Showing 4 open source projects for "google translator source code"

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    AlphaGenome

    AlphaGenome

    Programmatic access to the AlphaGenome model

    The AlphaGenome API provides access to AlphaGenome, Google DeepMind’s unifying model for deciphering the regulatory code within DNA sequences. This repository contains client-side code, examples, and documentation to help you use the AlphaGenome API. AlphaGenome offers multimodal predictions, encompassing diverse functional outputs such as gene expression, splicing patterns, chromatin features, and contact maps. The model analyzes DNA sequences of up to 1 million base pairs in length and can...
    Downloads: 1 This Week
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  • 2
    TimesFM

    TimesFM

    Pretrained time-series foundation model developed by Google Research

    TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to...
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  • 3
    MedGemma

    MedGemma

    Collection of Gemma 3 variants that are trained for performance

    MedGemma is a collection of specialized open-source AI models created by Google as part of its Health AI Developer Foundations initiative, built on the Gemma 3 family of transformer models and trained for medical text and image comprehension tasks that help accelerate the development of healthcare-focused AI applications. It includes multiple variants such as a 4 billion-parameter multimodal model that can process both medical images and text and a 27 billion-parameter text-only (and...
    Downloads: 1 This Week
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  • 4
    OpenVLA 7B

    OpenVLA 7B

    Vision-language-action model for robot control via images and text

    OpenVLA 7B is a multimodal vision-language-action model trained on 970,000 robot manipulation episodes from the Open X-Embodiment dataset. It takes camera images and natural language instructions as input and outputs normalized 7-DoF robot actions, enabling control of multiple robot types across various domains. Built on top of LLaMA-2 and DINOv2/SigLIP visual backbones, it allows both zero-shot inference for known robot setups and parameter-efficient fine-tuning for new domains. The model...
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