---
title: WindFM-Tokenizer-robust
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/WindFM-Tokenizer-robust"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/WindFM-Tokenizer-robust.md"
repository: AI-ModelScope/WindFM-Tokenizer-robust
last_updated: 2026-04-21
license: mit
parameters: 4.0M
tensor_type:
  - F32
  - I64
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 16
stars: 0
tags:
  - "Wind Power"
---

# WindFM-Tokenizer-robust

> WindFM-Tokenizer-robust - AI-ModelScope 在 ModelScope 开源的模型。WindFM-Tokenizer-robust

AI-ModelScope/WindFM-Tokenizer-robust 是 ModelScope 魔搭社区上的 4.0M 参数机器学习模型，采用 mit 许可。

- **Repository**: AI-ModelScope/WindFM-Tokenizer-robust
- **License**: mit
- **Parameters**: 4.0M
- **Tags**: Wind Power
- **Downloads**: 16
- **Stars**: 0
- **Last updated**: 2026-04-21

Source: https://www.modelscope.cn/models/AI-ModelScope/WindFM-Tokenizer-robust

---

# WindFM-Tokenizer-robust

This repository contains the model weights for `WindFM-Tokenizer-robust`.

Regarding how to use this model, please refer to this GitHub [repository](https://github.com/shiyu-coder/WindFM).

The model supports prediction when some covariates are missing. When a covariate is missing, set all values of that variable to 0.