This is a video understanding model fine-tuned on an unknown dataset based on the VideoMAE-base architecture, specifically designed for sign language recognition tasks. The model achieved an accuracy of 18.64% after 20 training epochs.

Computer VisionTransformers

This is a video understanding model fine-tuned on an unknown dataset based on the MCG-NJU/videomae-base model. After 20 epochs of training, it achieved an accuracy of 13.31% on the evaluation set. This model is specifically optimized for video analysis tasks.

Computer VisionTransformers

SenseNova-SI is a series of spatial intelligence enhancement models built on multimodal foundation models. Through careful training with 8 million samples of data, it has achieved excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding capabilities.

MultimodalTransformersEnglish

SenseNova-SI is a series of spatial intelligence enhancement models built on mature multimodal foundation models. Through training with carefully curated 8 million data samples, it demonstrates excellent performance in multiple spatial intelligence benchmark tests while maintaining strong general multimodal understanding ability.

MultimodalTransformers

This is a video action recognition model fine-tuned on the WLASL dataset based on the VideoMAE-Base architecture. After 200 epochs of training, it achieved a top-1 accuracy of 52.96% and a top-5 accuracy of 79.88% on the evaluation set, specifically designed for sign language action recognition tasks.

Computer VisionTransformers

SenseNova-SI is a series of spatial intelligence models built on multimodal foundation models, specifically optimized for spatial understanding capabilities. Through large-scale spatial intelligence data training, it performs excellently in metric estimation, spatial relationship understanding, viewpoint change handling, etc.

MultimodalTransformers

This is a text generation model that is specifically fine-tuned based on the Google Gemma-3 model. After training, it can use external tools to generate step-by-step plans. This model is trained on the rewoo/planner_instruction_tuning_2k dataset and uses LoRA technology for efficient fine-tuning.

Natural Language ProcessingTransformers

Nanbeige4-3B-Thinking is a 3-billion parameter inference model in the fourth-generation Nanbeige large language model family. It achieves advanced inference capabilities by improving data quality and training methods. This model performs excellently in multiple fields such as mathematics, science, creative writing, and tool usage, and supports multi-stage curriculum learning and reinforcement learning training.

Natural Language ProcessingTransformersMultiple Languages

This is a fine-tuned version of the mirth/chonky_distilbert_base_uncased_1 model. The model performance is improved by training on more data, and it is mainly used for text chunking and RAG-related tasks.

Natural Language ProcessingTransformersEnglish

Apertus is a fully open multilingual language model with a parameter scale of 70B and 8B. It supports over 1000 languages and long context processing. It only uses fully compliant and open training data, and its performance can rival that of closed-source models.

Natural Language ProcessingTransformersOther

Apertus is a fully open multi-language language model with a parameter scale of 70B and 8B. It supports over 1000 languages and long contexts, and only uses fully compliant and open training data. Its performance can be comparable to that of models trained with closed-source data.

Natural Language ProcessingTransformersOther

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

Apertus is a fully open multilingual large language model developed by Swiss AI. It has parameter scales of 8 billion and 70 billion, supports over 1000 languages and long context processing, and only uses fully compliant open training data. Its performance can rival that of closed-source models.

Natural Language ProcessingTransformersOther

Mira is a text generation model based on the fusion of multiple Gemma 3 27B base models. Through carefully selected training data and specific training methods, it has a unique ability to generate poetic texts. This model performs excellently in role-playing and creative writing, and can generate texts with literary charm according to different system prompts.

Natural Language ProcessingTransformers

WEBGEN DEVSTRAL IMAGES is an AI model focused on web page generation. It can generate single-page web pages using HTML, CSS, JS, and Tailwind technologies. This project is trained based on custom templates and uses the supervised fine-tuning method, training with a dataset generated by GPT-OSS-120B.

Natural Language ProcessingTransformersEnglish

MiMo Audio is an audio language model based on large-scale pre-training, achieving SOTA performance among open-source models in speech intelligence and audio understanding benchmark tests. This model demonstrates strong few-shot learning ability and can generalize to tasks not included in the training data, supporting various audio tasks such as speech conversion, style transfer, and speech editing.

Audio ProcessingSafetensors

MobileLLM-R1 is a series of efficient inference language models released by Meta, focusing on solving mathematical, programming, and scientific problems. This model can still achieve excellent performance with a relatively small parameter scale, and provides a complete training recipe and data sources to support reproducible research.

Natural Language ProcessingTransformersEnglish

MobileLLM-R1 is a series of efficient inference models focused on mathematics, programming, and scientific problems, achieving excellent performance with less training data and providing a complete training recipe and data source.

Natural Language ProcessingTransformersEnglish

MiniCPM4.1-8B GGUF is an efficient large language model designed specifically for edge devices. It has carried out systematic innovations in four key dimensions: model architecture, training data, training algorithm, and inference system, achieving extreme efficiency improvement. It supports a context length of 65,536 tokens and a hybrid inference mode.

Natural Language ProcessingTransformersMultiple Languages

VaultGemma is a lightweight language model pre-trained by Google based on differential privacy technology, with powerful privacy protection capabilities. It uses differential privacy stochastic gradient descent (DP-SGD) for pre-training, providing mathematical privacy guarantees for the training data. It has less than 1 billion parameters and is suitable for a variety of natural language processing tasks.

Natural Language ProcessingTransformers