diff --git a/tutorials/docs-16-external-samplers/Manifest.toml b/tutorials/docs-16-external-samplers/Manifest.toml
deleted file mode 100644
index 6a0fc46ca..000000000
--- a/tutorials/docs-16-external-samplers/Manifest.toml
+++ /dev/null
@@ -1,1466 +0,0 @@
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diff --git a/tutorials/docs-16-external-samplers/Project.toml b/tutorials/docs-16-external-samplers/Project.toml
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--- a/tutorials/docs-16-external-samplers/Project.toml
+++ /dev/null
@@ -1,8 +0,0 @@
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diff --git a/tutorials/docs-16-external-samplers/external-samplers.jmd b/tutorials/docs-16-external-samplers/external-samplers.jmd
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----
-title: Using External Sampler
-permalink: /tutorials/:name/
-mathjax: true
-weave_options:
-  error : false
----
-
-# Using External Samplers
-
-`Turing` provides several wrapped samplers from external sampling libraries, e.g., HMC samplers from `AdvancedHMC`.
-These wrappers allow new users to seamlessly sample statistical models without leaving `Turing`
-However, these wrappers might only sometimes be complete, missing some functionality from the wrapped sampling library.
-Moreover, users might want to use samplers currently not wrapped within `Turing`.
-
-For these reasons, `Turing` also makes running external samplers on Turing models easy without any necessary modifications or wrapping!
-Throughout, we will use a 10-dimensional Neal's funnel as a running example::
-
-```julia
-# Import libraries.
-using Turing, Random, LinearAlgebra
-
-d = 10
-@model function funnel()
-    θ ~ Truncated(Normal(0, 3), -3, 3)
-    z ~ MvNormal(zeros(d - 1), exp(θ) * I)
-    return x ~ MvNormal(z, I)
-end
-```
-
-Now we sample the model to generate some observations, which we can then condition on.
-
-```julia
-(; x) = rand(funnel() | (θ=0,))
-model = funnel() | (; x);
-```
-
-Users can use any sampler algorithm to sample this model if it follows the `AbstractMCMC` API.
-Before discussing how this is done in practice, giving a high-level description of the process is interesting.
-Imagine that we created an instance of an external sampler that we will call `spl` such that `typeof(spl)<:AbstractMCMC.AbstractSampler`.
-In order to avoid type ambiguity within Turing, at the moment it is necessary to declare `spl` as an external sampler to Turing `espl = externalsampler(spl)`, where `externalsampler(s::AbstractMCMC.AbstractSampler)` is a Turing function that types our external sampler adequately.
-
-An excellent point to start to show how this is done in practice is by looking at the sampling library `AdvancedMH` ((`AdvancedMH`'s GitHub)[[https://github.com/TuringLang/AdvancedMH.jl]) for Metropolis-Hastings (MH) methods.
-Let's say we want to use a random walk Metropolis-Hastings sampler without specifying the proposal distributions.
-The code below constructs an MH sampler using a multivariate Gaussian distribution with zero mean and unit variance in `d` dimensions as a random walk proposal.
-
-```julia
-# Importing the sampling library
-using AdvancedMH
-rwmh = AdvancedMH.RWMH(d)
-```
-
-Sampling is then as easy as:
-
-```julia
-chain = sample(model, externalsampler(rwmh), 10_000)
-```
-
-## Going beyond the Turing API
-
-As previously mentioned, the Turing wrappers can often limit the capabilities of the sampling libraries they wrap.
-`AdvancedHMC`[^1] ((`AdvancedHMC`'s GitHub)[https://github.com/TuringLang/AdvancedHMC.jl]) is a clear example of this. A common practice when performing HMC is to provide an initial guess for the mass matrix.
-However, the native HMC sampler within Turing only allows the user to specify the type of the mass matrix despite the two options being possible within `AdvancedHMC`.
-Thankfully, we can use Turing's support for external samplers to define an HMC sampler with a custom mass matrix in `AdvancedHMC` and then use it to sample our Turing model.
-
-We will use the library `Pathfinder`[^2] ((`Pathfinder`'s GitHub)[https://github.com/mlcolab/Pathfinder.jl]) to construct our estimate of mass matrix.
-`Pathfinder` is a variational inference algorithm that first finds the maximum a posteriori (MAP) estimate of a target posterior distribution and then uses the trace of the optimization to construct a sequence of multivariate normal approximations to the target distribution.
-In this process, `Pathfinder` computes an estimate of the mass matrix the user can access.
-
-The code below shows this can be done in practice.
-
-```julia
-using AdvancedHMC, Pathfinder
-# Running pathfinder
-draws = 1_000
-result_multi = multipathfinder(model, draws; nruns=8)
-
-# Estimating the metric
-inv_metric = result_multi.pathfinder_results[1].fit_distribution.Σ
-metric = DenseEuclideanMetric(Matrix(inv_metric))
-
-# Creating an AdvancedHMC NUTS sampler with the custom metric.
-n_adapts = 1000 # Number of adaptation steps
-tap = 0.9 # Large target acceptance probability to deal with the funnel structure of the posterior
-nuts = AdvancedHMC.NUTS(tap; metric=metric)
-
-# Sample
-chain = sample(model, externalsampler(nuts), 10_000; n_adapts=1_000)
-```
-
-## Using new inference methods
-
-So far we have used Turing's support for external samplers to go beyond the capabilities of the wrappers.
-We want to use this support to employ a sampler not supported within Turing's ecosystem yet.
-We will use the recently developed Micro-Cannoncial Hamiltonian Monte Carlo (MCHMC) sampler to showcase this.
-MCHMC[^3,^4] ((MCHMC's GitHub)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl]) is HMC sampler that uses one single Hamiltonian energy level to explore the whole parameter space.
-This is achieved by simulating the dynamics of a microcanonical Hamiltonian with an additional noise term to ensure ergodicity.
-
-Using this as well as other inference methods outside the Turing ecosystem is as simple as executing the code shown below:
-
-```julia
-using MicroCanonicalHMC
-# Create MCHMC sampler
-n_adapts = 1_000 # adaptation steps
-tev = 0.01 # target energy variance
-mchmc = MCHMC(n_adapts, tev; adaptive=true)
-
-# Sample
-chain = sample(model, externalsampler(mchmc), 10_000)
-```
-
-The only requirement to work with `externalsampler` is that the provided `sampler` must implement the AbstractMCMC.jl-interface [INSERT LINK] for a `model` of type `AbstractMCMC.LogDensityModel` [INSERT LINK].
-
-As previously stated, in order to use external sampling libraries within `Turing` they must follow the `AbstractMCMC` API.
-In this section, we will briefly dwell on what this entails.
-First and foremost, the sampler should be a subtype of `AbstractMCMC.AbstractSampler`.
-Second, the stepping function of the MCMC algorithm must be made defined using `AbstractMCMC.step` and follow the structure below:
-
-```
-# First step
-function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}(
-    rng::Random.AbstractRNG,
-    model::AbstractMCMC.LogDensityModel,
-    spl::T;
-    kwargs...,
-)
-    [...]
-    return transition, sample
-end
-
-# N+1 step
-function AbstractMCMC.step{T<:AbstractMCMC.AbstractSampler}(
-    rng::Random.AbstractRNG,
-    model::AbstractMCMC.LogDensityModel,
-    sampler::T,
-    state;
-    kwargs...,
-) 
-    [...]
-    return transition, sample
-end
-```
-
-There are several characteristics to note in these functions:
-
-  - There must be two `step` functions:
-    
-      + A function that performs the first step and initializes the sampler.
-      + A function that performs the following steps and takes an extra input, `state`, which carries the initialization information.
-
-  - The functions must follow the displayed signatures.
-  - The output of the functions must be a transition, the current state of the sampler, and a sample, what is saved to the MCMC chain.
-
-The last requirement is that the transition must be structured with a field `θ` which contains the values of the parameters of the model for said transition.
-This allows `Turing` to seamlessly extract the parameter values at each step of the chain when bundling the chains.
-Note that if the external sampler produces transitions that Turing cannot parse the bundling of the samples will be different or fail.
-
-For practical examples of how to adapt a sampling library to the `AbstractMCMC` interface, the readers can consult the following libraries:
-
-  - (AdvancedMH)[https://github.com/TuringLang/AdvancedMH.jl/blob/458a602ac32a8514a117d4c671396a9ba8acbdab/src/mh-core.jl#L73-L115]
-  - (AdvancedHMC)[https://github.com/TuringLang/AdvancedHMC.jl/blob/762e55f894d142495a41a6eba0eed9201da0a600/src/abstractmcmc.jl#L102-L170]
-  - (MicroCanonicalHMC)[https://github.com/JaimeRZP/MicroCanonicalHMC.jl/blob/master/src/abstractmcmc.jl] within `MicroCanonicalHMC`.
-
-# Refences
-
-[^1]: Xu et al, (AdvancedHMC.jl: A robust, modular and efficient implementation of advanced HMC algorithms)[http://proceedings.mlr.press/v118/xu20a/xu20a.pdf], 2019
-[^2]: Zhang et al, (Pathfinder: Parallel quasi-Newton variational inference)[https://arxiv.org/abs/2108.03782], 2021
-[^3]: Robnik et al, (Microcanonical Hamiltonian Monte Carlo)[https://arxiv.org/abs/2212.08549], 2022
-[^4]: Robnik and Seljak, (Langevine Hamiltonian Monte Carlo)[https://arxiv.org/abs/2303.18221], 2023