sub38-197
mansoor2917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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詳細を見る ↗DISCOVER & COMPARE
AIモデルを探す 85001
sub38-211
mansoor2917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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詳細を見る ↗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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詳細を見る ↗bge-fr-en
manuThis is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
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詳細を見る ↗llama-wikitext
manu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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詳細を見る ↗Qwen3.8-27B-GGUF
manubc33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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詳細を見る ↗FactCC
manueldeprada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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詳細を見る ↗WhiteRabbitNeo-33B-v1.5-GGUF
manuelgutierrezThis repository contains the GGUF conversion of the WhiteRabbitNeo-33B-v1.5 model. **Creator:** WhiteRabbitNeo **Original Repository:** WhiteRabbitNeo-33B-v1.5 > WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
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詳細を見る ↗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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詳細を見る ↗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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詳細を見る ↗aster-live-transformer
manus4oHER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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詳細を見る ↗Qwen-Image-2.1-Uncensored-GGUF
Manusagents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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入力 テキスト→ 出力 画像
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詳細を見る ↗mica-qwen3.8-flash-next-q4
manuuuel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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詳細を見る ↗SpatialLM-Llama-1B
manycore-researchSpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding. It enhances spatial reasoning capabilities for applications in embodied robotics, autonomous navigation, and other complex 3D scene analysis tasks. SpatialLM reconstructs 3D layout from a monocular RGB video with MASt3R-SLAM. Results aligned to video with GT cameras for visualization. In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.
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詳細を見る ↗SpatialLM-Qwen-0.5B
manycore-researchSpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding. It enhances spatial reasoning capabilities for applications in embodied robotics, autonomous navigation, and other complex 3D scene analysis tasks. SpatialLM reconstructs 3D layout from a monocular RGB video with MASt3R-SLAM. Results aligned to video with GT cameras for visualization. In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.
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詳細を見る ↗SpatialLM1.1-Llama-1B
manycore-researchSpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding. It enhances spatial reasoning capabilities for applications in embodied robotics, autonomous navigation, and other complex 3D scene analysis tasks. SpatialLM reconstructs 3D layout from a monocular RGB video with MASt3R-SLAM. Results aligned to video with GT cameras for visualization. In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.
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詳細を見る ↗SpatialLM1.1-Qwen-0.5B
manycore-researchSpatialLM is a 3D large language model designed to process 3D point cloud data and generate structured 3D scene understanding outputs. These outputs include architectural elements like walls, doors, windows, and oriented object bounding boxes with their semantic categories. Unlike previous methods that require specialized equipment for data collection, SpatialLM can handle point clouds from diverse sources such as monocular video sequences, RGBD images, and LiDAR sensors. This multimodal architecture effectively bridges the gap between unstructured 3D geometric data and structured 3D representations, offering high-level semantic understanding. It enhances spatial reasoning capabilities for applications in embodied robotics, autonomous navigation, and other complex 3D scene analysis tasks. SpatialLM reconstructs 3D layout from a monocular RGB video with MASt3R-SLAM. Results aligned to video with GT cameras for visualization. In the current version of SpatialLM, input point clouds are considered axis-aligned where the z-axis is the up axis. This orientation is crucial for maintaining consistency in spatial understanding and scene interpretation across different datasets and applications. Example preprocessed point clouds, reconstructed from RGB videos using MASt3R-SLAM, are available in SpatialLM-Testset.
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詳細を見る ↗maobailing_rvc
maotianchen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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詳細を見る ↗decider-0.8b
MapikaThe smallest decider: a language model that does not generate text. It reads a **state** (a string or any JSON value) and a set of **typed questions** and returns a probability distribution for every question from a single forward pass: Choice (2-255 options, optionally described), Score (2-10 described levels), Noul (probability of yes). No decoding, no parsing, no output outside the options you defined. Same code, same wire format (`POST /v1/systemone`, TypeSafe Jev's format), same training recipe as the 2B: one epoch of `scripts/train.sh full` from `Qwen/Qwen3.5-0.8B-Base` over the full mixture (1.47M examples, 455M tokens, 4.5 h on one GH200). All six repositories share one interface (`decider.infer.Decider`, `POST /v1/systemone` in TypeSafe's format) and one readout: the letter logits at an answer slot, softmaxed over the options. Pick by size and input. Code, data registry, training scripts, the changelog and the per-version history: https://github.com/Mapika/decider.
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詳細を見る ↗decider-12b
Mapika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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詳細を見る ↗decider-2b
MapikaA language model that does not generate text. It reads a state and one or more typed questions, each with an explicit option list, and returns a probability distribution over the options for every question from one forward pass. It is called from software, not chatted with. From v12 the state may include images. **This repository holds v12 (2026-10-20).** v11 stays available under the Hub tag `v11`, with its own card; v10 under `v10`. - **Weights:** the text model at the repository root (Qwen3.5-2B-Base architecture, bf16, 3.8 GB), loaded as before by `decider.infer.Decider`, `decider.serve` and the GGUF builds. `vision/` holds the image part: the vision tower of Qwen3.5-2B-Base (unchanged, 0.6 GB) and the config of the image-text model. The image path reuses the text model's weights. - **Code:** decider-ai 1.10.0 for images; text works with 1.4.0 and later.
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詳細を見る ↗decider-2b-GGUF
MapikaGGUF files of Mapika/decider-2b v12, for llama.cpp. decider-2b does not generate text. It reads a state and one or more questions, each with an explicit option list, and returns a probability for every option from one forward pass. See the decider-2b card for what the model is, how it was trained, and where it is weak. **This repository holds the v12 files (2026-10-20).** The v11 files stay available under the Hub tag `v11`. The GGUF files hold the text model only: image input needs the PyTorch weights of Mapika/decider-2b and decider-ai 1.10.0. `decider_config.json` reads yes/no (Noul) answers at temperature 0.05, as the model's own config does (see its card); `decide_gguf.py` reads every question as Choice, at the Choice temperature. The tokenizer, `decider_config.json` (temperatures) and `decide_gguf.py` (the readout on llama.cpp) are in this repository too.
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詳細を見る ↗decider-2b-vision
Mapika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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詳細を見る ↗decider-31b
Mapika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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詳細を見る ↗該当するモデルがありません
提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
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モデル選びに、費用の視点も。
月間トークン数を調整してAPI費用を比較。
1 M = 100万トークン・米ドル
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同一チャネルの基本テキスト料金を比較
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
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API料金はどう比較しますか？+
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
オープンウェイトは無料で商用利用できますか？+
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
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順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。