I'm not sure you need tidymodels, just tidyverse. Check out this example on StackOverflow: https://stackoverflow.com/a/50005191 Showing reprex below but it's just running code from that post:
library(tidyverse)
d <- data.frame(state=rep(c('NY', 'CA'), c(10, 10)),
year=rep(1:10, 2),
response=c(rnorm(10), rnorm(10)))
fitted_models = d %>%
group_by(state) %>%
nest() %>%
mutate(model = map(data, ~lm(response ~ year, data = .)))
fitted_models
#> # A tibble: 2 x 3
#> # Groups: state [2]
#> state data model
#> <fct> <list<df[,2]>> <list>
#> 1 NY [10 x 2] <lm>
#> 2 CA [10 x 2] <lm>
fitted_models$model
#> [[1]]
#>
#> Call:
#> lm(formula = response ~ year, data = .)
#>
#> Coefficients:
#> (Intercept) year
#> -0.42980 0.07558
#>
#>
#> [[2]]
#>
#> Call:
#> lm(formula = response ~ year, data = .)
#>
#> Coefficients:
#> (Intercept) year
#> 0.07846 0.06735
Created on 2020-02-14 by the reprex package (v0.3.0)