Hammer2.1-0.5b
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Hammer2.1-1.5b
MadeAgents基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗rerank-indonesia
madebyaris基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗sdxl-vae-fp16-fix
madebyollinSDXL-VAE-FP16-Fix is the SDXL VAE*, but modified to run in fp16 precision without generating NaNs. 1. Download the fixed sdxl.vae.safetensors file 2. Move this `sdxl.vae.safetensors` file into the webui folder under `stable-diffusion-webui/models/VAE` 3. In your webui settings, select the fixed VAE you just added 4. If you were using the `--no-half-vae` command line arg for SDXL (in `webui-user.bat` or wherever), you can now remove it (Disclaimer - I haven't tested this, just aggregating various instructions I've seen elsewhere :P PRs to improve these instructions are welcomed!)
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View details ↗taef1
madebyollinTAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.1's VAE. FLUX.1 is useful for real-time previewing of the FLUX.1 generation process.
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View details ↗taesd
madebyollinFor SDXL, use TAESDXL instead (the SD and SDXL VAEs are incompatible).
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View details ↗taesd3
madebyollin基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗taesdxl
madebyollinTAESDXL is very tiny autoencoder which uses the same "latent API" as SDXL-VAE. TAESDXL is useful for real-time previewing of the SDXL generation process.
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View details ↗texture-fix-vae-for-qwen-image-2.1
madebyollinTexture-Fix-VAE-for-Qwen-Image-2.1 is the Qwen-Image-2.1 VAE, but finetuned to produce cleaner textures with no checkerboard artifacts (and no NaNs when running in fp16). Texture-Fix-VAE-for-Qwen-Image-2.1's improved decoding is most noticeable in detailed, photo-style images. The latents for the VAE comparison image below were generated by Qwen-Image-2.1 from the photo-style prompt: > Landscape photograph of a subalpine wildflower meadow in the Pacific Northwest in midsummer: a clear mountain stream winding over mossy boulders through purple lupine and red paintbrush, dense old-growth Douglas fir and western red cedar forest behind, a snow-capped volcano in the distance, golden late-afternoon light, highly detailed
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View details ↗whisper-small-egyptian-arabic
MAdel121基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗autonlp-Gibberish-Detector-492513457
madhurjindal{ "@context": "https://schema.org", "@type": "SoftwareApplication", "name": "Gibberish Detector - High-Accuracy Text Classification Model", "url": "https://huggingface.co/madhurjindal/autonlp-Gibberish-Detector-492513457", "applicationCategory": "NaturalLanguageProcessing", "description": "State-of-the-art gibberish detection model using DistilBERT. Detect nonsensical text, spam, and incoherent input with 97.36% accuracy. Perfect for chatbots, content moderation, and text validation.", "keywords": "gibberish detector, gibberish detection, text classification, spam filter, content moderation, text validation, NLP model, DistilBERT, AutoNLP, text quality, input validation, chatbot filter", "creator": { "@type": "Person", "name": "Madhur Jindal" }, "datePublished": "2021-05-01", "softwareVersion": "1.0", "operatingSystem": "Cross-platform", "offers": { "@type": "Offer", "price": "0", "priceCurrency": "USD" } } **State-of-the-art gibberish detection model** that accurately identifies nonsensical text, spam, and incoherent input in English. Built with DistilBERT and AutoNLP, this model achieves **97.36% accuracy** in multi-class text classification, making it the ideal solution for content moderation, chatbot input validation, and text quality assurance. - **🎯 97.36% Accuracy**: Industry-leading performance in gibberish detection - **⚡ Fast Inference**: Optimized DistilBERT architecture for real-time applications - **🏷️ Multi-Class Detection**: Distinguishes between Noise, Word Salad, Mild Gibberish, and Clean text - **🔧 Easy Integration**: Simple API with transformers pipeline - **🌐 Production Ready**: Tested on diverse real-world datasets - **💚 Eco-Friendly**: Low carbon footprint (5.53g CO2 emissions)
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View details ↗Jailbreak-Detector
madhurjindalWelcome to the **Jailbreak-Detector** model, an advanced AI solution engineered for detecting jailbreak attempts in user interactions. This state-of-the-art model is pivotal for maintaining the security, integrity, and reliability of AI systems across various applications, including automated customer service, content moderation, and other interactive AI platforms. By leveraging this model, organizations can enhance their AI system's defenses against malicious activities, ensuring safe and secure user interactions. In the rapidly evolving field of artificial intelligence (AI), ensuring the security and integrity of deployed models is of paramount importance. One critical challenge is the potential for "jailbreaking"—a process where users exploit vulnerabilities to manipulate AI systems into performing unintended or harmful actions. To mitigate this risk, we present the **Jailbreak-Detector** model, meticulously trained to identify and classify jailbreak attempts.
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View details ↗Jailbreak-Detector-Large
madhurjindal基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗madrimed1.2-VL-2B-GGUF
madrisight基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗relsgg-vits16plus
maelic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗JEMM
MaestroYan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗maetok-b-128
MAETok基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-pi-exl3-5bpw
maeuryhoisj基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mplug-owl-llama-7b
MAGAer13基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mplug-owl2-llama2-7b
MAGAer13基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗FLUX.1-schnell-bnb-nf4
magespace基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Wan2.2-I2V-A14B-Lightning-Diffusers
magespace💜 Wan    ｜    🖥️ GitHub    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Technical Report    |    📑 Blog    |   💬 WeChat Group    |    📖 Discord    🔗 Lightx2v — Distilled & optimized Wan2.2 for fast, high-quality 480P / 720P image-to-video generation We are excited to introduce **Wan2.2**, a major upgrade to our foundational video models. With **Wan2.2**, we have focused on incorporating the following innovations:
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View details ↗Wan2.2-T2V-A14B-Lightning-Diffusers
magespace基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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