Qwen3-VL-2B-Thinking is one of the most powerful vision-language models in the Qwen series. It uses GGUF format weights and supports efficient inference on devices such as CPUs, NVIDIA GPUs, and Apple Silicon. This model has excellent multimodal understanding and reasoning capabilities, especially enhancing visual perception, spatial understanding, and agent interaction functions.

MultimodalTransformers

NVIDIA Qwen2.5-VL-7B-Instruct-FP4 is a quantized version of Alibaba's Qwen2.5-VL-7B-Instruct model. It adopts an optimized Transformer architecture, supports multimodal inputs (text and images), and is suitable for various AI application scenarios. This model undergoes FP4 quantization through the TensorRT Model Optimizer, providing efficient inference performance on NVIDIA GPUs.

MultimodalSafetensors

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

ESM-2 is a protein language model optimized by NVIDIA based on TransformerEngine, which can predict the 3D structure of proteins from amino acid sequences. This model is trained with a masked language modeling objective and has faster training and inference speeds on NVIDIA GPUs.

Scientific ComputingTransformers

Phi-3 Small is a 7B-parameter lightweight cutting-edge open-source model, optimized for NVIDIA GPUs in ONNX format, supporting 8K context length with strong inference capabilities.

Natural Language ProcessingTransformers