AutoGLM-Phone-9B-GGUF
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Bespoke-Nimble-9B-v3-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. See https://github.com/ggml-org/llama.cpp/pull/29818 - https://huggingface.co/bespokelabs/Bespoke-Nimble-9B-v3 - https://huggingface.co/Qwen/Qwen3.5-9B > [!NOTE] > The weights are released under CC BY-NC 4.0 (non-commercial use only).
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View details ↗bge-m3-Q8_0-GGUF
ggml-orgThis model was converted to GGUF format from `BAAI/bge-m3` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.
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View details ↗bge-small-en-v1.5-Q8_0-GGUF
ggml-orgThis model was converted to GGUF format from `BAAI/bge-small-en-v1.5` 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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View details ↗Clef-Flash-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. Requires https://github.com/ggml-org/llama.cpp/pull/29831 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Clef-GGUF
ggml-orgThis is a decision model, to be used via `/v1/systemone` API. Requires https://github.com/ggml-org/llama.cpp/pull/29831 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗DeepSeek-OCR-GGUF
ggml-org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 ↗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 ↗DeepSeek-V4-Flash-0731-GGUF
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View details ↗DeepSeek-V4-Flash-GGUF
ggml-org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 ↗dots.ocr-GGUF
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View details ↗dots3-note-prev-GGUF
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View details ↗embeddinggemma-2-GGUF
ggml-org> [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗embeddinggemma-300M-GGUF
ggml-orgThen the endpoint can be accessed at http://localhost:8080/embedding, for example using `curl`: When a model uses pooling, or the pooling method is specified using `--pooling`, the normalization can be controlled by the `embd_normalize` parameter. The default value is `2` which means that the embeddings are normalized using the Euclidean norm (L2). Other options are: * -1 No normalization * 0 Max absolute * 1 Taxicab * 2 Euclidean/L2 * \>2 P-Norm
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View details ↗Then the endpoint can be accessed at http://localhost:8080/embedding, for example using `curl`: When a model uses pooling, or the pooling method is specified using `--pooling`, the normalization can be controlled by the `embd_normalize` parameter. The default value is `2` which means that the embeddings are normalized using the Euclidean norm (L2). Other options are: * -1 No normalization * 0 Max absolute * 1 Taxicab * 2 Euclidean/L2 * \>2 P-Norm
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View details ↗Flint-GGUF
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View details ↗functiongemma-270m-it-GGUF
ggml-org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-3-12b-it-GGUF
ggml-orgGemma 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. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.
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View details ↗gemma-3-12b-it-qat-GGUF
ggml-org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-3-1b-it-GGUF
ggml-org* [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] 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. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size
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View details ↗gemma-3-1b-it-qat-GGUF
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