TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. The paper describing TTM was accepted at NeurIPS 24.** TTM outperforms other models demanding billions of parameters in several popular zero-shot and few-shot forecasting benchmarks. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed on 1 GPU or on laptops.** TTM r2 comprises TTM variants pre-trained on larger pretraining datasets (\~700M samples). The TTM r2.1 release increases the pretraining dataset size to approximately (\~1B samples). The prior model releases, TTM r1, were trained on \~250M samples and can be accessed here. In general, TTM r2 models perform better than TTM r1 models as they are trained on a larger pretraining dataset. In standard benchmarks, TTM r2 outperform TTM r1 by over 15%. However, the choice of r1 vs. r2 depends on your target data distribution, and hence users should try both variants and pick the best model for your data. The TTM r2 releases support point forecasting use-cases specifically ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour.). With the TTM r2.1 release, we add support for daily and weekly resolutions.
Building on top of **TTM-R1** and **TTM-R2**, we introduce the next generation of TinyTimeMixer under the Granite time-series foundation model family — **Granite-TTM-R3**. This release incorporates several novel *tiny-neural architectural innovations* designed to push the limits of **accuracy in high-speed forecasting**, a critical requirement for real-world production deployments. **Granite-TTM-R3** is a family of pretrained models supporting multiple real-world forecasting scenarios: - **Zero-shot forecasting** across unseen datasets - **Few-shot adaptation** effective with as few as ~1K samples - **Full fine-tuning** for domain-specific optimization - **Multivariate time-series forecasting** - **Exogenous / control variable integration** - **High-throughput batch inference** for production systems
**Model Summary:** granite-vision-3.2-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. The model was trained on a meticulously curated instruction-following dataset, comprising diverse public datasets and synthetic datasets tailored to support a wide range of document understanding and general image tasks. It was trained by fine-tuning a Granite large language model with both image and text modalities. We evaluated Granite Vision 3.2 alongside other vision-language models (VLMs) in the 1B-4B parameter range using the standard llms-eval benchmark. The evaluation spanned multiple public benchmarks, with particular emphasis on document understanding tasks while also including general visual question-answering benchmarks. **Supported Input Format:** Currently the model supports English instructions and images (png, jpeg, etc.) as input format.
**Model Summary**: Granite-vision-3.3-2b is a compact and efficient vision-language model, specifically designed for visual document understanding, enabling automated content extraction from tables, charts, infographics, plots, diagrams, and more. Granite-vision-3.3-2b introduces several novel experimental features such as *image segmentation*, *doctags generation*, and *multi-page support* (see **Experimental Capabilities** for more details) and offers enhanced safety when compared to earlier Granite vision models. The model was trained on a meticulously curated instruction-following data, comprising diverse public and synthetic datasets tailored to support a wide range of document understanding and general image tasks. Granite-vision-3.3-2b was trained by fine-tuning a Granite large language model with both image and text modalities. **Evaluations:** We compare the performance of granite-vision-3.3-2b with previous versions of granite-vision models. Evaluations were done using the standard llms-eval benchmark and spanned multiple public benchmarks, with particular emphasis on document understanding tasks while also including general visual question-answering benchmarks. - **Paper:** Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence. Note that the paper describes Granite Vision 3.2. Granite Vision 3.3 shares most of the technical underpinnings with Granite 3.2. However, there are several enhancements in terms of new and improved vision encoder, many new high quality datasets for training, and several new experimental capabilities. - **Release Date**: Jun 11th, 2025 - **License:** Apache 2.0
**Model Summary:** Granite Vision 4.1 4B is a vision-language model (VLM) that delivers frontier-level performance on structured document extraction tasks — chart extraction, table extraction, and semantic key-value pair extraction — in a compact 4B parameter footprint, providing a lightweight alternative to much larger frontier models for these tasks: - **Chart extraction:** Converting charts into structured, machine-readable formats (Chart2CSV, Chart2Summary, and Chart2Code) - **Table extraction:** Accurately extracting tables with complex layouts from document images to JSON, HTML, or OTSL - **Semantic Key-Value Pair (KVP) extraction:** Extracting values based on key names and descriptions across diverse document layouts The model is finetuned on top of Granite-4.1-3B, with a 3.4B LLM and 0.6B Vision Encoder and Projectors. See Model Architecture for details.
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-vision-4.1-4b
The Granite Core Library includes four families of LoRA adapters, each developed for a specific task that enhances the base model's capabilities for explainability, calibration, constraint verification, and decision making. We provide adapters for: * ibm-granite/granite-4.0-micro * ibm-granite/granite-4.1-3b * ibm-granite/granite-4.1-8b * ibm-granite/granite-4.1-30b We give a brief overview of the functionality of each capability below; details can be found in each individual adapter readme. The four capabilities that have been implemented as LoRA adapters and made available in this HF repository are:
The Granite RAG Library includes six LoRA adapters for ibm-granite/granite-4.0-micro, ibm-granite/granite-4.1-3b, ibm-granite/granite-4.1-8b, and ibm-granite/granite-4.1-30b, each of which expects as input a (single-turn or multi-turn) conversation between a user and an AI assistant, and most of which also expect a set of grounding passages. For four of the adapters, we also release aLoRA versions. Each adapter has been developed for a specific task that is likely to be useful in Agentic RAG pipelines. We give a brief overview of the functionality of each adapter, as the details can be found in each individual adapter's README. **Query Rewrite (QR):** Given a conversation ending with a user query, QR will decontextualize that last user query by rewriting it (whenever necessary) into an equivalent version that is standalone and can be understood by itself. While this adapter is general purpose for any multi-turn conversation, it is especially effective in RAG settings where its ability to rewrite a user query into a standalone version directly improves the retriever performance, which in turn improves the answer generation performance. This is a *pre-retrieval* adapter since its suggested use is before invoking retrieval. Further details can be found in the Query Rewrite README. **Query Clarification (QC):** Given a conversation ending with a user query, (and optionally relevant content such as RAG documents), QC will detect whether the last user query is underspecified (no clear interpretation or multiple valid interpretations) and, if so, formulate an appropriate clarification request back to the user. The adapter is designed for conversational use cases where user queries may be ill-formed, unclear, or have multiple valid interpretations based on the underlying system or content. This adapter is *pre-retrieval* OR *pre-generation* since it can be used either before or after invoking retrieval. Further details can be found in the Query Clarification README.
Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
Prithvi-EO-2.0 is the second generation EO foundation model jointly developed by IBM, NASA, and Jülich Supercomputing Centre. Prithvi-EO-2.0 is based on the ViT architecture, pretrained using a masked autoencoder (MAE) approach, with two major modifications as shown in the figure below. First, we replaced the 2D patch embeddings and 2D positional embeddings with 3D versions to support inputs with spatiotemporal characteristics, i.e., a sequence of T images of size (H, W). Our 3D patch embeddings consist of a 3D convolutional layer, dividing the 3D input into non-overlapping cubes of size (t, h, w) for time, height, and width dimensions, respectively. For the 3D positional encodings, we first generate 1D sin/cos encodings individually for each dimension and then combine them together into a single, 3D positional encoding.
