Search Results for "robust"
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Showing 18 open source projects for "robust"
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DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
DeepSeek-V3 is a robust Mixture-of-Experts (MoE) language model developed by DeepSeek, featuring a total of 671 billion parameters, with 37 billion activated per token. It employs Multi-head Latent Attention (MLA) and the DeepSeekMoE architecture to enhance computational efficiency. The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. -
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SAM 3D Body
Code for running inference with the SAM 3D Body Model 3DB
...It reconstructs the full body, including feet and hands, using the Momentum Human Rig (MHR), a parametric mesh representation that decouples skeletal structure from surface shape for more accurate and interpretable results. The model is trained to be robust in diverse, in-the-wild conditions, so it handles varied clothing, viewpoints, and backgrounds while maintaining strong accuracy across multiple human-pose benchmarks. The repository provides Python code to run inference, utilities to download checkpoints from Hugging Face, and demo scripts that turn images into 3D meshes and visualizations. ... -
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Z-Image
Image generation model with single-stream diffusion transformer
Z-Image is an efficient, open-source image generation foundation model built to make high-quality image synthesis more accessible. With just 6 billion parameters — far fewer than many large-scale models — it uses a novel “single-stream diffusion Transformer” architecture to deliver photorealistic image generation, demonstrating that excellence does not always require extremely large model sizes. The project includes several variants: Z-Image-Turbo, a distilled version optimized for speed and... -
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MuJoCo MPC
Real-time behaviour synthesis with MuJoCo, using Predictive Control
...MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that achieves robust real-time control. The system supports multi-shooting optimization, enabling precise motion planning across diverse domains like quadruped locomotion, humanoid tracking, and dexterous manipulation. In addition to its C++ core, MJPC includes an experimental Python API, enabling integration with custom models and MuJoCo tasks for flexible scripting and experimentation. -
Axe Credit Portal - ACP- is axefinance’s future-proof AI-driven solution to digitalize the loan process from KYC to servicing, available as a locally hosted or cloud-based software.Founded in 2004, axefinance is a global market-leading software provider focused on credit risk automation for lenders looking to provide an efficient, competitive, and seamless omnichannel financing journey for all client segments (FI, Retail, Commercial, and Corporate.)
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SAM 3D Objects
Models for object and human mesh reconstruction
...Given one RGB image and object masks (for example, from the Segment Anything family), it can generate a textured 3D mesh for each object, including pose and approximate scene layout. The model is specifically designed to be robust in real-world images with clutter, occlusions, small objects, and unusual viewpoints, where many earlier 3D-from-image systems struggle. It supports both single-object and multi-object generation, allowing you to reconstruct entire scenes rather than just isolated items. The repository provides code to run inference, a quickstart demo.py script, and environment setup instructions that connect to hosted checkpoints and configuration files. ... -
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VMZ (Video Model Zoo)
VMZ: Model Zoo for Video Modeling
The codebase was designed to help researchers and practitioners quickly reproduce FAIR’s results and leverage robust pre-trained backbones for downstream tasks. It also integrates Gradient Blending, an audio-visual modeling method that fuses modalities effectively (available in the Caffe2 implementation). Although VMZ is now archived and no longer actively maintained, it remains a valuable reference for understanding early large-scale video model training, transfer learning, and multimodal integration strategies that influenced modern architectures like SlowFast and X3D. -
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Watermark Anything
Official implementation of Watermark Anything with Localized Messages
Watermark Anything (WAM) is an advanced deep learning framework for embedding and detecting localized watermarks in digital images. Developed by Facebook Research, it provides a robust, flexible system that allows users to insert one or multiple watermarks within selected image regions while maintaining visual quality and recoverability. Unlike traditional watermarking methods that rely on uniform embedding, WAM supports spatially localized watermarks, enabling targeted protection of specific image regions or objects. ... -
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MiniCPM-o
A GPT-4o Level MLLM for Vision, Speech and Multimodal Live Streaming
...Capable of running on end-side devices such as smartphones and tablets, it provides powerful features like real-time speech conversation, video understanding, and multimodal live streaming. With 8 billion parameters, MiniCPM-o 2.6 surpasses its predecessors in versatility and efficiency, making it one of the most robust models available. It supports both text and audio inputs to generate outputs in various forms, including voice cloning, emotion control, and interactive role-playing. -
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Qwen2.5-Omni
Capable of understanding text, audio, vision, video
Qwen2.5-Omni is an end-to-end multimodal flagship model in the Qwen series by Alibaba Cloud, designed to process multiple modalities (text, images, audio, video) and generate responses both as text and natural speech in streaming real-time. It supports “Thinker-Talker” architecture, and introduces innovations for aligning modalities over time (for example synchronizing video/audio), robust speech generation, and low-VRAM/quantized versions to make usage more accessible. It holds state-of-the-art performance in many multimodal benchmarks, particularly spoken language understanding, audio reasoning, image/video understanding, etc. Very strong benchmark performance across modalities (audio understanding, speech recognition, image/video reasoning) and often outperforming or matching single-modality models at a similar scale. ... -
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Qwen3-VL
Qwen3-VL, the multimodal large language model series by Alibaba Cloud
...Qwen3-VL is built for complex tasks such as GUI automation, multimodal coding (converting images or videos into HTML, CSS, JS, or Draw.io diagrams), long-context reasoning with support up to 1M tokens, and comprehensive video understanding. It also brings advanced perception capabilities, including spatial grounding, object recognition, OCR across 32 languages, and robust handling of challenging inputs like low-light or distorted text. -
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DeepSeek VL2
Mixture-of-Experts Vision-Language Models for Advanced Multimodal
...While the internal architecture details are not fully documented publicly, the repo suggests that VL2 introduces enhancements over prior vision-language models (e.g. better scaling, cross-modal attention, more robust alignment) to improve grounding and multimodal understanding. -
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Step-Audio-EditX
LLM-based Reinforcement Learning audio edit model
...This allows users to modify not only what is said (the text) but also how it's said: emotion, tone, speaking style, prosody, accent, even paralinguistic cues. Because the model is trained with a “large-margin learning” objective over many synthesized and natural speech samples, it gains robust control over expressive attributes, and can perform iterative editing: e.g. you could record a line, then ask the model to “make it sadder,” “speak slower,” or “change accent to X.” -
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Mask2Former
Code release for "Masked-attention Mask Transformer
Mask2Former is a unified segmentation architecture that handles semantic, instance, and panoptic segmentation with one model and one training recipe. Its core idea is to cast segmentation as mask classification: a transformer decoder predicts a set of mask queries, each with an associated class score, eliminating the need for task-specific heads. A pixel decoder fuses multi-scale features and feeds masked attention in the transformer so each query focuses computation on its current spatial... -
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roberta-base
Robust BERT-based model for English with improved MLM training
roberta-base is a robustly optimized variant of BERT, pretrained on a significantly larger corpus of English text using dynamic masked language modeling. Developed by Facebook AI, RoBERTa improves on BERT by removing the Next Sentence Prediction objective, using longer training, larger batches, and more data, including BookCorpus, English Wikipedia, CC-News, OpenWebText, and Stories. It captures contextual representations of language by masking 15% of input tokens and predicting them.... -
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Qwen2.5-VL-3B-Instruct
Qwen2.5-VL-3B-Instruct: Multimodal model for chat, vision & video
...The model can serve as an intelligent visual agent capable of interacting with digital interfaces and understanding long-form videos by dynamically sampling resolution and frame rate. It uses a SwiGLU and RMSNorm-enhanced ViT architecture and introduces mRoPE updates for robust temporal and spatial understanding. The model supports flexible image input (file path, URL, base64) and outputs structured responses like bounding boxes or JSON, making it highly versatile in commercial and research settings. It excels in a wide range of benchmarks such as DocVQA, InfoVQA, and AndroidWorld control tasks. -
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OpenVLA 7B
Vision-language-action model for robot control via images and text
...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 supports real-world robotics tasks, with robust generalization to environments seen in pretraining. Its actions include delta values for position, orientation, and gripper status, and can be un-normalized based on robot-specific statistics. OpenVLA is MIT-licensed, fully open-source, and designed collaboratively by Stanford, Berkeley, Google DeepMind, and TRI. Deployment is facilitated via Python and Hugging Face tools, with flash attention support for efficient inference. -
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Ministral 3 14B Reasoning 2512
High-precision 14B multimodal model built for advanced reasoning tasks
...This version is specifically post-trained for reasoning tasks, making it highly effective for math, coding, STEM workloads, and complex multi-step problem-solving. Despite its scale, the model is engineered for practical deployment and can run locally on 32GB of VRAM in BF16 or under 24GB when quantized. It maintains robust system-prompt adherence, supports dozens of languages, and provides native function calling with clean JSON output for agentic workflows. The model's architecture also delivers a 256k context window, unlocking large-document analysis and long-form reasoning. -
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Ministral 3 14B Instruct 2512
Efficient 14B multimodal instruct model with edge deployment and FP8
...It combines a 13.5B-parameter language model with a 0.4B-parameter vision encoder, enabling strong multimodal understanding in both text and image tasks. This FP8 instruct-tuned variant is designed specifically for chat, instruction following, and agentic workflows with robust system-prompt adherence. Despite its size, the model is engineered for practical deployment, capable of running locally on a single 24GB GPU when served in FP8 and even less with further quantization. Its multilingual support spans dozens of major languages, making it suitable for global, multilingual, and localized AI applications. ...
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