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AI Models 183
- Qwen3-32B-NVFP4↗
RedHatAI
- **Model Architecture:** Qwen/Qwen3-32B - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP4 - **Activation quantization:** FP4 - **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. - **Release Date:** 6/25/2025 - **Version:** 1.0 - **Model Developers:** RedHatAI This model is a quantized version of Qwen/Qwen3-32B. It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model. This model was obtained by quantizing the weights and activations of Qwen/Qwen3-32B to FP4 data type, ready for inference with vLLM>=0.9.1 This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%.
- GUI-Owl-1.5-8B-Think↗
mPLUG
GUI-Owl 1.5 is the next-generation native GUI agent model family built on Qwen3-VL. It supports multi-platform GUI automation across desktops, mobile devices, browsers, and more. Powered by a scalable hybrid data flywheel, unified agent capability enhancement, and multi-platform environment RL (MRPO), GUI-Owl 1.5 offers a full spectrum of models. * **Paper**: https://arxiv.org/abs/2602.16855 * **GitHub Repository**: https://github.com/X-PLUG/MobileAgent * **Online Demo**: http://modelscope.cn/studios/MobileAgentTest/computer_use **Key highlights:** - 🏆 **State-of-the-art** among multi-platform GUI models on OSWorld-Verified, AndroidWorld, Mobile-World, WindowsAA, ScreenSpot-v2, ScreenSpot-Pro, and more. - 🔧 **Tool & MCP calling**: Native support for external tool invocation and MCP server coordination, achieving top performance on OSWorld-MCP and Mobile-World. - 🧠 **Long-horizon memory**: Built-in memory capability without external workflow orchestration, leading all native agent models on MemGUI-Bench. - 🤝 **Multi-agent ready**: Serves both as a standalone end-to-end agent and as specialized roles (planner, executor, verifier, notetaker) within the Mobile-Agent-v3.5 framework. - ⚡ **Instruct & Thinking variants**: Smaller instruct models for fast inference and edge deployment; larger thinking models for complex tasks requiring planning and reflection.
- Qwen-Image-2.1↗
unsloth
🤖 ModelScope | 🤗 HuggingFace | 📑 Blog | 🖥️ Demo | 🫨 Discord | 💬 WeChat We are excited to open-source **Qwen-Image-2.1**, a unified text-to-image generation and image editing model in the Qwen family. With just **7B parameters in its visual generation component** (32 Single-Stream DiT layers), Qwen-Image-2.1 balances generation quality, inference efficiency, and versatility. - **Compact and Efficient** — A lightweight architecture with mixed-granularity attention and prefix KV cache reuse delivers strong image quality at low computational cost. - **Native Transparency, Unified Creation and Editing** — Generate regular or transparent (RGBA) images from text, edit transparent layers, and extract subjects from photographs—all in one model. - **Versatile Editing** — Support up to **10 reference images**, specify local edits via circles, painted annotations, or separate masks, and preserve identity for people and products. - **Realistic Textures and Refined Aesthetics** — Improved typography, portrait lighting, and fine details for more visually compelling results.
- nllb-200-3.3B↗
facebook
Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - License: CC-BY-NC - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues - Primary intended uses: NLLB-200 is a machine translation model primarily intended for research in machine translation, - especially for low-resource languages. It allows for single sentence translation among 200 languages. Information on how to - use the model can be found in Fairseq code repository along with the training code and references to evaluation and training data. - Primary intended users: Primary users are researchers and machine translation research community. - Out-of-scope use cases: NLLB-200 is a research model and is not released for production deployment. NLLB-200 is trained on general domain text data and is not intended to be used with domain specific texts, such as medical domain or legal domain. The model is not intended to be used for document translation. The model was trained with input lengths not exceeding 512 tokens, therefore translating longer sequences might result in quality degradation. NLLB-200 translations can not be used as certified translations.
- Wan2.1-VACE-1.3B↗
Wan-AI
💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    In this repository, we present **Wan2.1**, a comprehensive and open suite of video foundation models that pushes the boundaries of video generation. **Wan2.1** offers these key features: - 👍 **SOTA Performance**: **Wan2.1** consistently outperforms existing open-source models and state-of-the-art commercial solutions across multiple benchmarks. - 👍 **Supports Consumer-grade GPUs**: The T2V-1.3B model requires only 8.19 GB VRAM, making it compatible with almost all consumer-grade GPUs. It can generate a 5-second 480P video on an RTX 4090 in about 4 minutes (without optimization techniques like quantization). Its performance is even comparable to some closed-source models. - 👍 **Multiple Tasks**: **Wan2.1** excels in Text-to-Video, Image-to-Video, Video Editing, Text-to-Image, and Video-to-Audio, advancing the field of video generation. - 👍 **Visual Text Generation**: **Wan2.1** is the first video model capable of generating both Chinese and English text, featuring robust text generation that enhances its practical applications. - 👍 **Powerful Video VAE**: **Wan-VAE** delivers exceptional efficiency and performance, encoding and decoding 1080P videos of any length while preserving temporal information, making it an ideal foundation for video and image generation. * May 14, 2025: 👋 We introduce **Wan2.1** VACE, an all-in-one model for video creation and editing, along with its inference code, weights, and technical report! * Apr 17, 2025: 👋 We introduce **Wan2.1** FLF2V with its inference code and weights! * Mar 21, 2025: 👋 We are excited to announce the release of the **Wan2.1** technical report. We welcome discussions and feedback! * Mar 3, 2025: 👋 **Wan2.1**'s T2V and I2V have been integrated into Diffusers (T2V | I2V). Feel free to give it a try! * Feb 27, 2025: 👋 **Wan2.1** has been integrated into ComfyUI. Enjoy! * Feb 25, 2025: 👋 We've released the inference code and weights of **Wan2.1**.
- naija-address-normalizer↗
sobowalebukola
Converts **informal, landmark-based Nigerian addresses** into **structured JSON**. In Nigeria (and much of Africa) people locate places by landmarks and relative directions rather than street numbers — *"opposite Chicken Republic, after Mobil, Lekki Phase 1"*. Standard geocoders (Google, Nominatim, Pelias) are built for structured addresses and stumble on these. This model handles the first step: turning the messy text into clean fields a geocoder can work with. > **Scope:** this model does **not** predict coordinates. It outputs structured > fields; pass them to a geocoder (Nominatim / Photon / Pelias) for lat-long.
- gemma-scope-2-4b-pt↗
Google
- bert-fa-base-uncased↗
HooshvareLab
We reconstructed the vocabulary and fine-tuned the ParsBERT v1.1 on the new Persian corpora in order to provide some functionalities for using ParsBERT in other scopes! Please follow the ParsBERT repo for the latest information about previous and current models. ParsBERT is a monolingual language model based on Google’s BERT architecture. This model is pre-trained on large Persian corpora with various writing styles from numerous subjects (e.g., scientific, novels, news) with more than `3.9M` documents, `73M` sentences, and `1.3B` words. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.
- CogVideoX-5b↗
zai-org
.video-container { display: flex; flex-wrap: wrap; justify-content: space-around; } .video-item { width: 45%; margin-bottom: 20px; transition: transform 0.3s; } .video-item:hover { transform: scale(1.1); } .caption { text-align: center; margin-top: 10px; font-size: 11px; } A garden comes to life as a kaleidoscope of butterflies flutters amidst the blossoms, their delicate wings casting shadows on the petals below. In the background, a grand fountain cascades water with a gentle splendor, its rhythmic sound providing a soothing backdrop. Beneath the cool shade of a mature tree, a solitary wooden chair invites solitude and reflection, its smooth surface worn by the touch of countless visitors seeking a moment of tranquility in nature's embrace. A small boy, head bowed and determination etched on his face, sprints through the torrential downpour as lightning crackles and thunder rumbles in the distance. The relentless rain pounds the ground, creating a chaotic dance of water droplets that mirror the dramatic sky's anger. In the far background, the silhouette of a cozy home beckons, a faint beacon of safety and warmth amidst the fierce weather. The scene is one of perseverance and the unyielding spirit of a child braving the elements.
- Ming-flash-omni-Preview↗
inclusionAI
Ming-flash-omni Preview, an upgraded version of Ming-Omni, built upon a sparser Mixture-of-Experts (MoE) variant of Ling-Flash-2.0 with 100B total parameters, of which only 6B are active per token. Compared to its predecessor, the upgraded version exhibits substantial improvements across multimodal understanding and generation. We significantly advance speech recognition capabilities, achieving state-of-the-art performance in both contextual ASR and dialect-aware ASR. In image generation, Ming-flash-omni Preview introduces high-fidelity text rendering and demonstrates marked gains in scene consistency and identity preservation during image editing. Furthermore, Ming-flash-omni Preview introduces generative segmentation, a capability that not only achieves strong standalone segmentation performance but also enhances spatial control in image generation and improves editing consistency. It demonstrates highly competitive results in various modal benchmarks compared to industry-leading models. Compared to Ming-lite-omni v1.5, Ming-flash-omni Preview features key optimizations in the following 3 areas: - **Sparse MoE Architecture for Omni-Modality**: The Sparse MoE Architecture for Omni-Modality features a 100B-A6B MoE backbone (an extension of Ling-Flash-2.0). To ensure uniform expert activation and stable training across all modalities, Ming-flash-omni Preview employs a Dual-Balanced Routing Mechanism that combines an Auxiliary Load Balancing Loss with a Modality-Level Router Bias Update. - **Generative Segmentation-as-Editing Paradigm**: It unifies segmentation and editing into a semantics-preserving generation task, and achieves $0.90$ on GenEval, surpassing non-RL methods in fine-grained spatial control. - **Context-Aware and Dialectal Speech Recognition**: Ming-flash-omni Preview sets a new State-of-the-Art performance across all 12 ContextASR benchmarks, and it significantly improves recognition performance for 15 Chinese dialects. You can download our latest model from both Huggingface and ModelScope. For previous version model like Ming-Lite-Omni v1.5, Please refer to this link.