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AI Models 84759

  • SRA-RiskGate-4B-i1-GGUF↗

    mradermacher

  • TripleMerge-7B-Ties-GGUF↗

    mradermacher

  • Virtuoso-Lite-i1-GGUF↗

    mradermacher

  • Aryabhata-1.0-GGUF↗

    Mungert

  • hindi_gpt2↗

    NebulaByte

  • nomic-embed-text-v1-unsupervised↗

    nomic-ai

  • Qwen-Image-Edit-NVPCB-OVSL2SL↗

    NVIDIA

  • Mapperatorinator-v32-mini↗

    OliBomby

  • clip-vit-base-patch16↗

    OpenAI

    Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found here. The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within. The base model uses a ViT-B/16 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.

  • InternVL2_5-4B↗

    OpenGVLab

    We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.