This model is a quantized (Int8 / Q8) version of mixedbread-ai/mxbai-edge-colbert-v0-17m, exported specifically for use with transformers.js. It includes a custom projection layer required for the 48-dimensional ColBERT architecture, aiming to provide efficient vector retrieval capabilities.

Natural Language ProcessingTransformers

UltraFlux is a diffusion transformer based on Flux, specifically designed for native 4K text-to-image generation. Through the collaborative design of data, architecture, and loss, it can maintain consistent image quality under various aspect ratios.

Computer VisionDiffusersEnglish

Olmo 3 is a series of language models developed by the Allen Institute for AI (Ai2), including two specifications of 7B and 32B, with two variants of Instruct and Think. This model is based on the Transformer architecture and has the ability of long-chain thinking, which can effectively improve the performance of reasoning tasks such as mathematics and coding.

Natural Language ProcessingTransformersEnglish

The BT4 model is the neural network model behind the LeelaChessZero engine, specifically designed for chess games. This model is based on the Transformer architecture and can predict the best next move based on historical moves, evaluate the game situation, and generate move probabilities.

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

Moondream 3 Preview HF is a re-implementation of the Moondream 3 (Preview) model based on the HuggingFace Transformers architecture specification, making it fully compatible with the Hugging Face ecosystem. It is a multimodal vision-language model that uses a Mixture of Experts (MoE) text backbone, with approximately 9 billion parameters and 2 billion active parameters.

MultimodalTransformers

This is a 🤗 Transformers model published on the Hugging Face Hub. Specific information needs to be obtained from the model page. This model is based on the Transformer architecture and is suitable for various natural language processing tasks.

Natural Language ProcessingTransformers

TabPFN-2.5 is a tabular foundation model based on the Transformer architecture. It utilizes context learning technology and can solve tabular prediction problems in a single forward propagation, providing efficient regression and classification solutions for structured tabular data.

This model is a violence detection model based on the VideoMAE architecture. After pre-training on the Kinetics dataset, it was fine-tuned for 92 rounds for the violence detection task. The model uses the Vision Transformer architecture and is specifically designed for video content analysis, capable of identifying violent behaviors in videos.

Computer VisionTransformers

This is the third version of the monolingual encoder-decoder language model released by the HPLT project, specifically designed for German. It is a text-to-text transformer model trained based on the denoising objective, following the NorT5 architecture settings, and using a tokenizer trained with German HPLT data.

Natural Language ProcessingPytorchMultiple Languages

The Impresso NER model is a multilingual named entity recognition model specifically designed for historical document processing. Based on the stacked Transformer architecture, it can identify fine-grained and coarse-grained entity types in digitized historical texts, including person names, titles, locations, etc. The model is optimized for OCR noise, spelling variations, and non-standard language usage in historical documents.

Natural Language ProcessingTransformersMultiple Languages

NVIDIA-Nemotron-Nano-VL-12B-V2-FP4-QAD is an autoregressive vision-language model launched by NVIDIA. Based on an optimized Transformer architecture, it can handle both image and text inputs simultaneously. The model uses FP4 quantization technology, which significantly reduces the model size and inference cost while maintaining performance, and is suitable for various multimodal application scenarios.

MultimodalTransformers

NVIDIA-Nemotron-Nano-VL-12B-V2-FP8 is a quantized vision-language model launched by NVIDIA, which adopts an optimized Transformer architecture and has undergone three-stage training on commercial images. This model supports single-image inference, has multilingual and multimodal processing capabilities, and is suitable for various scenarios such as image summarization and text-image analysis.

MultimodalTransformers

The INL architecture is a production-level neural architecture based on integral neuron dynamics, replacing traditional feed-forward network layers with iterative dynamics. It is suitable for various types of models such as large language models, vision transformers, and multi-modal models.

Natural Language ProcessingSafetensorsEnglish

LibreFLUX-ControlNet is a text-to-image generation model based on the ControlNet architecture, using LibreFLUX as the base Transformer model. This model is trained on the SA1B dataset and can generate high-quality image content based on text prompts and control images.

Computer VisionDiffusers

This is a Transformers model published on the Hugging Face model hub. Specific information needs to be supplemented by the model author. This model is based on the Transformer architecture and can be used for various natural language processing tasks.

Natural Language ProcessingTransformers

AHN is an innovative neural network architecture for efficient long context modeling. By converting lossless memory into a fixed-size compressed representation, it combines the advantages of Transformer and RNN, achieving efficient computation and accurate prediction in long sequence processing.

Natural Language ProcessingTransformersEnglish

This is a Transformer model published on the Hugging Face model hub. Specific information needs to be obtained from the model page. This model is based on the Transformer architecture and can be used for natural language processing-related tasks.

Natural Language ProcessingTransformers

IBM Granite-4.0-H-Tiny is a hybrid Mamba-2/Transformer model optimized for Apple silicon chips, using 3-bit quantization technology, and is designed for long context, efficient inference, and enterprise use. This model combines the Mamba-2 architecture with the Mixture of Experts (MoE) technology, significantly reducing memory usage while maintaining expressiveness.

Natural Language ProcessingMlxMultiple Languages

OpenLLaMA 3B v2 is an open-source large language model based on the Transformer architecture, with 3 billion parameters. This model uses the MIT license and is mainly used for English text generation tasks, supporting various application scenarios such as chatbots.

Natural Language ProcessingPeftEnglish