LLM Models
Browse the world's large language models. Compare parameters, benchmarks, VRAM and more.
BaichuanMed-OCR-7B is a model fine-tuned from the Qwen2.5-VL-7B-Instruct with our constructed and curated medical report datasets consists of medical report images and related questions and answers (QAs). It has been specifically adapted to perform Optical Character Recognition (…
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
WARNING: The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).
SEA-LION-VL is an instruct-tuned vision-text model for the Southeast Asia (SEA) region.
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
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A 15B-parameter token-mixer supernet derived from Apriel-1.6 via stochastic distillation. Every decoder layer exposes four trained mixer options —Full Attention, Sliding Window Attention, Gated DeltaNet, and Kimi Delta Attention—enabling flexible architecture selection from a sin…
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
Model Introduction We introduce LongCat-Flash-Omni , a state-of-the-art open-source omni-modal model with 560 billion parameters (with 27B activated), excelling at real-time audio-visual interaction, which is attained by leveraging LongCat-Flash's high-performance Shortcut-connec…
[!NOTE] Note: " -Paddle " models use PaddlePaddle weights, while " -PT " models use Transformer-style PyTorch weights.
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