diff --git a/src/mcmc/Inference.jl b/src/mcmc/Inference.jl
index f38220201..7a12f4877 100644
--- a/src/mcmc/Inference.jl
+++ b/src/mcmc/Inference.jl
@@ -767,13 +767,13 @@ julia> [first(t.θ.x) for t in transitions] # extract samples for `x`
  [-1.704630494695469]
 ```
 """
-function transitions_from_chain(model::Turing.Model, chain::MCMCChains.Chains; kwargs...)
+function transitions_from_chain(model::DynamicPPL.Model, chain::MCMCChains.Chains; kwargs...)
     return transitions_from_chain(Random.default_rng(), model, chain; kwargs...)
 end
 
 function transitions_from_chain(
     rng::Random.AbstractRNG,
-    model::Turing.Model,
+    model::DynamicPPL.Model,
     chain::MCMCChains.Chains;
     sampler=DynamicPPL.SampleFromPrior(),
 )
diff --git a/test/mcmc/abstractmcmc.jl b/test/mcmc/abstractmcmc.jl
index 4de63aa94..50334fc51 100644
--- a/test/mcmc/abstractmcmc.jl
+++ b/test/mcmc/abstractmcmc.jl
@@ -18,7 +18,7 @@ using Test: @test, @test_throws, @testset
 using Turing
 using Turing.Inference: AdvancedHMC
 
-function initialize_nuts(model::Turing.Model)
+function initialize_nuts(model::DynamicPPL.Model)
     # Create a log-density function with an implementation of the
     # gradient so we ensure that we're using the same AD backend as in Turing.
     f = LogDensityProblemsAD.ADgradient(DynamicPPL.LogDensityFunction(model))