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AI モデル 85003

• icefog72_-_IceLemonTeaRP-32k-7b-gguf↗

  RichardErkhov

• Rakuten_-_RakutenAI-7B-instruct-gguf↗

  RichardErkhov

• sot-phase1-wikitext103-sot_compact-seed123-tc017↗

  sbartlett97

• Llama-3-Taiwan-70B-Instruct-GGUF↗

  second-state

• all-MiniLM-L6-v2-BE↗

  taronaeo

• bart-base-sled↗

  tau

• mt5-cpe-kmutt-thai-sentence-sum↗

  thanathorn

  This repository contains the finetuned mT5-base model for Thai sentence summarization. The architecture of the model is based on mT5 model and fine-tuned on text-summarization pairs in Thai. Also, this project is a Senior Project of Computer Engineering Student at King Mongkut’s University of Technology Thonburi. You can use this model for Thai sentence text summarization. Not intended to use with paragraph text.

• DaringFortitude-GGUF↗

  TheBloke

  GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

• LLaMA-65B-GGUF↗

  TheBloke

• vit_base_patch16_384.augreg_in21k_ft_in1k↗

  timm

  A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 86.9 - GMACs: 49.4 - Activations (M): 48.3 - Image size: 384 x 384 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.