mistralai/Timestral-5M开源了新模型

mistralai/Timestral-5M · Hugging Face Hugging Face (https://huggingface.co/) Models (https://huggingface.co/models) Datasets (https://huggingface.co/datasets) Spaces (https://huggingface.co/spaces) Buckets new (https://huggingface.co/storage) Docs (https://huggingface.co/docs) Enterprise (https://huggingface.co/enterprise) Pricing (https://huggingface.co/pricing) Website Tasks (https://huggingface.co/tasks) HuggingChat (https://huggingface.co/chat) Collections (https://huggingface.co/collections) Languages (https://huggingface.co/languages) Organizations (https://huggingface.co/organizations) Community Blog (https://huggingface.co/blog) Posts (https://huggingface.co/posts) Daily Papers (https://huggingface.co/papers) Hardware (https://huggingface.co/hardware) Learn (https://huggingface.co/learn) Discord (https://huggingface.co/join/discord) Forum (https://discuss.huggingface.co/) GitHub (https://github.com/huggingface) Solutions Team & Enterprise (https://huggingface.co/enterprise) Hugging Face PRO (https://huggingface.co/pro) Enterprise Support (https://huggingface.co/support) Inference Providers (https://huggingface.co/inference/models) Inference Endpoints (https://huggingface.co/inference-endpoints) Storage Buckets (https://huggingface.co/storage) Log In (https://huggingface.co/login) Sign Up (https://huggingface.co/join) (https://huggingface.co/mistralai) mistralai (https://huggingface.co/mistralai) / Timestral-5M (https://huggingface.co/mistralai/Timestral-5M) Like 25 Follow Mistral AI_ 20.1k Time Series Forecasting (https://huggingface.co/models?pipeline_tag=time-series-forecasting) Safetensors (https://huggingface.co/models?library=safetensors) timestral (https://huggingface.co/models?other=timestral) time-series (https://huggingface.co/models?other=time-series) forecasting (https://huggingface.co/models?other=forecasting) foundation-model (https://huggingface.co/models?other=foundation-model) Eval Results (legacy) (https://huggingface.co/models?other=model-index) License: other Model card (https://huggingface.co/mistralai/Timestral-5M) Files Files and versions xet (https://huggingface.co/mistralai/Timestral-5M/tree/main) Community 1 (https://huggingface.co/mistralai/Timestral-5M/discussions) Copy to bucket new Model Details (https://huggingface.co/mistralai/Timestral-5M#model-details) Benchmarks (https://huggingface.co/mistralai/Timestral-5M#benchmarks) Results across the Timestral family (https://huggingface.co/mistralai/Timestral-5M#results-across-the-timestral-family) License (https://huggingface.co/mistralai/Timestral-5M#license) Timestral-5M Timestral is a family of time series foundation models for zero-shot forecasting, available in 5M (https://huggingface.co/mistralai/Timestral-5M) , 28M (https://huggingface.co/mistralai/Timestral-28M) , 123M (https://huggingface.co/mistralai/Timestral-123M) , and 209M (https://huggingface.co/mistralai/Timestral-209M) parameter versions. This repository contains the 5M checkpoint. For more information about our Timestral family, go to the main model page: Timestral-209M (https://huggingface.co/mistralai/Timestral-209M) . (https://huggingface.co/mistralai/Timestral-5M#model-details) Model Details Size: 5M parameters. Architecture: patch-based transformer with time attention and variate attention, 8 layers, embedding dimension 192. Patching: 32 time steps per patch. Each patch is embedded from its values together with their missing-value indicators. Context length: up to 32,768 time steps (1,024 patches). Forecast horizon: Up to 1,024 time steps (32 patches) in a single forward pass. Longer horizons are forecast block by block, with each block's median forecast appended to the context. Outputs: 21 quantiles (0.01, 0.05, 0.1, 0.15, …, 0.85, 0.9, 0.95, 0.99). Inputs: univariate or multivariate target variates, plus past-only and known-future covariates, numeric or categorical (categorical covariates are target-encoded)

Mistral AI mistralai/Timestral-5M mistralai/Timestral-5M https://huggingface.co/mistralai/Timestral-5M