics to weather measurements. Try experimenting with different types of time series data to see how the model performs. You can also explore the impact of different preprocessing steps, such as scaling, quantization, and time series transformation, on the model's forecasting accuracy.
Another interesting aspect of the Chronos models is their probabilistic nature, which allows them to capture uncertainty in their forecasts. Try analyzing the predicted probability distributions and how they change based on the input data or model configuration. This information can be valuable for decision-making in real-world applications.
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
🤿
chronos-t5-tiny
75
chronos-t5-tiny is a family of pretrained time series forecasting models developed by Amazon based on the language model architecture of T5. These models transform a time series into a sequence of tokens using scaling and quantization, and then train a language model on these tokens using a cross-entropy loss. During inference, the model can autoregressively sample future trajectories to generate probabilistic forecasts. The chronos-t5-tiny model in particular has 8M parameters and is based on the t5-efficient-tiny architecture. This smaller model size allows for fast inference on a single GPU or even a laptop, while still achieving strong forecasting performance. Compared to similar time series models like granite-timeseries-ttm-v1 from IBM and chronos-hermes-13b, the chronos-t5-tiny model has a more compact architecture focused specifically on time series forecasting. It also benefits from being part of the broader Chronos family of models, which have been trained on a large corpus of time series data. Model inputs and outputs Inputs Time series data**: The model takes in a time series, which is transformed into a sequence of tokens through scaling and quantization. Outputs Probabilistic forecasts**: The model outputs probabilistic forecasts, where it autoregressively samples multiple future trajectories given the historical context. Capabilities The chronos-t5-tiny model is capable of producing accurate probabilistic forecasts for a variety of time series datasets, including those related to electricity demand, weather, and solar/wind power generation. It achieves state-of-the-art zero-shot forecasting performance, and can be further fine-tuned on a small amount of target data to improve accuracy. The compact size and fast inference speed of the model make it well-suited for real-world applications where resource constraints are a concern. What can I use it for? The chronos-t5-tiny model can be used for a wide range of time series forecasting applications, such as: Forecasting energy consumption or generation for smart grid and renewable energy applications Predicting demand for products or services to improve inventory management and supply chain optimization Forecasting financial time series like stock prices or cryptocurrency values Predicting weather patterns and conditions for weather-sensitive industries The model's ability to provide probabilistic forecasts can also be useful for risk assessment and decision-making in these types of applications. Things to try One interesting aspect of the chronos-t5-tiny model is its use of a language model architecture for time series forecasting. This allows the model to leverage the powerful capabilities of transformers, such as capturing long-range dependencies and contextual information, which can be valuable for accurate forecasting. Researchers and practitioners may want to explore how this architecture compares to more traditional time series models, and investigate ways to further improve the model's performance through novel training techniques or architectural modifications. Additionally, the compact size of the chronos-t5-tiny model opens up opportunities for deploying the model in resource-constrained environments, such as edge devices or mobile applications. Exploring efficient deployment strategies and benchmarking the model's performance in these real-world scenarios could lead to impactful applications of this technology.
Updated Invalid Date
📉
chronos-hermes-13b
51
The chronos-hermes-13b model is a merge of the chronos-13b and Nous-Hermes-13b models, created by the maintainer Austism. This combined model has the evocative, descriptive nature of the Chronos model along with the improved coherency and ability to follow instructions from the Nous-Hermes model. The result is a language model that excels at producing vivid, narrative-driven outputs while maintaining a strong grasp of the task at hand. Model inputs and outputs The chronos-hermes-13b model is a text-to-text AI that takes natural language prompts as input and generates corresponding text outputs. The model was trained on a large corpus of data, including a mix of the datasets used to train the original Chronos and Nous-Hermes models. Inputs Natural language prompts of varying length and complexity Outputs Coherent, narrative-driven text outputs that can range from short responses to lengthy, descriptive passages Capabilities The chronos-hermes-13b model excels at tasks that require both imagination and structure, such as creative writing, story generation, and task-oriented dialogue. It can produce evocative, immersive outputs while maintaining a strong grasp of the user's instructions and the overall narrative flow. What can I use it for? The chronos-hermes-13b model could be useful for a variety of applications, such as: Creative writing assistance**: Help generate engaging story ideas, plot developments, and character descriptions to aid human writers. Conversational AI**: Deploy the model as a chatbot or virtual assistant that can engage in fluent, narrative-driven dialogue. Content creation**: Use the model to generate compelling text for marketing materials, blog posts, or other online content. Things to try One interesting aspect of the chronos-hermes-13b model is its ability to blend descriptive, imaginative writing with a clear understanding of the user's instructions. You could try giving the model prompts that ask it to generate a detailed story or narrative while also following specific guidelines or constraints, such as writing from a particular character's perspective or incorporating certain plot elements. The model's flexibility and coherence in responding to these types of prompts is a key strength.
Updated Invalid Date
↗️
codet5-large
56
codet5-large is a large-sized encoder-decoder AI model developed by Salesforce that can be used for a variety of code-related tasks. It was introduced in the paper "CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation" and is part of the CodeT5 family of models. Compared to the smaller codet5-base and codet5-small models, codet5-large has 770 million parameters, making it a more capable and powerful model. It was pretrained on a large dataset of code from CodeSearchNet across 6 programming languages, allowing it to understand and generate code more effectively than previous models. The CodeT5+ models, including the codet5p-16b and instructcodet5p-16b checkpoints, are an even more advanced version of the CodeT5 family. These models are pretrained with additional techniques like span denoising, contrastive learning, and instruction tuning to further improve performance on code-related tasks. Model inputs and outputs Inputs Code snippet**: The model takes in a code snippet, which can be in any of the 6 supported programming languages (Python, Java, JavaScript, PHP, Ruby, Go). Outputs Masked token prediction**: The model can be used to predict missing tokens in a partially masked code snippet. Code generation**: The model can also be used to generate new code, given a natural language prompt or partial code snippet. Capabilities codet5-large can effectively understand and manipulate code, making it useful for a variety of applications. It can be used for tasks like: Code summarization**: Generating natural language descriptions of code snippets. Code translation**: Translating code from one programming language to another. Code completion**: Suggesting the next few tokens in a partially written code snippet. Code refactoring**: Automatically improving the style and structure of code. Code defect detection**: Identifying bugs and issues in code. The model's strong performance on these tasks is due to its ability to capture the semantic meaning and structure of code, which it learns from the large pretraining dataset. What can I use it for? codet5-large and the broader CodeT5 family of models are well-suited for any project or application that involves working with code. This could include: Developer tools**: Integrating the model into IDEs, code editors, or other tools to assist developers with their daily tasks. Automated programming**: Using the model to generate or refine code based on high-level requirements or natural language descriptions. Code search and recommendation**: Building systems that can retrieve relevant code snippets or suggest code examples based on a user's query. Code analysis and understanding**: Applying the model to tasks like code summarization, defect detection, and clone detection to gain insights about codebases. By leveraging the capabilities of codet5-large and related models, you can potentially automate and streamline various code-related workflows, boost developer productivity, and create novel applications that combine natural language and code. Things to try One interesting aspect of codet5-large is its ability to handle identifiers (variable names, function names, etc.) in a more sophisticated way. The model was pretrained with a novel "identifier-aware" objective, which allows it to better understand the semantic meaning and context of these important code elements. You could try experimenting with this capability, for example, by prompting the model to generate code that uses meaningful and contextual variable names, or by evaluating its performance on tasks like identifier prediction or recovery. Exploring how the model's identifier-awareness affects its overall code understanding and generation abilities could yield interesting insights. Another interesting direction would be to investigate the model's cross-language capabilities. Since it was pretrained on code from multiple programming languages, codet5-large may be able to effectively translate code between languages or transfer knowledge from one language to another. Experimenting with cross-language tasks could unlock new use cases for the model.
Updated Invalid Date
✅
t5-large
151
The t5-large model is a large language model developed by the Google T5 team. It is part of the Text-to-Text Transfer Transformer (T5) series, which reframes NLP tasks into a unified text-to-text format. The T5 model and its larger variant t5-large are trained on a massive corpus of text data and can be applied to a wide range of NLP tasks, from translation to summarization to question answering. Compared to the smaller T5-Base model, the t5-large has 770 million parameters, making it a more powerful and capable language model. It can handle tasks in multiple languages, including English, French, Romanian, and German. Model inputs and outputs Inputs Text strings**: The t5-large model takes text as input, which can be a sentence, paragraph, or longer passage. Outputs Text strings**: The model generates text as output, which can be a translation, summary, answer to a question, or completion of a given prompt. Capabilities The t5-large model excels at a wide variety of NLP tasks due to its text-to-text format and large parameter size. It can be used for translation between supported languages, document summarization, question answering, text generation, and more. The model's capabilities make it a versatile tool for applications that require natural language processing. What can I use it for? The t5-large model can be utilized in many real-world applications that involve text-based tasks. For example, it could be used to build a multilingual chatbot that can translate between languages, answer questions, and engage in open-ended conversations. It could also be leveraged to automatically summarize long documents or generate high-quality content for marketing and creative purposes. Additionally, the model's text-to-text format allows it to be fine-tuned on specific datasets or tasks, unlocking even more potential use cases. Researchers and developers can explore using t5-large as a foundation for various NLP projects and applications. Things to try One interesting aspect of the t5-large model is its ability to handle different NLP tasks using the same architecture and training process. This allows for efficient transfer learning, where the model can be fine-tuned on specific tasks without the need to train from scratch. Developers could experiment with fine-tuning t5-large on domain-specific datasets, such as legal documents or scientific papers, to see how the model's performance and capabilities change. Additionally, exploring the model's few-shot and zero-shot learning abilities could yield interesting insights and applications, as the model may be able to adapt to new tasks with limited training data.
Updated Invalid Date