qwen3ns
huangdffs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 ↗DISCOVER & COMPARE
Explore AI models 84999
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 ↗MythoMax-L2-13b-GGUF
huanhuamengxue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 ↗mv-adapter
huanngzhCreate High-fidelity Multi-view Images with Various Base T2I Models and Various Conditions. MV-Adapter is a creative productivity tool that seamlessly transfer text-to-image models to multi-view generators. Highlights: - 768x768 multi-view images - work well with personalized models (e.g. DreamShaper, Animagine), LCM, ControlNet - support text or image to multi-view (reconstruct 3D thereafter), or with geometry guidance for 3D texture generation - arbitrary view generation
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View details ↗stable-diffusion-2-1-base
huanzi05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 ↗TinyBERT_4L_zh
huawei-noah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 ↗TinyBERT_General_4L_312D
huawei-noahTinyBERT: Distilling BERT for Natural Language Understanding ======== TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transformer distillation on the text from general domain, we obtain a general TinyBERT which provides a good initialization for the task-specific distillation. We here provide the general TinyBERT for your tasks at hand. For more details about the techniques of TinyBERT, refer to our paper: TinyBERT: Distilling BERT for Natural Language Understanding Citation ======== If you find TinyBERT useful in your research, please cite the following paper:
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View details ↗TinyBERT_General_6L_768D
huawei-noah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 ↗GPTFuzz
hubert233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 ↗snac_24khz
hubertsiuzdakMulti-**S**cale **N**eural **A**udio **C**odec (SNAC) compressess audio into discrete codes at a low bitrate. 👉 This model was primarily trained on speech data, and its recommended use case is speech synthesis. See below for other pretrained models. SNAC encodes audio into hierarchical tokens similarly to SoundStream, EnCodec, and DAC. However, SNAC introduces a simple change where coarse tokens are sampled less frequently, covering a broader time span.
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View details ↗snac_32khz
hubertsiuzdak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 ↗snac_44khz
hubertsiuzdak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 ↗SELFormer
HUBioDataLab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 ↗Wan2.2-Remix-I2V-v2.1-GGUF
huchukato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 ↗Chatter-70M
hudsongouge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 ↗minicpm5-1B-GLM-5.2-Agentic-v3
hudsongouge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 ↗minicpm5-1B-GLM-5.2-Agentic-v7
hudsongouge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 ↗minicpm5-1B-GLM-5.2-Agentic-v8
hudsongouge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 ↗minicpm5-1B-GLM-5.2-Agentic-v9
hudsongouge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 ↗nanochat-40M
hudsongouge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 ↗klue-roberta-base-nli
Huffon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 ↗qwen-image-2-1-aoti
hugging-apps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 ↗gemma-2-9b-it-AWQ-INT4
hugging-quants> [!IMPORTANT] > This repository is a community-driven quantized version of the original model `google/gemma-2-9b-it` which is the BF16 half-precision official version released by Google. > [!WARNING] > This model has been quantized using `transformers` 4.45.0, meaning that the tokenizer available in this repository won't be compatible with lower versions. Same applies for e.g. Text Generation Inference (TGI) that only installs `transformers` 4.45.0 or higher starting in v2.3.1. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights for both pre-trained variants and instruction-tuned variants. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as a laptop, desktop or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone.
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View details ↗Llama-3.2-1B-Instruct-Q4_K_M-GGUF
hugging-quantsThis model was converted to GGUF format from `meta-llama/Llama-3.2-1B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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