diff --git a/README.md b/README.md
index ef7357a1..6a8e4f75 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,7 @@ Flux v0.13 is the latest right now, marked with ☀️; models upgraded to use
   * [ConvMixer "Patches are all you need?"](vision/convmixer_cifar10/) ☀️ v0.13
 
 **Text**
-* [CharRNN](text/char-rnn) ☀️ v0.13.9
+* [CharRNN](text/char-rnn) ☀️ v0.13 +
 * [Character-level language detection](text/lang-detection) ⛅️ v0.11
 * [Seq2Seq phoneme detection on CMUDict](text/phonemes) ⛅️ v0.11
 * [Recursive net on IMDB sentiment treebank](text/treebank) ⛅️ v0.11
diff --git a/text/char-rnn/Manifest.toml b/text/char-rnn/Manifest.toml
index 6d60fd26..ee67b2c5 100644
--- a/text/char-rnn/Manifest.toml
+++ b/text/char-rnn/Manifest.toml
@@ -1,404 +1,673 @@
 # This file is machine-generated - editing it directly is not advised
 
-[[AbstractFFTs]]
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+
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-[[Libdl]]
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-[[Logging]]
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+[[deps.MbedTLS_jll]]
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-[[Missings]]
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-[[Mmap]]
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-[[NNlib]]
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+[[deps.MozillaCACerts_jll]]
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-[[OpenSpecFun_jll]]
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-[[Pkg]]
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+[[deps.Pkg]]
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-[[Printf]]
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-[[Profile]]
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+[[deps.ProgressLogging]]
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-[[REPL]]
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+[[deps.REPL]]
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 uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
 
-[[Random]]
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+[[deps.Random]]
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-[[Reexport]]
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+[[deps.Random123]]
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+[[deps.RealDot]]
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-[[Requires]]
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-[[SHA]]
+[[deps.SHA]]
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-[[Scratch]]
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-
-[[Serialization]]
+[[deps.Serialization]]
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-[[SharedArrays]]
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+[[deps.Setfield]]
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-[[Sockets]]
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-[[SortingAlgorithms]]
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+[[deps.SortingAlgorithms]]
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-[[SparseArrays]]
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-[[SpecialFunctions]]
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-git-tree-sha1 = "75394dbe2bd346beeed750fb02baa6445487b862"
+[[deps.SpecialFunctions]]
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-[[StaticArrays]]
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+[[deps.SplittablesBase]]
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-[[Statistics]]
+[[deps.Statistics]]
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-[[StatsBase]]
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-git-tree-sha1 = "7bab7d4eb46b225b35179632852b595a3162cb61"
+[[deps.StatsAPI]]
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+[[deps.StructArrays]]
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+
+[[deps.TableTraits]]
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+[[deps.Tar]]
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-[[TimerOutputs]]
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+[[deps.TimerOutputs]]
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-[[TranscodingStreams]]
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+[[deps.Transducers]]
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-[[UUIDs]]
+[[deps.UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
-[[UnPack]]
-git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
-uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
-version = "1.0.2"
-
-[[Unicode]]
+[[deps.Unicode]]
 uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
 
-[[ZipFile]]
-deps = ["Libdl", "Printf", "Zlib_jll"]
-git-tree-sha1 = "c3a5637e27e914a7a445b8d0ad063d701931e9f7"
-uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
-version = "0.9.3"
-
-[[Zlib_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
-git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
+[[deps.Zlib_jll]]
+deps = ["Libdl"]
 uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.11+18"
+version = "1.2.12+3"
 
-[[Zygote]]
-deps = ["AbstractFFTs", "ChainRules", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"]
-git-tree-sha1 = "52032f3eb3bf383df34f5455c031457632e8c6d4"
+[[deps.Zygote]]
+deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"]
+git-tree-sha1 = "60c4588669caf2a23363e0b191d3a459773e91b4"
 uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
-version = "0.6.1"
+version = "0.6.53"
 
-[[ZygoteRules]]
+[[deps.ZygoteRules]]
 deps = ["MacroTools"]
-git-tree-sha1 = "9e7a1e8ca60b742e508a315c17eef5211e7fbfd7"
+git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
 uuid = "700de1a5-db45-46bc-99cf-38207098b444"
-version = "0.2.1"
+version = "0.2.2"
+
+[[deps.libblastrampoline_jll]]
+deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
+uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
+version = "5.1.1+0"
+
+[[deps.nghttp2_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
+version = "1.48.0+0"
+
+[[deps.p7zip_jll]]
+deps = ["Artifacts", "Libdl"]
+uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
+version = "17.4.0+0"
diff --git a/text/char-rnn/Project.toml b/text/char-rnn/Project.toml
index 3eaad834..50eded9f 100644
--- a/text/char-rnn/Project.toml
+++ b/text/char-rnn/Project.toml
@@ -1,6 +1,6 @@
 [deps]
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-Parameters = "d96e819e-fc66-5662-9728-84c9c7592b0a"
+OneHotArrays = "0b1bfda6-eb8a-41d2-88d8-f5af5cad476f"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]
diff --git a/text/char-rnn/char-rnn.jl b/text/char-rnn/char-rnn.jl
index 354ee483..024d9a62 100644
--- a/text/char-rnn/char-rnn.jl
+++ b/text/char-rnn/char-rnn.jl
@@ -26,15 +26,15 @@
 # To run this example, we need the following packages:
 
 using Flux
-using Flux: onehot, chunk, batchseq, throttle, logitcrossentropy
+using Flux: chunk, batchseq, logitcrossentropy
+using OneHotArrays
 using StatsBase: wsample
 using Base.Iterators: partition
-using Parameters: @with_kw
 using Random: shuffle
 
 # We set default values for the hyperparameters:
 
-@with_kw mutable struct Args
+Base.@kwdef mutable struct Args
     lr::Float64 = 1e-2	       # Learning rate
     seqlen::Int = 50	       # Length of batch sequences
     batchsz::Int = 50	       # Number of sequences in each batch
@@ -49,7 +49,7 @@ end
 # for training the model:
 
 
-function getdata(args)
+function getdata(args::Args)
     ## Download the data if not downloaded as 'input.txt'
     isfile("input.txt") || download(
         "https://cs.stanford.edu/people/karpathy/char-rnn/shakespeare_input.txt",
@@ -87,11 +87,11 @@ end
 
 # We create the RNN with two Flux’s LSTM layers and an output layer of the size of the alphabet:
 
-function build_model(N)
+function build_model(N::Int)
     return Chain(
-            LSTM(N, 128),
-            LSTM(128, 128),
-            Dense(128, N))
+            LSTM(N => 128),
+            LSTM(128 => 128),
+            Dense(128 => N))
 end 
 
 # The size of the input and output layers is the same as the size of the alphabet. 
@@ -124,30 +124,29 @@ function train(; kws...)
     trainX, trainY, testX, testY = device.((trainX, trainY, testX, testY))
 
     ## Constructing Model
-    m = build_model(N) |> device
+    model = build_model(N) |> device
 
-    function loss(xs, ys)
+    function loss(m, xs, ys)
         Flux.reset!(m)
         return sum(logitcrossentropy.([m(x) for x in xs], ys))
     end
     
     ## Training
-    opt = ADAM(args.lr)
+    opt_state = Flux.setup(Adam(args.lr), model)
 
-    @info "Start Training, total $(args.epochs) epochs"
     for epoch = 1:args.epochs
-        @info "Epoch $(epoch) / $(args.epochs)"
+        @info "Training, epoch $(epoch) / $(args.epochs)"
         Flux.train!(
             loss,
-            Flux.params(m),
+            model,
             zip(trainX, trainY),
-            opt
+            opt_state
         )
         
         ## Show loss-per-character over the test set
-        @show sum(loss.(testX, testY)) / (args.batchsz * args.seqlen * length(testX))
+        @show sum(loss.(Ref(model), testX, testY)) / (args.batchsz * args.seqlen * length(testX))
     end
-    return m, alphabet
+    return model, alphabet
 end
 
 # The function `train` performs the following tasks:
@@ -160,8 +159,6 @@ end
 # * Sets the [ADAM optimiser](https://fluxml.ai/Flux.jl/stable/training/optimisers/#Flux.Optimise.RADAM) with the learning rate *lr* we defined above.
 # * Creates a [callback](https://fluxml.ai/Flux.jl/stable/training/training/#Callbacks) *evalcb* so that you can observe the training process (print the loss value).
 # * Runs the training loop using [Flux’s train!](https://fluxml.ai/Flux.jl/stable/training/training/#Flux.Optimise.train!). 
-# It uses the function [throttle](https://fluxml.ai/Flux.jl/stable/utilities/#Flux.throttle) so that the callback *evalcb* 
-# can only be triggered at most once during timeout seconds (as defined above).
 
 # ## Test the model