AutoGLM-Phone-9B-GGUF
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探索 AI 模型 181
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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗DeepSeek-OCR-GGUF
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查看详情 ↗DeepSeek-V4-Flash-0731-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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查看详情 ↗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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查看详情 ↗dots.ocr-GGUF
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查看详情 ↗dots3-note-prev-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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查看详情 ↗embeddinggemma-2-GGUF
ggml-org> [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗Flint-GGUF
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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗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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查看详情 ↗gemma-3-1b-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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。