Wan2.1-I2V-14B-480P
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Wan2.1-T2V-14B
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View details ↗Wan2.1BasedModels
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View details ↗Wan2.2BasedModels
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View details ↗mango-orcasaq2-cyber-27b
isilicon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mango-qwen27-cyber-iq4xs-mtp
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View details ↗mango-qwen27-orca-bf16
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View details ↗mango-qwen38-27b-nvfp4mtp
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View details ↗mango-qwen38-flashnext-iq4xs
isilicon基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-27B-ByteShape-IQ4_XS-ASCII-GGUF
islamsidratul基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗autotrain-h21ao-zct4v
islamytchaev基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗rubaistt_v2_medium
islomovClassic Whisper medium model fine-tuned for Uzbek language. The dataset included of diverse audio: publicly available podcasts, Tashkent dialect podcasts, news, google fleurs, USC and Common Voice 17. Data quality was mixed with 50% human transcribed and 50% pseudo-transcribed using Gemini 2.5 Pro. Difference between v1 is that v2 is fully open-sourced. Due to some conflicts with data partners, v1 was removed, and the 500-hour dataset was excluded. Instead, new and different datasets were included—all of which will be open-sourced. Training scripts will also be open-sourced. The entire process will be fully repeatable. Special attention was given to Tashkent dialect audio materials, resulting in strong performance on this dialect. Future versions will include other regional dialects to improve overall coverage.
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View details ↗modernbert-ner-conll2003
IsmaelMousa基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗MiniMax-H3
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View details ↗This model is a fine-tuned version of microsoft/deberta-v3-base on the ai4privacy/pii-masking-200k dataset. This model has been finetuned on the World's largest open source privacy dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs.
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View details ↗This model is a fine-tuned version of distilbert-base-german-cased on the German subset of pii-masking-200k dataset. It achieves the following results on the evaluation set: - Loss: 0.0821 - Overall Precision: 0.9086 - Overall Recall: 0.9379 - Overall F1: 0.9230 - Overall Accuracy: 0.9679 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 5
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View details ↗distilbert_finetuned_ai4privacy
Isotonic基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗This model is a fine-tuned version of distilbert-base-uncased on the English Subset of ai4privacy/pii-masking-200k dataset. The purpose of the trained models is to remove personally identifiable information (PII) from text, especially in the context of AI assistants and LLMs. The example texts have 54 PII classes (types of sensitive data), targeting 229 discussion subjects / use cases split across business, education, psychology and legal fields, and 5 interactions styles (e.g. casual conversation, formal document, emails etc...).
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View details ↗TinyMixtral-4x248M-MoE
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View details ↗deepseek-vl2-tiny
Isotr0pyIntroducing DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL. DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Zhiyu Wu*, Xiaokang Chen*, Zizheng Pan*, Xingchao Liu*, Wen Liu**, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan*** (* Equal Contribution, ** Project Lead, *** Corresponding author) On the basis of `Python >= 3.8` environment, install the necessary dependencies by running the following command:
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View details ↗test-gguf-sample
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