Olmo-3-7B-Instruct-AIO-GGUF is a GGUF quantized version based on the Olmo-3-7B-Instruct model developed by the Allen Institute for AI. This is an autoregressive language model with 7 billion parameters, trained on datasets such as Tulu 2 and UltraFeedback through supervised fine-tuning and direct preference optimization, and performs excellently in question-answering and instruction following.

Natural Language ProcessingTransformersEnglish

This is a bidirectional autoregressive Transformer model based on the 🤗 Transformers library and has been uploaded to the model hub. The model card is automatically generated by the system, and specific technical details and application information need to be further supplemented.

Natural Language ProcessingTransformers

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

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

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

Ming-UniVision is a large multimodal language model that first integrates continuous visual representations into the next-token prediction framework, unifying vision and language under a single autoregressive paradigm without discrete quantization or modality-specific heads. This model supports joint image understanding and generation, converges faster in vision-language training, and also supports multi-round context visual tasks.

MultimodalTransformersMultiple Languages

The NVIDIA Qwen3-14B FP4 model is a quantized version of the Alibaba Cloud Qwen3-14B model. It adopts an optimized Transformer architecture and is an autoregressive language model. This model uses the TensorRT Model Optimizer for quantization, quantizing weights and activations to the FP4 data type, enabling efficient inference on NVIDIA GPU acceleration systems.

Natural Language ProcessingSafetensors

NVIDIA Qwen3-8B FP8 is a quantized version of the Alibaba Qwen3-8B model. It uses an optimized Transformer architecture and belongs to the autoregressive language model. This model is optimized through FP8 quantization technology and can achieve efficient inference on NVIDIA GPUs. It supports both commercial and non-commercial use.

Natural Language ProcessingSafetensors

The NVIDIA Qwen3-8B FP4 model is a quantized version of Alibaba's Qwen3-8B model. It is an autoregressive language model with an optimized Transformer architecture. This model uses FP4 quantization technology, which significantly reduces memory usage and computational requirements while maintaining performance. It is suitable for applications such as AI agent systems, chatbots, and RAG systems.

Natural Language ProcessingSafetensors

SDAR is a new type of large language model that integrates autoregressive and discrete diffusion modeling strategies, combining the advantages of efficient training of AR models and parallel inference of diffusion models. It performs comparably to SOTA open - source AR models on general tasks and excels in scientific reasoning tasks, becoming the most powerful diffusion language model.

Natural Language ProcessingTransformers

NVIDIA DeepSeek-R1-0528-FP4 v2 is a quantized version of the DeepSeek R1 0528 model. It adopts an optimized Transformer architecture and is an autoregressive language model. Through FP4 quantization optimization, it reduces the disk size and GPU memory requirements while maintaining high inference efficiency.

Natural Language ProcessingSafetensors

PARD is a high-performance speculative decoding method that can convert autoregressive draft models into parallel draft models at low cost, significantly improving the inference speed of large language models and reducing deployment complexity and adaptation costs.

Natural Language ProcessingTransformers

The NVIDIA Qwen3-30B-A3B FP4 model is a quantized version of the Alibaba Cloud Qwen3-30B-A3B model. It adopts an optimized Transformer architecture and is an autoregressive language model. This model uses the TensorRT Model Optimizer for FP4 quantization, reducing the number of bits per parameter from 16 to 4, which reduces the disk size and GPU memory requirements by approximately 3.3 times while maintaining high performance.

Natural Language ProcessingSafetensors

The NVIDIA Qwen3-235B-A22B FP4 model is a quantized version of the Alibaba Cloud Qwen3-235B-A22B model. Based on an optimized Transformer architecture, it is an autoregressive language model. This model uses the TensorRT Model Optimizer for quantization, quantizing weights and activation values to the FP4 data type, significantly reducing storage and computational requirements.

Natural Language ProcessingSafetensors

The NVIDIA Qwen3-235B-A22B FP4 model is a quantized version of the Alibaba Cloud Qwen3-235B-A22B model. It adopts an optimized Transformer architecture and is an autoregressive language model. This model reduces the parameters from 16 bits to 4 bits through FP4 quantization technology, reducing the disk size and GPU memory requirements by approximately 3.3 times while maintaining high accuracy and performance.

Natural Language ProcessingSafetensors

The NVIDIA Qwen3-235B-A22B FP8 model is a quantized version of the Alibaba Cloud Qwen3-235B-A22B model. It adopts an optimized Transformer architecture and is an autoregressive language model. This model significantly reduces disk space and GPU memory requirements through FP8 quantization technology while maintaining high inference accuracy, making it suitable for various AI application scenarios.

Natural Language ProcessingSafetensors

SongBloom is an innovative full-length song generation framework that adopts an interleaved paradigm of autoregressive sketch drawing and diffusion-based refinement, combining the high fidelity of diffusion models and the scalability of language models to generate high-quality full-length songs.

Show-o2 is an improved native unified multimodal model that utilizes autoregressive modeling and flow matching techniques to support unified understanding and generation of text, image, and video modalities.

MultimodalDiffusers

A quantized version of the DeepSeek R1 0528 model from DeepSeek AI, an autoregressive language model based on an optimized Transformer architecture, which can be used for commercial and non-commercial purposes.

Natural Language ProcessingSafetensors

ReasonGen-R1 is an autoregressive image generation model that integrates chain-of-thought reasoning. It enhances the logic and quality of image generation through SFT and RL.

MultimodalTransformers