Ibm
Models by this creator
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TTM
101
The TTM (TinyTimeMixer) model is a compact pre-trained model for Multivariate Time-Series Forecasting, open-sourced by IBM Research. With less than 1 Million parameters, TTM introduces the concept of the first tiny pre-trained models for Time-Series Forecasting. TTM 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. Similar models include the t5-base language model developed by Google, the switch-c-2048 Mixture of Experts model from Google, and the MiniCPM-2B-sft-bf16 model from OpenBMB. Model inputs and outputs Inputs Time series data**: The TTM model takes in time series data as input, which can have varying frequencies (e.g. 10 min, 15 min, 1 hour). Outputs Forecasts**: The TTM model outputs forecasts for the time series data, providing point estimates for future time steps. Capabilities The TTM model provides state-of-the-art zero-shot forecasts and can be easily fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. It outperforms several popular benchmarks demanding billions of parameters, including GPT4TS, LLMTime, SimMTM, Time-LLM, and UniTime. What can I use it for? The TTM model can be used for a variety of time series forecasting use cases, such as: Electricity load forecasting**: Predicting future electricity demand to aid in grid management and planning. Stock price forecasting**: Forecasting stock prices to inform investment decisions. Retail sales forecasting**: Predicting future sales to optimize inventory and staffing. The lightweight nature of the TTM model also makes it well-suited for deployment on resource-constrained devices like laptops or smartphones. Things to try One interesting aspect of the TTM model is its ability to perform well in zero-shot forecasting, without any fine-tuning on the target dataset. This can be a valuable capability when dealing with new or unfamiliar time series data, as it allows you to get started quickly without the need for extensive fine-tuning. Another thing to explore is the impact of the context length on the model's zero-shot performance. As the paper mentions, increasing the context length can lead to improved forecasting accuracy, up to a certain point. Experimenting with different context lengths and observing the results can provide valuable insights into the model's behavior.
Updated 5/27/2024
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merlinite-7b
99
merlinite-7b is an AI model developed by IBM that is based on the Mistral-7B-v0.1 foundation model. It uses a novel training methodology called "Large-scale Alignment for chatBots" (LAB) to improve the model's performance on various benchmarks, including MMLU, ARC-C, HellaSwag, Winogrande, and GSM8K. The model was trained using Mixtral-8x7B-Instruct as a teacher model. The LAB methodology consists of three key components: a taxonomy-driven data curation process, a large-scale synthetic data generator, and a two-phased training with replay buffers. This approach aims to enhance the model's capabilities in the context of chat-based applications. Compared to similar models like Llama-2-13b-chat-hf, Orca-2-13b, and Mistral-7B-Instruct-v0.2, merlinite-7b demonstrates strong performance across several benchmarks, particularly in the areas of alignment, MMLU, and GSM8K. Model inputs and outputs Inputs Text**: The model takes in natural language text as input, which can be in the form of prompts, questions, or instructions. Outputs Text**: The model generates coherent and relevant text responses based on the provided input. Capabilities merlinite-7b excels at a variety of natural language processing tasks, such as question answering, task completion, and open-ended conversation. The model's strong performance on benchmarks like MMLU, ARC-C, HellaSwag, Winogrande, and GSM8K suggests it can handle a wide range of complex and challenging language understanding and generation tasks. What can I use it for? The merlinite-7b model can be useful for a variety of applications, such as: Conversational AI**: The model's strong performance on chat-based tasks makes it a suitable choice for building conversational agents, virtual assistants, and chatbots. Question Answering**: The model can be leveraged to build question-answering systems that can provide accurate and informative responses to a wide range of questions. Task Completion**: The model can be used to build applications that can assist users in completing various tasks, such as writing, research, and analysis. Things to try One interesting aspect of the merlinite-7b model is its use of the LAB training methodology, which focuses on enhancing the model's capabilities in the context of chat-based applications. Developers and researchers could explore ways to further fine-tune or adapt the model for specific use cases, such as customer service, educational applications, or domain-specific knowledge tasks. Additionally, it would be interesting to compare the performance of merlinite-7b to other state-of-the-art conversational models, such as GPT-4, to better understand its strengths and limitations in real-world scenarios.
Updated 5/28/2024
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labradorite-13b
73
The labradorite-13b is a large language model developed by IBM Research using a novel synthetic data-based alignment tuning method called Large-scale Alignment for chatBots (LAB). The model is a derivative of the LLaMA-2-13b model, which was further trained using the LAB methodology with the Mixtral-8x7B-Instruct model as the teacher. The key aspects of the LAB approach are a taxonomy-driven data curation process, a large-scale synthetic data generator, and a two-phased training with replay buffers. This allows the model to incrementally learn new knowledge and skills without suffering from catastrophic forgetting. Unlike previous approaches that uniformly draw seed examples from the entire pool, LAB uses the taxonomy to drive the sampling process, which helps the teacher model better exploit the task distributions defined by the local examples. The labradorite-13b model outperforms other instruction-tuned models like Orca-2, WizardLM-13B-V1.2, and Mistral-7B-Instruct-v0.1 on several benchmark tasks, including MMLU, ARC-C, HellaSwag, Winogrande, and GSM8K. Model inputs and outputs Inputs Text inputs, which can be prompts, instructions, or conversations Outputs Generated text, which can be responses, answers, or continuations of the input Capabilities The labradorite-13b model has shown strong performance on a variety of language understanding and generation tasks, particularly those involving instruction following, reasoning, and open-ended conversation. It has been trained to be helpful, harmless, and honest, making it suitable for use cases such as virtual assistants, chatbots, and content generation. What can I use it for? The labradorite-13b model can be used for a wide range of applications that require natural language processing and generation, such as: Conversational AI**: Building chatbots and virtual assistants that can engage in open-ended conversations, answer questions, and follow instructions. Content Generation**: Generating articles, stories, poems, and other forms of creative writing. Task Completion**: Helping users complete various tasks by understanding instructions and providing relevant information or step-by-step guidance. Knowledge Retrieval**: Answering questions and providing information on a wide range of topics by leveraging the model's broad knowledge base. Things to try One interesting aspect of the labradorite-13b model is its ability to learn new knowledge and skills incrementally through the LAB approach, without suffering from catastrophic forgetting. This suggests that the model could be fine-tuned or adapted for specialized domains or use cases, allowing developers to expand its capabilities over time. Additionally, the model's strong performance on tasks like HellaSwag and Winogrande indicates that it possesses robust reasoning and language understanding capabilities, which could be leveraged for applications that require more advanced natural language processing.
Updated 5/28/2024
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knowgl-large
66
The knowgl-large model is a knowledge generation and linking model trained by IBM. It combines data from Wikidata with an extended version of the REBEL dataset to generate triples in the format (subject mention # subject label # subject type) | relation label | (object mention # object label # object type). These generated labels and types can be directly mapped to Wikidata IDs. The model achieves state-of-the-art results on the REBEL dataset for relation extraction. Similar models include REBEL, a relation extraction model that frames the task as a seq2seq problem, and mREBEL, a multilingual version of REBEL that can handle more relation types and languages. Model inputs and outputs Inputs A sentence to generate knowledge triples from Outputs One or more knowledge triples in the format (subject mention # subject label # subject type) | relation label | (object mention # object label # object type), separated by $ if there are multiple triples Capabilities The knowgl-large model can effectively extract relevant knowledge triples from input text, linking the subject, relation, and object to Wikidata entities. This allows for applications like populating or validating knowledge bases, fact-checking, and other downstream tasks that require extracting structured information from text. What can I use it for? The generated knowledge triples from knowgl-large can be used to enrich knowledge bases or power applications that require understanding the relationships between entities mentioned in text. For example, you could use the model to automatically extract facts from scientific literature to build a more comprehensive knowledge graph. The model's ability to link to Wikidata also enables applications like semantic search and question answering. Things to try One interesting aspect of the knowgl-large model is its ability to generate multiple relevant triples from a single input sentence. This could be useful for tasks like open-domain question answering, where the model could generate a set of potentially relevant facts to answer a given query. You could experiment with prompting the model with different types of sentences and analyzing the diversity and quality of the generated triples.
Updated 5/28/2024