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View details ↗BFS-Best-Face-Swap
mmrech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-Whittle-MoE-27B-A17.8B
mmRoshani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 ↗Kokoro_GGUF
mmwillet2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 ↗mega-base-wikitext
mnaylorThis is the location on the Hugging Face hub for the Mega MLM checkpoint. I trained this model on the `wikitext-103` dataset using standard BERT-style masked LM pretraining using the original Mega repository and uploaded the weights initially to hf.co/mnaylor/mega-wikitext-103. When the implementation of Mega into Hugging Face's `transformers` is finished, the weights here are designed to be used with `MegaForMaskedLM` and are compatible with the other (encoder-based) `MegaFor*` model classes. This model uses the RoBERTa base tokenizer since the Mega paper does not implement a specific tokenizer aside from the character-level tokenizer used to illustrate long-sequence performance.
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View details ↗genguard-1.0-light
mncai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 ↗nuevamol-135m-reinvent-sft
mnoukhov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 ↗nuevamol-360m-reinvent-sft
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View details ↗nuevamol-46m-reinvent-sft
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View details ↗nuevamol-80m-reinvent-sft
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View details ↗pythia410m-sft-tldr
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View details ↗gemma-4-E2B-it-GGUF
mnsims1024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 ↗Qwen2.5-VL-7B-Instruct-GGUF
mnsims1024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 ↗clt-gemma-2-2b-2.5M
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View details ↗clt-gemma-2-2b-426k
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View details ↗Llama-3.2-3B-Instruct-GGUF
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View details ↗Deepfake-audio-detection
mo-thecreatorThis model is a fine-tuned version of mo-thecreator/wav2vec2-base-finetuned on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0829 - Accuracy: 0.9882 The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5
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View details ↗vit-Facial-Expression-Recognition
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