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gi111f11cb7f354a74a8c123f5b155498e / CharacterGLM-6B_a14114209229434880147986
Python 0 0
CharacterGLM-6B
Updated 2024-12-30 18:47:54 +08:00
pice35408784b54431987c4d13c457b9cd / wd-swinv2-tagger-v3-hf_a14114833405964288278588
Python 0 0
WD SwinV2 Tagger v3 with transformers
Updated 2024-12-30 17:46:31 +08:00
pice35408784b54431987c4d13c457b9cd / Birds-Classifier-EfficientNetB2_a14114516198354944962191
0 0
该模型是 Kaggle 上提供的 gpiosenka/100-bird-species 数据集上 google/efficientnet-b2 的微调版本。
Updated 2024-12-30 17:02:50 +08:00
gi111f11cb7f354a74a8c123f5b155498e / XuanYuan-6B-Chat_a14114185833476096175406
0 0
XuanYuan-6B-Chat
Updated 2024-12-30 16:40:15 +08:00
pice35408784b54431987c4d13c457b9cd / clip-ViT-B-32-vision_a14114379172933632101472
0 0
此模型旨在用于图像分类和相似性搜索。
Updated 2024-12-30 16:24:20 +08:00
gi111f11cb7f354a74a8c123f5b155498e / ModernBERT-large_a14113111107956736838540
0 0
ModernBERT-large
Updated 2024-12-30 14:36:50 +08:00
gi111f11cb7f354a74a8c123f5b155498e / DeepSeek-V2-Lite-Chat_a14111100400955392400197
Python 0 0
DeepSeek-V2-Lite-Chat
Updated 2024-12-30 14:30:24 +08:00
gi111f11cb7f354a74a8c123f5b155498e / CosyVoice2-0.5B_a14111524751798272537811
0 0
CosyVoice2-0.5B
Updated 2024-12-30 13:32:09 +08:00
gi111f11cb7f354a74a8c123f5b155498e / jina-clip-v2_a14110905162133504646628
Python 0 0
jina-clip-v2
Updated 2024-12-30 11:08:34 +08:00
gi111f11cb7f354a74a8c123f5b155498e / DeepSeek-V2-Lite_a14111090808582144290247
0 0
DeepSeek-V2-Lite
Updated 2024-12-30 09:18:00 +08:00
gi111f11cb7f354a74a8c123f5b155498e / SAIL-VL-2B_a14080666861039616457885
Python 0 0
SAIL-VL-2B
Updated 2024-12-27 20:56:49 +08:00
gi111f11cb7f354a74a8c123f5b155498e / CogAgent-9B-2024122_a14079275235536896916920
Python 0 0
CogAgent-9B-2024122
Updated 2024-12-27 19:03:41 +08:00
pice35408784b54431987c4d13c457b9cd / vit-with-attentions_a14081023549763584250409
0 0
带有注意力机制的视觉 Transformer（ViT）
Updated 2024-12-27 17:46:29 +08:00
pice35408784b54431987c4d13c457b9cd / vit-base-patch16-224-in21k-finetuned-cifar10_a14080813824471040829501
0 0
此模型是 cifar10 数据集上 google/vit-base-patch16-224-in21k 的微调版本。
Updated 2024-12-27 17:29:35 +08:00
gi111f11cb7f354a74a8c123f5b155498e / granite_3.1_8b_instruct_a14079171131863040343094
Text 0 0
granite-3.1-8b-instruct
Updated 2024-12-27 15:02:34 +08:00
gi111f11cb7f354a74a8c123f5b155498e / whisper_large_v3_turbo_a14066698510921728889828
Text 0 0
whisper_large_v3_turbo
Updated 2024-12-27 13:10:30 +08:00
gi111f11cb7f354a74a8c123f5b155498e / Ovis1.6-Gemma2-9B_a14066570331942912351439
Python 0 0
Ovis1.6-Gemma2-9B
Updated 2024-12-26 17:50:52 +08:00
gi111f11cb7f354a74a8c123f5b155498e / mPLUG-Owl3-2B-241014_a14065968674238464724418
Python 0 0
mPLUG-Owl3-2B-241014
Updated 2024-12-26 15:01:15 +08:00
pice35408784b54431987c4d13c457b9cd / beit-base-patch16-224_a14068217198145536486664
0 0
BEiT 模型在 ImageNet-21k（1400 万张图像，21841 个类别）上以自我监督的方式以 224x224 的分辨率进行预训练，并在 ImageNet 2012（100 万张图像，1000 个类别）上以 224x224 的分辨率进行微调。
Updated 2024-12-26 14:47:28 +08:00
pice35408784b54431987c4d13c457b9cd / deit-base-distilled-patch16-384_a14067993204617216284856
0 0
在224x224分辨率下预训练并在384x384分辨率下微调的蒸馏数据高效图像Transformer（DeiT）模型，使用ImageNet-1k（100万张图像，1000个类别）进行训练。
Updated 2024-12-26 14:13:43 +08:00