xlm-roberta-base-finetuned-recipe-all
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View details ↗DISCOVER & COMPARE
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flux-dev-nfsw
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View details ↗jeanne
eeys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 ↗e5-base-multilingual-4096
efedericiBelow is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
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View details ↗Jun-LoRA-12B-GGUF
efficiencyx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 ↗Jun-LoRA-E2B-GGUF
efficiencyx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 ↗Jun-LoRA-E4B-GGUF
efficiencyx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 ↗Fast_dLLM_v2_1.5B
Efficient-Large-Model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 ↗Fast_dLLM_v2_7B
Efficient-Large-Model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 ↗Fast_dVLM_3B
Efficient-Large-Model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-2-2b-it
Efficient-Large-Model* [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma2] 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. Below we share some code snippets on how to get quickly started with running the model. First, install the Transformers library with:
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View details ↗H3-to-LTX-Latent-Adapter
Efficient-Large-ModelAn original BF16 inference adapter that converts normalized MiniMax-H3 video latents into normalized LTX-2.5 Conv VideoVAE latents. It is used by Sol-H3-Spark in Sana. The model applies the released temporal/spatial packing transform and a 194,759,504-parameter Conv3D residual adapter (width 752, 22 blocks). It does not contain MiniMax-H3 or LTX model weights and does not generate video alone. Pass the original pixel-frame count and pixel geometry to the adapter's `convert` method. The inference library in Sana performs the matching packing and normalization. Changing that transform is not interchangeable with this checkpoint.
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View details ↗LongLive-Plug-Wan2.2-TI2V-5B-few-step
Efficient-Large-Model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 ↗NVILA-15B-hf
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View details ↗NVILA-8B-hf
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View details ↗NVILA-Lite-15B-hf
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View details ↗NVILA-Lite-2B-hf
Efficient-Large-Model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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COST EXPLORER
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1 M = 1 million tokens · USD
Compares base text-token rates within the same channel
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MODEL HANDBOOK
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How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
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Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.