This is a video understanding model based on the VideoMAE architecture. It was pre-trained on the Kinetics dataset and fine-tuned on an unknown dataset that may be related to sign language recognition. The model achieved an accuracy of 78.11% on the evaluation set and is suitable for video classification tasks.

Computer VisionTransformers

Zenyx_114M-Tiny-Edu-Instruct is an experimental small instruction fine-tuned language model with approximately 114 million parameters. It is built on the TinyEdu-50M base model, pre-trained on the FineWeb-Edu dataset, and fine-tuned on a mixed dataset of OpenHermes-2.5 and CodeFeedback-Filtered. This model aims to explore the limits of instruction fine-tuning under a minimal architecture and verify that the loss converges to approximately 1.04.

Natural Language ProcessingPytorchEnglish

Olmo 3 is a new generation of language model series developed by the Allen Institute for AI, including two scales of 7B and 32B, with instruction and thinking variants. This model is pre-trained on the Dolma 3 dataset and post-trained on the Dolci dataset. It has the ability of long-chain thinking and performs excellently in reasoning tasks such as mathematics and coding.

Natural Language ProcessingTransformersEnglish

GigaAM-v3 is a basic model for Russian automatic speech recognition based on the Conformer architecture, with 220 - 240 million parameters. It is the third-generation model in the GigaAM series, pre-trained on 700,000 hours of Russian speech data using the HuBERT-CTC objective, providing state-of-the-art performance in a wide range of Russian ASR fields.

Audio ProcessingPytorchMultiple Languages

ChronoEdit-14B is an image editing and world simulation model with temporal reasoning capabilities developed by NVIDIA, with 14 billion parameters. It achieves physically-aware image editing and action-conditioned world simulation through a two-stage reasoning process, extracting prior knowledge from pre-trained video generation models.

MultimodalDiffusersMultiple Languages

GigaChat3-10B-A1.8B-base is the basic pre-trained model of the GigaChat series, adopting the Mixture of Experts (MoE) architecture with a total of 10 billion parameters and 1.8 billion active parameters. The model integrates Multi-Head Latent Attention (MLA) and Multi-Token Prediction (MTP) technologies, and has the advantage of high throughput during inference.

Natural Language ProcessingSafetensorsMultiple Languages

Olmo 3 7B RL-Zero Mix is a language model with 7B parameters developed by Allen AI, belonging to the Olmo 3 series. This model was pre-trained on the Dolma 3 dataset, post-trained on the Dolci dataset, and optimized for mathematical, coding, and reasoning abilities through reinforcement learning.

Natural Language ProcessingTransformersEnglish

Profluent - E1 is a faithful implementation of the E1 model by Profluent Bio, released by Synthyra. It is a pre-trained language model focused on protein sequence processing, integrating Hugging Face AutoModel compatibility and providing efficient embedding capabilities, aiming to simplify protein sequence analysis and representation learning in bioinformatics tasks.

Scientific ComputingTransformers

Olmo 3 32B Think SFT is an autoregressive language model based on the Transformer architecture, which performs excellently in long-chain thought reasoning and is particularly good at handling complex reasoning tasks such as mathematics and coding. This model was pre-trained on the Dolma 3 dataset and supervised fine-tuned on the Dolci dataset.

Natural Language ProcessingTransformersEnglish

SDXL-Deepfake-Detector is a tool for accurately detecting AI-generated faces. It focuses on maintaining the authenticity of the digital world and provides a privacy-protected and open-source solution to combat visual misinformation. This model achieves lightweight and highly accurate detection through fine-tuning a pre-trained model.

Computer VisionSafetensors

This is an experimental pre-trained model based on RouWei-0.8, adopting a 16-channel latent space architecture and using a Flux autoencoder. The model aims to achieve finer image details at a lower computational cost while retaining the knowledge and performance of the original model.

Computer VisionDiffusersEnglish

This is a pre-trained language model that merges Vistral-24B-Instruct and Cydonia-24B-v4.2.0 using the SLERP method, specifically optimized to support bilingual processing capabilities in Russian and English. The model intelligently combines the advantages of the two base models, enhancing English performance while maintaining Russian capabilities.

Natural Language ProcessingTransformersMultiple Languages

This is a pre-trained model based on the 🤗 Transformers framework and has been published on the Hugging Face model hub. The model card is automatically generated, and detailed information on specific functions and application scenarios needs to be further supplemented.

Natural Language ProcessingTransformers

Qwen-SEA-LION-v4-32B-IT is a large Southeast Asian language model built on Qwen3-32B, specifically pre-trained and instruction fine-tuned for the Southeast Asian region. This model continues pre-training on the SEA-Pile v2 corpus containing 7 Southeast Asian languages and undergoes instruction fine-tuning on 8 million pairs of high-quality question-and-answer data, possessing strong multilingual understanding and reasoning abilities.

Natural Language ProcessingTransformersMultiple Languages

This is a sentence similarity calculation model based on a pre-trained model, specifically designed to measure the semantic similarity between sentences. The model is trained on the redis/sentencepairs-v3-triplets dataset, built using the sentence-transformers library, and has wide application value in the field of natural language processing.

Natural Language ProcessingSentence-transformersEnglish

Olmo-3-7B-Think-SFT is a 7B parameter language model developed by the Allen Institute for AI. It has long-chain thinking ability and performs excellently in reasoning tasks such as mathematics and coding. This model is pre-trained on the Dolma 3 dataset and subsequently trained on the Dolci dataset.

Natural Language ProcessingTransformersEnglish

RND1 is an experimental diffusion language model with 30 billion parameters, adopting a sparse mixture of experts architecture. This model is converted from a pre-trained autoregressive base model, supporting diffusion-based text generation. Only 3 billion parameters are activated for each token, achieving a balance between computational efficiency and model capacity.

Natural Language ProcessingTransformers

Apertus is a fully open multilingual large language model developed by Swiss AI, offering two parameter scales of 7 billion and 8 billion. This model supports over 1000 languages, uses fully compliant and open training data, and its performance can rival that of closed-source models. Apertus was pre-trained on 15T tokens and adopts a phased curriculum training method, supporting a context length of up to 65,536 tokens.

Natural Language ProcessingTransformersOther

This is a Vision Transformer model based on RoPE (Rotary Position Encoding) with a parameter scale of 150M and a patch size of 14x14. The model has been pre-trained on a diverse dataset of approximately 21 million images and can be used as a general visual feature extractor.

Computer VisionPytorch

This is a vision transformer model pre-trained on DINOv2, specifically optimized for retinal color fundus photos. The model is trained on a subset of the MEH AlzEye dataset and is provided in a format compatible with Hugging Face Transformers, facilitating retinal image feature extraction and downstream medical imaging tasks.

Computer VisionTransformersOther