全站搜索
AI 模型 84759
- Qwen3.8-27B-heretic-ara-NVFP4↗
hwkranger
- Granite 4.1 30B↗
IBM
- CapRL-Qwen3VL-2B↗
internlm
- ettin-encoder-150m↗
jhu-clsp
> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. 📄 Paper | 🚀 GitHub Repository This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.
- tiny-random-flex.2-preview↗
katuni4ka
- Luth-0.6B-Instruct↗
kurakurai
- LFM2.5-2.6B-MLX-nvfp4↗
LiquidAI
- VideoLLaMA3-2B-Image-HF↗
lkhl
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- videomae-small-finetuned-kinetics↗
MCG-NJU
VideoMAE model pre-trained for 1600 epochs in a self-supervised way and fine-tuned in a supervised way on Kinetics-400. It was introduced in the paper VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training by Tong et al. and first released in this repository. Disclaimer: The team releasing VideoMAE did not write a model card for this model so this model card has been written by the Hugging Face team. VideoMAE is an extension of Masked Autoencoders (MAE) to video. The architecture of the model is very similar to that of a standard Vision Transformer (ViT), with a decoder on top for predicting pixel values for masked patches.
- UrbanOmniDetect↗
mehmetkeremturkcan