moirai-1.0-R-large
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探索 AI 模型 120
moirai-1.0-R-small
SalesforceMoirai, the Masked Encoder-based Universal Time Series Forecasting Transformer is a Large Time Series Model pre-trained on LOTSA data. For more details on the Moirai architecture, training, and results, please refer to the paper. Fig. 1: Overall architecture of Moirai. Visualized is a 3-variate time series, where variates 0 and 1 are target variables (i.e. to be forecasted, and variate 2 is a dynamic covariate (values in forecast horizon known). Based on a patch size of 64, each variate is patchified into 3 tokens. The patch embeddings along with sequence and variate id are fed into the Transformer. The shaded patches represent the forecast horizon to be forecasted, whose corresponding output representations are mapped into the mixture distribution parameters. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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查看详情 ↗moirai-1.1-R-base
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-base). The new Moirai model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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查看详情 ↗moirai-1.1-R-large
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-large). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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查看详情 ↗moirai-1.1-R-small
SalesforceThis is new updated version of Moirai-1.0-R (https://huggingface.co/Salesforce/moirai-1.0-R-base). The Moirai-1.1-R model achieved significant improvements (~20%) for low-frequency cases like Yearly and Quarterly data in Normalised Mean Absolute Error (NMAE) for 40 datasets on the Monash repository. This release is for research purposes only in support of an academic paper. Our models, datasets, and code are not specifically designed or evaluated for all downstream purposes. We strongly recommend users evaluate and address potential concerns related to accuracy, safety, and fairness before deploying this model. We encourage users to consider the common limitations of AI, comply with applicable laws, and leverage best practices when selecting use cases, particularly for high-risk scenarios where errors or misuse could significantly impact people’s lives, rights, or safety. For further guidance on use cases, refer to our AUP and AI AUP.
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查看详情 ↗moirai-2.0-R-small
SalesforceMoirai 2.0 is a decoder-only universal time series forecasting transformer model pre-trained on: - Subset of GIFT-Eval Pretrain, and Train datasets (Non-leaking historical context). - Mixup data generated from non-leaking subsets of Chronos Dataset. - Synthetic time series produced via KernelSynth introduced in Chronos paper. - Internal Salesforce operational data. We make significant improvements over the first version of Moirai (please refer to the paper for previous version): - Switched from a distributional loss to a quantile loss formulation. - Moved from single-token to multi-token prediction, improving efficiency and stability. - Added a data filtering mechanism to filter out non-forecastable, low quality, time series during pretraining. - Added a new patch token embedding which includes missing value information. - Added patch-level random mask to improve robustness of the model during inference. To perform inference with Moirai 2.0, install the uni2ts library from our GitHub repo.
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Synthefy**Nori** is a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~5.9M - **License:** Apache-2.0 Mean and median R² of the base model across 96 regression tasks from three public benchmark suites (single H200, up to 50K context rows per dataset):
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Synthefy**Nori-100M** is the ~98.3M-parameter variant of Nori, a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~98.3M (98,304,698) - **Architecture:** 42 layers, embed_dim 352, hid_dim 1056, 8 heads - **License:** Apache-2.0 It uses a GPU when one is available and falls back to CPU. A one-shot helper skips the object entirely:
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Synthefy**Nori-30M** is the ~29.2M-parameter variant of Nori, a tabular foundation model for **regression** via in-context learning (ICL). Given a few labeled rows as context, it predicts on new query rows in a **single forward pass**, with no task-specific training or fine-tuning. The model is trained **entirely on synthetic data**. - **Documentation:** https://docs.synthefy.com/nori/ - **Repository:** https://github.com/Synthefy/synthefy-nori - **Library:** `pip install synthefy-nori` - **Checkpoint:** `nori.pt` (this repo) - **Parameters:** ~29.2M - **License:** Apache-2.0 Mean and median R² across 96 regression tasks from three public benchmark suites, on the same protocol as the base Nori:
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theforecastingcompany`t0-alpha` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-alpha` is the first public iteration of the model. You can use `t0` on Retrocast, The Forecasting Company's platform for forecasting on your own data and comparing forecasts across open-weight models.
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theforecastingcompany`t0-beta` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-beta` is the second public iteration of the model. It predicts 21 quantile levels natively, and is an incremental improvement over `t0-alpha`.
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theforecastingcompany`t0-gamma` is an open-weights time-series forecasting foundation model from The Forecasting Company. `t0` is a transformer-based model that produces probabilistic multi-horizon forecasts and natively operates on multiple covariates. `t0-gamma` is the third public iteration of the model. You can use `t0` on Retrocast, The Forecasting Company's platform for forecasting on your own data and comparing forecasts across open-weight models.
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thuml🚩 **News (2025.08)** Sundial has been integrated into Apache IoTDB, a native time-series database. 🚩 **News (2025.05)** Get **1st MASE** on the GIFT-Eval Benchmark. 🚩 **News (2025.02)** Get **1st MSE/MAE** zero-shot performance on Time-Series-Library datasets.
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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。