diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index 86ea69de99db88512605e6b5d05973f909099d4a..4f2d135eb435a569f53dfd76f21df487ce9b7ecd 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -6,31 +6,40 @@ stages:
 #  - archive
 #  - deploy
 
+.compile_models: &compile_models
+  - xcrun coremlcompiler compile Sources/SwiftNLP/Resources/all-MiniLM-L6-v2.mlpackage/ Sources/SwiftNLP/Models
+  - xcrun coremlcompiler generate Sources/SwiftNLP/Resources/all-MiniLM-L6-v2.mlpackage/ --language Swift Sources/SwiftNLP/Resources
+  - mv Sources/SwiftNLP/Resources/all-MiniLM-L6-v2.swift Sources/SwiftNLP/2.\ Encoding
+  - xcrun coremlcompiler compile Sources/SwiftNLP/Resources/float32_model.mlpackage/ Sources/SwiftNLP/Models
+  - xcrun coremlcompiler generate Sources/SwiftNLP/Resources/float32_model.mlpackage/ --language Swift Sources/SwiftNLP/Resources
+  - mv Sources/SwiftNLP/Resources/float32_model.swift Sources/SwiftNLP/2.\ Encoding
+
 build-macOS:
   stage: build
 
   script:
-    - swift build -c release -Xswiftc -cross-module-optimization -Xlinker -framework -Xlinker CoreML
+    - *compile_models
+    - swift build
   
   tags:
     - macOS
 
 test-macOS:
   stage: test
+
   script:
-     - export SKIP_TESTS=DurableHNSWCorpusTests
-     - swift test -c release -Xswiftc -enable-testing
-#    - swift test --sanitize=address -c release -Xswiftc -enable-testing
-#    - swift test --sanitize=thread -c release -Xswiftc -enable-testing
+    - *compile_models
+    - swift test -c release -Xswiftc -enable-testing
+
   tags:
     - macOS
 
-
 build-linux:
   stage: build
 
   script:
     - swiftly install latest
+    - *compile_models
     - swift build -c release -Xswiftc -cross-module-optimization
   tags:
     - linux
@@ -39,8 +48,10 @@ build-linux:
 
 test-linux:
   stage: test
+
   script:
-     - swift test -c release -Xswiftc -enable-testing
+    - *compile_models
+    - swift test -c release -Xswiftc -enable-testing
 #    - swift test --sanitize=address -c release -Xswiftc -enable-testing
 #    - swift test --sanitize=thread -c release -Xswiftc -enable-testing
   tags:
diff --git a/Package.resolved b/Package.resolved
index 5f2e68ff947b10697835521b7d4554ca87972db2..a7b19d4ff9daa92c318f7b28b6bd8dc35c71d647 100644
--- a/Package.resolved
+++ b/Package.resolved
@@ -5,7 +5,7 @@
       "kind" : "remoteSourceControl",
       "location" : "https://github.com/tannerdsilva/CLMDB.git",
       "state" : {
-        "revision" : "39e2f317c898824777365d5669f6fa7571541893",
+        "revision" : "30d45263c8e512b01ab073e77ebff09039bdc593",
         "version" : "0.9.31"
       }
     },
@@ -27,6 +27,15 @@
         "version" : "2.2.0"
       }
     },
+    {
+      "identity" : "faissmobile",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/jkrukowski/FaissMobile",
+      "state" : {
+        "revision" : "9d5b9925305eea9398cc92ce4a8e51c8a4b043af",
+        "version" : "0.0.1"
+      }
+    },
     {
       "identity" : "similarity-topology",
       "kind" : "remoteSourceControl",
@@ -45,6 +54,24 @@
         "version" : "2.3.2"
       }
     },
+    {
+      "identity" : "swift-argument-parser",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/apple/swift-argument-parser",
+      "state" : {
+        "revision" : "c8ed701b513cf5177118a175d85fbbbcd707ab41",
+        "version" : "1.3.0"
+      }
+    },
+    {
+      "identity" : "swift-log",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/apple/swift-log",
+      "state" : {
+        "revision" : "e97a6fcb1ab07462881ac165fdbb37f067e205d5",
+        "version" : "1.5.4"
+      }
+    },
     {
       "identity" : "swift-numerics",
       "kind" : "remoteSourceControl",
@@ -62,6 +89,42 @@
         "revision" : "0ce7065dd10a2936179220283f8bb039482d237f",
         "version" : "0.5.0"
       }
+    },
+    {
+      "identity" : "swift-syntax",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/apple/swift-syntax.git",
+      "state" : {
+        "revision" : "64889f0c732f210a935a0ad7cda38f77f876262d",
+        "version" : "509.1.1"
+      }
+    },
+    {
+      "identity" : "swiftannoy",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/jbadger3/SwiftAnnoy",
+      "state" : {
+        "revision" : "6d90636e22510c2f0798f9f8ff072109e345750a",
+        "version" : "1.1.0"
+      }
+    },
+    {
+      "identity" : "swiftfaiss",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/jkrukowski/SwiftFaiss.git",
+      "state" : {
+        "revision" : "d3831c1e9898695ae7f680b6353e48e873d3f1d3",
+        "version" : "0.0.8"
+      }
+    },
+    {
+      "identity" : "swiftformat",
+      "kind" : "remoteSourceControl",
+      "location" : "https://github.com/nicklockwood/SwiftFormat",
+      "state" : {
+        "revision" : "dbc9a4406d21cc52f16caf1e299172b097145e5e",
+        "version" : "0.53.3"
+      }
     }
   ],
   "version" : 2
diff --git a/Package.swift b/Package.swift
index f4d6ea7dfc780051cecebc9ea00e172acec62904..a65eec373940f89f8f584736eb1cf2cdbd6080ad 100644
--- a/Package.swift
+++ b/Package.swift
@@ -2,11 +2,12 @@
 // The swift-tools-version declares the minimum version of Swift required to build this package.
 
 import PackageDescription
+import CompilerPluginSupport
 
 let package = Package(
     name: "SwiftNLP",
     platforms: [
-        .macOS(.v13),
+            .macOS(.v13)
     ],
     products: [
         .library(
@@ -20,15 +21,26 @@ let package = Package(
 //        ),
     ],
     dependencies: [
+        .package(url: "https://github.com/jbadger3/SwiftAnnoy", .upToNextMajor(from: "1.0.1")),
+        .package(url: "https://github.com/jkrukowski/SwiftFaiss.git", from: "0.0.7"),
         .package(url: "https://github.com/L1MeN9Yu/Elva", .upToNextMajor(from: "2.1.3")),
-        .package(url: "https://github.com/JadenGeller/similarity-topology", .exact("0.1.14")),
+        .package(url: "https://github.com/JadenGeller/similarity-topology", .upToNextMajor(from: "0.1.14")),
+        .package(url: "https://github.com/apple/swift-syntax", from: "509.0.0"),
         .package(url: "https://github.com/Jounce/Surge.git", .upToNextMajor(from: "2.0.0")),
-//        .package(url: "https://github.com/mingchungx/nifty.git", .branch("master"))
     ],
     targets: [
+        .macro(
+            name: "SwiftNLPGenericLLMMacros",
+            dependencies: [
+                .product(name: "SwiftSyntaxMacros", package: "swift-syntax"),
+                .product(name: "SwiftCompilerPlugin", package: "swift-syntax")
+            ]
+        ),
         .target(
             name: "SwiftNLP",
             dependencies: [
+                "SwiftAnnoy",
+                "SwiftNLPGenericLLMMacros",
                 .product(name: "HNSWAlgorithm", package: "similarity-topology"),
                 .product(name: "HNSWEphemeral", package: "similarity-topology"),
                 .product(name: "HNSWDurable", package: "similarity-topology", condition: .when(platforms: [.macOS])),
@@ -37,17 +49,16 @@ let package = Package(
                 .product(name: "ZSTD", package: "Elva"),
                 .byName(name: "Surge", condition: .when(platforms: [.macOS])),
             ],
-            resources: [.process("Resources")]
+            resources: [
+                .process("Resources/bert_vocab.txt"),
+                .process("Resources/glove.6B.50d.mmap"),
+            ]
         ),
         .testTarget(
             name: "SwiftNLPTests",
-            dependencies: ["SwiftNLP"],
-            resources: [.process("Resources")]
-        ),
-        /// This is commented out to fix the gitlab pipeline, but must be uncommented when in use on macOS only.
-//        .executableTarget(
-//            name: "SwiftNLPVisualizer",
-//            dependencies: ["SwiftNLP"]
-//        ),
+            dependencies: ["SwiftNLPGenericLLMMacros", "SwiftNLP"],
+            resources: [
+                .process("Resources"),
+            ]),
     ]
 )
diff --git a/Sources/SwiftNLP/2. Encoding/CoreMLEncoder.swift b/Sources/SwiftNLP/2. Encoding/CoreMLEncoder.swift
index 96800d39dc9ba6d4fe00a8e16760e060e903a2f0..b98aa739a03e46ac6527f2dec4439309798ffedd 100644
--- a/Sources/SwiftNLP/2. Encoding/CoreMLEncoder.swift	
+++ b/Sources/SwiftNLP/2. Encoding/CoreMLEncoder.swift	
@@ -26,69 +26,99 @@ import Foundation
 import CoreML
 
 
-struct CoreMLEncoder<Scalar: BinaryFloatingPoint>: SNLPEncoder {
+@freestanding(expression)
+public macro MODEL_MAKE_PREDICTION(_ input_name: Any, _ attention_ids: Any, _ output_name: Any) = #externalMacro(
+    module: "SwiftNLPGenericLLMMacros",
+    type: "LLMModelPredictionCases")
+
+@freestanding(expression)
+public macro MODEL_VALIDATE_NAME_AND_SET_INPUT_SIZE() = #externalMacro(
+    module: "SwiftNLPGenericLLMMacros",
+    type: "LLMModelNameValidation")
+
+
+class CoreMLEncoder<Scalar: BinaryFloatingPoint & Codable>: SNLPEncoder {
+    
+    
     
-    var zeroes: [Scalar] = []
-    var dimensions: UInt = 0
+    var zeroes: [Scalar]
+    var dimensions: UInt
+    var model: String
         
+    required init() {
+      zeroes = Array(repeating: Scalar(0), count: 384)
+      dimensions = 384
+      model = "all_MiniLM_L6_v2"
+    }
+    
+    
     func encodeToken(_ token: String) -> [Scalar] {
-        fatalError("CoreMLEncoder not implemented yet. Get on it.")
+        let tokenization = LLMEmbeddings(model_type: self.model).tokenizer.tokenizeToIds(text: token) as! [Scalar]
+        return tokenization
     }
     
     func encodeSentence(_ sentence: String) -> [Scalar] {
-        fatalError("CoreMLEncoder not implemented yet. Get on it.")
+        let encoding = Task {
+            await LLMEmbeddings(model_type: self.model).encode(sentence: sentence)
+        } as! [Scalar]
+        return encoding
     }
 }
 
-//@available(macOS 13.0, *)
-//public class MiniLMEmbeddings {
-//    public let model: all_MiniLM_L6_v2
-//    public let tokenizer: BertTokenizer
-//    public let inputDimention: Int = 512
-//    public let outputDimention: Int = 384
-//
-//    public init() {
-//        let modelConfig = MLModelConfiguration()
-//        modelConfig.computeUnits = .all
-//
-//        do {
-//            self.model = try all_MiniLM_L6_v2(configuration: modelConfig)
-//        } catch {
-//            fatalError("Failed to load the Core ML model. Error: \(error.localizedDescription)")
-//        }
-//
-//        self.tokenizer = BertTokenizer()
-//    }
-//
-//    // MARK: - Dense Embeddings
-//
-//    public func encode(sentence: String) async -> [Float]? {
-//        // Encode input text as bert tokens
-//        let inputTokens = tokenizer.buildModelTokens(sentence: sentence)
-//        let (inputIds, attentionMask) = tokenizer.buildModelInputs(from: inputTokens)
-//
-//        // Send tokens through the MLModel
-//        let embeddings = generateEmbeddings(inputIds: inputIds, attentionMask: attentionMask)
-//
-//        return embeddings
-//    }
-//
-//    public func generateEmbeddings(inputIds: MLMultiArray, attentionMask: MLMultiArray) -> [Float]? {
-//        let inputFeatures = all_MiniLM_L6_v2Input(input_ids: inputIds, attention_mask: attentionMask)
-//        
-//        let output = try? model.prediction(input: inputFeatures)
-//        guard let embeddings = output?.embeddings else {
-//            return nil
-//        }
-//        
-//        var embeddingsArray = [Float]()
-//        for index in 0..<embeddings.count {
-//            let value = embeddings[index].floatValue
-//            embeddingsArray.append(Float(value))
-//        }
-//        
-//        return embeddingsArray
-//    }
-//
-//}
+
+@available(macOS 13.0, *)
+public class LLMEmbeddings {
+    
+    private let model: String
+    public var tokenizer: BertTokenizer
+    public var inputDimention: Int = 512 // 512 is a dummy value, correct value is set by the macro below
+    public let outputDimention: Int = 384
+
+    public init(model_type: String) {
+        let modelConfig = MLModelConfiguration()
+        modelConfig.computeUnits = .all
+
+        self.model = model_type;
+        
+        // dummy initialization needed here to avoid compilation error
+        self.tokenizer = BertTokenizer(maxLen: self.inputDimention)
+        
+        // validate the model type is valid and set the correct input dimension
+        #MODEL_VALIDATE_NAME_AND_SET_INPUT_SIZE()
+        
+        // reinitialize with correct input size
+        self.tokenizer = BertTokenizer(maxLen: self.inputDimention)
+    }
+
+    public func encode(sentence: String) async -> [Float]? {
+        // Encode input text as bert tokens
+        let inputTokens = tokenizer.buildModelTokens(sentence: sentence)
+        let (inputIds, attentionMask) = tokenizer.buildModelInputs(from: inputTokens)
+
+        let embeddings = generateEmbeddings(inputIds: inputIds, attentionMask: attentionMask)
+
+        return embeddings
+    }
+
+    public func generateEmbeddings(inputIds: MLMultiArray, attentionMask: MLMultiArray) -> [Float]? {
+        var output: MLMultiArray? = nil
+        
+        // determine which model to use and generate predictions
+        #MODEL_MAKE_PREDICTION("inputIds", "attentionMask", "output")
+        
+        if (output === nil) {
+            return nil;
+        }
+        
+        let embeddings = output!;
+       
+        var embeddingsArray = [Float]()
+        for index in 0..<embeddings.count {
+            let value = embeddings[index].floatValue
+            embeddingsArray.append(Float(value))
+        }
+       
+        return embeddingsArray
+    }
+}
 #endif
diff --git a/Sources/SwiftNLP/2. Encoding/GenericModel.swift b/Sources/SwiftNLP/2. Encoding/GenericModel.swift
new file mode 100644
index 0000000000000000000000000000000000000000..65ded46bfabfa29aabbe6d6f9b783f0fbd1c3d37
--- /dev/null
+++ b/Sources/SwiftNLP/2. Encoding/GenericModel.swift	
@@ -0,0 +1,12 @@
+import CoreML
+
+
+@freestanding(declaration, names: arbitrary)
+public macro MODEL_PREDICTION_FUNCTIONS() = #externalMacro(
+    module: "SwiftNLPGenericLLMMacros",
+    type: "LLMPredictionFunctions")
+
+
+struct LLMModel {
+    #MODEL_PREDICTION_FUNCTIONS()
+}
diff --git a/Sources/SwiftNLP/2. Encoding/Tokenizers/BertTokenizer.swift b/Sources/SwiftNLP/2. Encoding/Tokenizers/BertTokenizer.swift
index 376cdff5220a49f7a57b60a71979f49db5ae57d2..8c1e01d38fa4b0688eb8cc86c73e2362c0b8f472 100644
--- a/Sources/SwiftNLP/2. Encoding/Tokenizers/BertTokenizer.swift	
+++ b/Sources/SwiftNLP/2. Encoding/Tokenizers/BertTokenizer.swift	
@@ -6,12 +6,12 @@ import CoreML
 public class BertTokenizer {
     private let basicTokenizer = BasicTokenizer()
     private let wordpieceTokenizer: WordpieceTokenizer
-    private let maxLen = 512
+    private var maxLen = 512
 
     private let vocab: [String: Int]
     private let ids_to_tokens: [Int: String]
 
-    public init() {
+    public init(maxLen: Int) {
         let url = Bundle.module.url(forResource: "bert_vocab", withExtension: "txt")!
         let vocabTxt = try! String(contentsOf: url)
         let tokens = vocabTxt.split(separator: "\n").map { String($0) }
@@ -24,6 +24,7 @@ public class BertTokenizer {
         self.vocab = vocab
         self.ids_to_tokens = ids_to_tokens
         self.wordpieceTokenizer = WordpieceTokenizer(vocab: self.vocab)
+        self.maxLen = maxLen
     }
 
     public func buildModelTokens(sentence: String) -> [Int] {
diff --git a/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel
new file mode 100644
index 0000000000000000000000000000000000000000..a23498c8857990e3a536ab50919b8358fe2fd143
--- /dev/null
+++ b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/model.mlmodel
@@ -0,0 +1,11270 @@
+ý
+L
+	input_ids2Indices of input sequence tokens in the vocabulary*	
+€ €
+w
+attention_maskXMask to avoid performing attention on padding token indices (1 = not masked, 0 = masked)*	
+€ €Rj
+last_hidden_stateFSequence of hidden-states at the output of the last layer of the model
*
+€€ €RX
+
pooler_output:Last layer hidden-state of the first token of the sequence*	
+€ €¢ì
+'thenlper/gte-small (feature-extraction)¢3
+#com.github.apple.coremltools.sourcetorch==2.1.0¢+
+$com.github.apple.coremltools.version7.1¢:
++com.github.apple.coremltools.source_dialectTorchScript¢3
+co.huggingface.exporters.namethenlper/gte-small¢3
+co.huggingface.exporters.taskfeature-extraction¢2
+%co.huggingface.exporters.architecture	BertModel¢-
+"co.huggingface.exporters.frameworkpytorch¢-
+"co.huggingface.exporters.precisionfloat32¢
+transformers_version4.28.1²¨²º°
+main°°
+ 
+	input_ids
+
+
+€
+%
+attention_mask
+
+
+€CoreML5Ú¯
+CoreML5Í¯last_hidden_state
pooler_outputÌ
+const@
+'model_embeddings_word_embeddings_weight
+
+ºî
+€*=
+name5
+-
++")
+'model_embeddings_word_embeddings_weight*B
+val;
+
+ºî
+€*"
+@model_path/weights/weight.bin@¯
+const0
+model_embeddings_LayerNorm_bias
+
+€*5
+name-
+%
+#"!
+model_embeddings_LayerNorm_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€¹³
+const2
+!model_embeddings_LayerNorm_weight
+
+€*7
+name/
+'
+%"#
+!model_embeddings_LayerNorm_weight*=
+val6
+
+€*%
+@model_path/weights/weight.binÀÅÏ
+const@
+/model_encoder_layer_0_attention_self_query_bias
+
+€*E
+name=
+5
+3"1
+/model_encoder_layer_0_attention_self_query_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€Òá
+constI
+1model_encoder_layer_0_attention_self_query_weight
+
+€
+€*G
+name?
+7
+5"3
+1model_encoder_layer_0_attention_self_query_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀÞË
+const>
+-model_encoder_layer_0_attention_self_key_bias
+
+€*C
+name;
+3
+1"/
+-model_encoder_layer_0_attention_self_key_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€ßÑÝ
+constG
+/model_encoder_layer_0_attention_self_key_weight
+
+€
+€*E
+name=
+5
+3"1
+/model_encoder_layer_0_attention_self_key_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀëÑÏ
+const@
+/model_encoder_layer_0_attention_self_value_bias
+
+€*E
+name=
+5
+3"1
+/model_encoder_layer_0_attention_self_value_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€ìõá
+constI
+1model_encoder_layer_0_attention_self_value_weight
+
+€
+€*G
+name?
+7
+5"3
+1model_encoder_layer_0_attention_self_value_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀøõÓ
+constB
+1model_encoder_layer_0_attention_output_dense_bias
+
+€*G
+name?
+7
+5"3
+1model_encoder_layer_0_attention_output_dense_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€ù™å
+constK
+3model_encoder_layer_0_attention_output_dense_weight
+
+€
+€*I
+nameA
+9
+7"5
+3model_encoder_layer_0_attention_output_dense_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀ…šÛ
+constF
+5model_encoder_layer_0_attention_output_LayerNorm_bias
+
+€*K
+nameC
+;
+9"7
+5model_encoder_layer_0_attention_output_LayerNorm_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€†¾ß
+constH
+7model_encoder_layer_0_attention_output_LayerNorm_weight
+
+€*M
+nameE
+=
+;"9
+7model_encoder_layer_0_attention_output_LayerNorm_weight*=
+val6
+
+€*%
+@model_path/weights/weight.binÀ’¾Ë
+const>
+-model_encoder_layer_0_intermediate_dense_bias
+
+€*C
+name;
+3
+1"/
+-model_encoder_layer_0_intermediate_dense_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€Ÿ¾Ý
+constG
+/model_encoder_layer_0_intermediate_dense_weight
+
+€
+€*E
+name=
+5
+3"1
+/model_encoder_layer_0_intermediate_dense_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀÏ¾¿
+const8
+'model_encoder_layer_0_output_dense_bias
+
+€*=
+name5
+-
++")
+'model_encoder_layer_0_output_dense_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€ÐÎÑ
+constA
+)model_encoder_layer_0_output_dense_weight
+
+€
+€*?
+name7
+/
+-"+
+)model_encoder_layer_0_output_dense_weight*D
+val=
+
+€
+€*%
+@model_path/weights/weight.binÀÜÎÇ
+const<
++model_encoder_layer_0_output_LayerNorm_bias
+
+€*A
+name9
+1
+/"-
++model_encoder_layer_0_output_LayerNorm_bias*=
+val6
+
+€*%
+@model_path/weights/weight.bin€ÝÞË
+const>
+-model_encoder_layer_0_output_LayerNorm_weight
+
+€*C
+name;
+3
+1"/
+-model_encoder_layer_0_output_LayerNorm_weight*=
+val6
+
+€*%
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diff --git a/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin
new file mode 100644
index 0000000000000000000000000000000000000000..71d86e38eb7ae4707228b8af0888c01ab9b7c4a5
Binary files /dev/null and b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Data/com.apple.CoreML/weights/weight.bin differ
diff --git a/Sources/SwiftNLP/Resources/float32_model.mlpackage/Manifest.json b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..2eebcce458da7726c52b94969188388ac7f645b7
--- /dev/null
+++ b/Sources/SwiftNLP/Resources/float32_model.mlpackage/Manifest.json
@@ -0,0 +1,18 @@
+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "63B0B880-B145-44C1-9871-506E9D0C9935": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        },
+        "98B220AA-0669-4838-B6C4-38F80D1282C2": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        }
+    },
+    "rootModelIdentifier": "63B0B880-B145-44C1-9871-506E9D0C9935"
+}
diff --git a/Sources/SwiftNLPGenericLLMMacros/Macros.swift b/Sources/SwiftNLPGenericLLMMacros/Macros.swift
new file mode 100644
index 0000000000000000000000000000000000000000..b756250edf1afa6c71a8e6f3cdcf8a22505c9179
--- /dev/null
+++ b/Sources/SwiftNLPGenericLLMMacros/Macros.swift
@@ -0,0 +1,173 @@
+import CoreML
+import SwiftSyntax
+import SwiftSyntaxMacros
+
+
+@available(macOS 12, iOS 15.0, tvOS 17.0, watchOS 10.0, *)
+public struct LLMModelNameValidation: ExpressionMacro {
+    /**
+     Example expansion:
+     try! {
+         switch self.model {
+         case "gte-small":
+             self.inputDimention = 128;
+         case "all_MiniLM_L6_v2":
+             self.inputDimention = 512;
+         default:
+             throw fatalError("Model is not valid");
+         }
+     }();
+     */
+    
+    public static func expansion(
+        of node:  some FreestandingMacroExpansionSyntax,
+        in context: some MacroExpansionContext
+    ) throws -> ExprSyntax {
+        
+        // generate code
+        var macro = "try! { switch self.model { "
+        
+        for (k, v) in LLM_MODEL_CLASSES {
+            // extract values
+            let model_dim = v[LLMModelClassesKey.InputDimension]!
+            macro +=
+                """
+                case \"\(k)\":
+                    self.inputDimention = \(model_dim);
+                """
+        }
+        
+        macro += "default: throw fatalError(\"Model is not valid\"); } }();"
+        
+        return ExprSyntax(stringLiteral: macro)
+    }
+}
+
+
+@available(macOS 12, iOS 15.0, tvOS 17.0, watchOS 10.0, *)
+public struct LLMModelPredictionCases: ExpressionMacro {
+    /**
+     Example expansion:
+     {
+         switch self.model {
+         case "all_MiniLM_L6_v2":
+             let input_class = all_MiniLM_L6_v2Input(input_ids: inputIds, attention_mask: attentionMask);
+             output = try! LLMModel.prediction(input: input_class).featureValue(for: "embeddings")!.multiArrayValue!;
+         case "gte-small":
+             let input_class = float32_modelInput(input_ids: inputIds, attention_mask: attentionMask);
+             output = try! LLMModel.prediction(input: input_class).featureValue(for: "pooler_output")!.multiArrayValue!;
+         default:
+             output = nil;
+         }
+     }();
+     */
+    
+    public static func expansion(
+        of node:  some FreestandingMacroExpansionSyntax,
+        in context: some MacroExpansionContext
+    ) throws -> ExprSyntax {
+        
+        // get first argument from macro invocation
+        guard let input_arg = node.argumentList.first?.expression,
+            let segments = input_arg.as(StringLiteralExprSyntax.self)?.segments,
+            segments.count == 1,
+            case .stringSegment(let input_literal_segment)? = segments.first
+        else {
+            throw fatalError("Bad argument to macro.")
+        }
+        
+        // get second argument from macro invocation
+        guard let attention_arg = node.argumentList.dropFirst().first?.expression,
+            let segments = attention_arg.as(StringLiteralExprSyntax.self)?.segments,
+            segments.count == 1,
+            case .stringSegment(let attn_literal_segment)? = segments.first
+        else {
+            throw fatalError("Bad argument to macro.")
+        }
+        
+        // get third argument from macro invocation
+        guard let output_arg = node.argumentList.dropFirst().dropFirst().first?.expression,
+            let segments = output_arg.as(StringLiteralExprSyntax.self)?.segments,
+            segments.count == 1,
+            case .stringSegment(let output_literal_segment)? = segments.first
+        else {
+            throw fatalError("Bad argument to macro.")
+        }
+        
+        // extract parameter values
+        let model_input = input_literal_segment.content.text
+        let model_attn = attn_literal_segment.content.text
+        let model_output = output_literal_segment.content.text
+        
+        // generate code
+        var macro = "{ switch self.model { "
+        
+        for (k, v) in LLM_MODEL_CLASSES {
+            let model_class = v[LLMModelClassesKey.Input]!
+            let model_feature = v[LLMModelClassesKey.FeatureName]!
+            macro +=
+                """
+                case \"\(k)\":
+                    let input_class = \(model_class)(input_ids: \(model_input), attention_mask: \(model_attn));
+                    \(model_output) = try! LLMModel.prediction(input: input_class).featureValue(for: \"\(model_feature)\")!.multiArrayValue!;
+                """
+        }
+        
+        macro += "default: output = nil; } }();"
+        
+        return ExprSyntax(stringLiteral: macro)
+            
+    }
+}
+
+
+@available(macOS 12, iOS 15.0, tvOS 17.0, watchOS 10.0, *)
+public enum LLMPredictionFunctions: DeclarationMacro {
+    public static func expansion(
+        of node: some FreestandingMacroExpansionSyntax,
+        in context: some MacroExpansionContext
+    ) throws -> [DeclSyntax] {
+        
+        var ret: String = "";
+        
+        // generate code
+        for (k, v) in LLM_MODEL_CLASSES {
+            
+            // extract values
+            let model_type_name = v[LLMModelClassesKey.Model]!;
+            let model_input_name = v[LLMModelClassesKey.Input]!;
+            let model_output_name = v[LLMModelClassesKey.Output]!;
+            let model_url = v[LLMModelClassesKey.URL]!;
+        
+            ret +=
+                """
+                public static func prediction(input: \(model_input_name)) throws -> \(model_output_name) {
+                    let model = try \(model_type_name)(contentsOf: URL(fileURLWithPath: \"Sources/SwiftNLP/Models/\(model_url)\"));
+                    return try model.prediction(input: input, options: MLPredictionOptions())
+                }
+
+                public static func prediction(input: \(model_input_name), options: MLPredictionOptions) throws -> \(model_output_name) {
+                    let model = try \(model_type_name)(contentsOf: URL(fileURLWithPath: \"Sources/SwiftNLP/Models/\(model_url)\"));
+                    let outFeatures: MLFeatureProvider = try model.prediction(input: input, options:options)
+                    return \(model_output_name)(features: outFeatures)
+                }
+
+                @available(macOS 13.6, iOS 17.0, tvOS 17.0, watchOS 10.0, *)
+                public static func prediction(input: \(model_input_name), options: MLPredictionOptions = MLPredictionOptions()) async throws -> \(model_output_name) {
+                    let model = try \(model_type_name)(contentsOf: URL(fileURLWithPath: \"Sources/SwiftNLP/Models/\(model_url)\"));
+                    let outFeatures: MLFeatureProvider? = try await model.prediction(input: input, options:options)
+                    return \(model_output_name)(features: outFeatures!)
+                }
+
+                public static func predictions(inputs: [\(model_input_name)], options: MLPredictionOptions = MLPredictionOptions()) throws -> [\(model_output_name)] {
+                    let model = try \(model_type_name)(contentsOf: URL(fileURLWithPath: \"Sources/SwiftNLP/Models/\(model_url)\"));
+                    let res = try model.predictions(inputs: inputs, options: options);
+                    return res;
+                }
+                
+                """;
+        }
+
+        return [DeclSyntax(stringLiteral: ret)];
+    }
+}
diff --git a/Sources/SwiftNLPGenericLLMMacros/Main.swift b/Sources/SwiftNLPGenericLLMMacros/Main.swift
new file mode 100644
index 0000000000000000000000000000000000000000..a1a9228af8c740e5644f388e9e93a9389c69b780
--- /dev/null
+++ b/Sources/SwiftNLPGenericLLMMacros/Main.swift
@@ -0,0 +1,12 @@
+import SwiftCompilerPlugin
+import SwiftSyntaxMacros
+
+@main
+struct SwiftNLPGenericLLMMacros: CompilerPlugin {
+    init() {}
+    var providingMacros: [SwiftSyntaxMacros.Macro.Type] = [
+        LLMPredictionFunctions.self,
+        LLMModelPredictionCases.self,
+        LLMModelNameValidation.self
+    ]
+}
diff --git a/Sources/SwiftNLPGenericLLMMacros/ModelClasses.swift b/Sources/SwiftNLPGenericLLMMacros/ModelClasses.swift
new file mode 100644
index 0000000000000000000000000000000000000000..2eb0950cb8d12335d0fd30b06375cff9e76c5723
--- /dev/null
+++ b/Sources/SwiftNLPGenericLLMMacros/ModelClasses.swift
@@ -0,0 +1,27 @@
+enum LLMModelClassesKey {
+    case Input
+    case Output
+    case Model
+    case FeatureName
+    case URL
+    case InputDimension
+}
+
+let LLM_MODEL_CLASSES: [String: [LLMModelClassesKey: Any]] = [
+    "all_MiniLM_L6_v2": [
+        LLMModelClassesKey.Input: all_MiniLM_L6_v2Input.self,
+        LLMModelClassesKey.Output: all_MiniLM_L6_v2Output.self,
+        LLMModelClassesKey.Model: all_MiniLM_L6_v2.self,
+        LLMModelClassesKey.FeatureName: "embeddings",
+        LLMModelClassesKey.URL: "all-MiniLM-L6-v2.mlmodelc",
+        LLMModelClassesKey.InputDimension: 512
+    ],
+    "gte-small": [
+        LLMModelClassesKey.Input: float32_modelInput.self,
+        LLMModelClassesKey.Output: float32_modelOutput.self,
+        LLMModelClassesKey.Model: float32_model.self,
+        LLMModelClassesKey.FeatureName: "pooler_output",
+        LLMModelClassesKey.URL: "float32_model.mlmodelc",
+        LLMModelClassesKey.InputDimension: 128
+    ]
+]
diff --git a/Sources/SwiftNLPGenericLLMMacros/all-MiniLM-L6-v2.swift b/Sources/SwiftNLPGenericLLMMacros/all-MiniLM-L6-v2.swift
new file mode 100644
index 0000000000000000000000000000000000000000..c2f0c441add7ef7602ae99c4a202576c29faa97b
--- /dev/null
+++ b/Sources/SwiftNLPGenericLLMMacros/all-MiniLM-L6-v2.swift
@@ -0,0 +1,306 @@
+//
+// all_MiniLM_L6_v2.swift
+//
+// This file was automatically generated and should not be edited.
+//
+
+import CoreML
+
+
+/// Model Prediction Input Type
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class all_MiniLM_L6_v2Input : MLFeatureProvider {
+
+    /// input_ids as 1 by 512 matrix of floats
+    var input_ids: MLMultiArray
+
+    /// attention_mask as 1 by 512 matrix of floats
+    var attention_mask: MLMultiArray
+
+    var featureNames: Set<String> {
+        get {
+            return ["input_ids", "attention_mask"]
+        }
+    }
+    
+    func featureValue(for featureName: String) -> MLFeatureValue? {
+        if (featureName == "input_ids") {
+            return MLFeatureValue(multiArray: input_ids)
+        }
+        if (featureName == "attention_mask") {
+            return MLFeatureValue(multiArray: attention_mask)
+        }
+        return nil
+    }
+    
+    init(input_ids: MLMultiArray, attention_mask: MLMultiArray) {
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+    }
+
+    convenience init(input_ids: MLShapedArray<Float>, attention_mask: MLShapedArray<Float>) {
+        self.init(input_ids: MLMultiArray(input_ids), attention_mask: MLMultiArray(attention_mask))
+    }
+
+}
+
+
+/// Model Prediction Output Type
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class all_MiniLM_L6_v2Output : MLFeatureProvider {
+
+    /// Source provided by CoreML
+    private let provider : MLFeatureProvider
+
+    /// embeddings as multidimensional array of floats
+    var embeddings: MLMultiArray {
+        return self.provider.featureValue(for: "embeddings")!.multiArrayValue!
+    }
+
+    /// embeddings as multidimensional array of floats
+    var embeddingsShapedArray: MLShapedArray<Float> {
+        return MLShapedArray<Float>(self.embeddings)
+    }
+
+    var featureNames: Set<String> {
+        return self.provider.featureNames
+    }
+    
+    func featureValue(for featureName: String) -> MLFeatureValue? {
+        return self.provider.featureValue(for: featureName)
+    }
+
+    init(embeddings: MLMultiArray) {
+        self.provider = try! MLDictionaryFeatureProvider(dictionary: ["embeddings" : MLFeatureValue(multiArray: embeddings)])
+    }
+
+    init(features: MLFeatureProvider) {
+        self.provider = features
+    }
+}
+
+
+/// Class for model loading and prediction
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class all_MiniLM_L6_v2 {
+    let model: MLModel
+
+    /// URL of model assuming it was installed in the same bundle as this class
+    class var urlOfModelInThisBundle : URL {
+        let bundle = Bundle(for: self)
+        return bundle.url(forResource: "all-MiniLM-L6-v2", withExtension:"mlmodelc")!
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance with an existing MLModel object.
+
+        Usually the application does not use this initializer unless it makes a subclass of all_MiniLM_L6_v2.
+        Such application may want to use `MLModel(contentsOfURL:configuration:)` and `all_MiniLM_L6_v2.urlOfModelInThisBundle` to create a MLModel object to pass-in.
+
+        - parameters:
+          - model: MLModel object
+    */
+    init(model: MLModel) {
+        self.model = model
+    }
+
+    /**
+        Construct a model with configuration
+
+        - parameters:
+           - configuration: the desired model configuration
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(configuration: MLModelConfiguration = MLModelConfiguration()) throws {
+        try self.init(contentsOf: type(of:self).urlOfModelInThisBundle, configuration: configuration)
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance with explicit path to mlmodelc file
+        - parameters:
+           - modelURL: the file url of the model
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(contentsOf modelURL: URL) throws {
+        try self.init(model: MLModel(contentsOf: modelURL))
+    }
+
+    /**
+        Construct a model with URL of the .mlmodelc directory and configuration
+
+        - parameters:
+           - modelURL: the file url of the model
+           - configuration: the desired model configuration
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(contentsOf modelURL: URL, configuration: MLModelConfiguration) throws {
+        try self.init(model: MLModel(contentsOf: modelURL, configuration: configuration))
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance asynchronously with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - configuration: the desired model configuration
+          - handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
+    */
+    class func load(configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<all_MiniLM_L6_v2, Error>) -> Void) {
+        return self.load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration, completionHandler: handler)
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance asynchronously with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - configuration: the desired model configuration
+    */
+    class func load(configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> all_MiniLM_L6_v2 {
+        return try await self.load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration)
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance asynchronously with URL of the .mlmodelc directory with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - modelURL: the URL to the model
+          - configuration: the desired model configuration
+          - handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
+    */
+    class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<all_MiniLM_L6_v2, Error>) -> Void) {
+        MLModel.load(contentsOf: modelURL, configuration: configuration) { result in
+            switch result {
+            case .failure(let error):
+                handler(.failure(error))
+            case .success(let model):
+                handler(.success(all_MiniLM_L6_v2(model: model)))
+            }
+        }
+    }
+
+    /**
+        Construct all_MiniLM_L6_v2 instance asynchronously with URL of the .mlmodelc directory with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - modelURL: the URL to the model
+          - configuration: the desired model configuration
+    */
+    class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> all_MiniLM_L6_v2 {
+        let model = try await MLModel.load(contentsOf: modelURL, configuration: configuration)
+        return all_MiniLM_L6_v2(model: model)
+    }
+
+    /**
+        Make a prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as all_MiniLM_L6_v2Input
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as all_MiniLM_L6_v2Output
+    */
+    func prediction(input: all_MiniLM_L6_v2Input) throws -> all_MiniLM_L6_v2Output {
+        return try self.prediction(input: input, options: MLPredictionOptions())
+    }
+
+    /**
+        Make a prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as all_MiniLM_L6_v2Input
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as all_MiniLM_L6_v2Output
+    */
+    func prediction(input: all_MiniLM_L6_v2Input, options: MLPredictionOptions) throws -> all_MiniLM_L6_v2Output {
+        let outFeatures = try model.prediction(from: input, options:options)
+        return all_MiniLM_L6_v2Output(features: outFeatures)
+    }
+
+    /**
+        Make an asynchronous prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as all_MiniLM_L6_v2Input
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as all_MiniLM_L6_v2Output
+    */
+    @available(macOS 14.0, iOS 17.0, tvOS 17.0, watchOS 10.0, *)
+    func prediction(input: all_MiniLM_L6_v2Input, options: MLPredictionOptions = MLPredictionOptions()) async throws -> all_MiniLM_L6_v2Output {
+        let outFeatures = try await model.prediction(from: input, options:options)
+        return all_MiniLM_L6_v2Output(features: outFeatures)
+    }
+
+    /**
+        Make a prediction using the convenience interface
+
+        - parameters:
+            - input_ids as 1 by 512 matrix of floats
+            - attention_mask as 1 by 512 matrix of floats
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as all_MiniLM_L6_v2Output
+    */
+    func prediction(input_ids: MLMultiArray, attention_mask: MLMultiArray) throws -> all_MiniLM_L6_v2Output {
+        let input_ = all_MiniLM_L6_v2Input(input_ids: input_ids, attention_mask: attention_mask)
+        return try self.prediction(input: input_)
+    }
+
+    /**
+        Make a prediction using the convenience interface
+
+        - parameters:
+            - input_ids as 1 by 512 matrix of floats
+            - attention_mask as 1 by 512 matrix of floats
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as all_MiniLM_L6_v2Output
+    */
+
+    func prediction(input_ids: MLShapedArray<Float>, attention_mask: MLShapedArray<Float>) throws -> all_MiniLM_L6_v2Output {
+        let input_ = all_MiniLM_L6_v2Input(input_ids: input_ids, attention_mask: attention_mask)
+        return try self.prediction(input: input_)
+    }
+
+    /**
+        Make a batch prediction using the structured interface
+
+        - parameters:
+           - inputs: the inputs to the prediction as [all_MiniLM_L6_v2Input]
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as [all_MiniLM_L6_v2Output]
+    */
+    func predictions(inputs: [all_MiniLM_L6_v2Input], options: MLPredictionOptions = MLPredictionOptions()) throws -> [all_MiniLM_L6_v2Output] {
+        let batchIn = MLArrayBatchProvider(array: inputs)
+        let batchOut = try model.predictions(from: batchIn, options: options)
+        var results : [all_MiniLM_L6_v2Output] = []
+        results.reserveCapacity(inputs.count)
+        for i in 0..<batchOut.count {
+            let outProvider = batchOut.features(at: i)
+            let result =  all_MiniLM_L6_v2Output(features: outProvider)
+            results.append(result)
+        }
+        return results
+    }
+}
diff --git a/Sources/SwiftNLPGenericLLMMacros/float32_model.swift b/Sources/SwiftNLPGenericLLMMacros/float32_model.swift
new file mode 100644
index 0000000000000000000000000000000000000000..895c2fdb7ba5c7e9a7e2901daa59205de3bbded4
--- /dev/null
+++ b/Sources/SwiftNLPGenericLLMMacros/float32_model.swift
@@ -0,0 +1,316 @@
+//
+// float32_model.swift
+//
+// This file was automatically generated and should not be edited.
+//
+
+import CoreML
+
+
+/// Model Prediction Input Type
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class float32_modelInput : MLFeatureProvider {
+
+    /// Indices of input sequence tokens in the vocabulary as 1 by 128 matrix of 32-bit integers
+    var input_ids: MLMultiArray
+
+    /// Mask to avoid performing attention on padding token indices (1 = not masked, 0 = masked) as 1 by 128 matrix of 32-bit integers
+    var attention_mask: MLMultiArray
+
+    var featureNames: Set<String> {
+        get {
+            return ["input_ids", "attention_mask"]
+        }
+    }
+    
+    func featureValue(for featureName: String) -> MLFeatureValue? {
+        if (featureName == "input_ids") {
+            return MLFeatureValue(multiArray: input_ids)
+        }
+        if (featureName == "attention_mask") {
+            return MLFeatureValue(multiArray: attention_mask)
+        }
+        return nil
+    }
+    
+    init(input_ids: MLMultiArray, attention_mask: MLMultiArray) {
+        self.input_ids = input_ids
+        self.attention_mask = attention_mask
+    }
+
+    convenience init(input_ids: MLShapedArray<Int32>, attention_mask: MLShapedArray<Int32>) {
+        self.init(input_ids: MLMultiArray(input_ids), attention_mask: MLMultiArray(attention_mask))
+    }
+
+}
+
+
+/// Model Prediction Output Type
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class float32_modelOutput : MLFeatureProvider {
+
+    /// Source provided by CoreML
+    private let provider : MLFeatureProvider
+
+    /// Sequence of hidden-states at the output of the last layer of the model as 1 Ã— 128 Ã— 384 3-dimensional array of floats
+    var last_hidden_state: MLMultiArray {
+        return self.provider.featureValue(for: "last_hidden_state")!.multiArrayValue!
+    }
+
+    /// Sequence of hidden-states at the output of the last layer of the model as 1 Ã— 128 Ã— 384 3-dimensional array of floats
+    var last_hidden_stateShapedArray: MLShapedArray<Float> {
+        return MLShapedArray<Float>(self.last_hidden_state)
+    }
+
+    /// Last layer hidden-state of the first token of the sequence as 1 by 384 matrix of floats
+    var pooler_output: MLMultiArray {
+        return self.provider.featureValue(for: "pooler_output")!.multiArrayValue!
+    }
+
+    /// Last layer hidden-state of the first token of the sequence as 1 by 384 matrix of floats
+    var pooler_outputShapedArray: MLShapedArray<Float> {
+        return MLShapedArray<Float>(self.pooler_output)
+    }
+
+    var featureNames: Set<String> {
+        return self.provider.featureNames
+    }
+    
+    func featureValue(for featureName: String) -> MLFeatureValue? {
+        return self.provider.featureValue(for: featureName)
+    }
+
+    init(last_hidden_state: MLMultiArray, pooler_output: MLMultiArray) {
+        self.provider = try! MLDictionaryFeatureProvider(dictionary: ["last_hidden_state" : MLFeatureValue(multiArray: last_hidden_state), "pooler_output" : MLFeatureValue(multiArray: pooler_output)])
+    }
+
+    init(features: MLFeatureProvider) {
+        self.provider = features
+    }
+}
+
+
+/// Class for model loading and prediction
+@available(macOS 12.0, iOS 15.0, tvOS 15.0, watchOS 8.0, *)
+class float32_model {
+    let model: MLModel
+
+    /// URL of model assuming it was installed in the same bundle as this class
+    class var urlOfModelInThisBundle : URL {
+        let bundle = Bundle(for: self)
+        return bundle.url(forResource: "float32_model", withExtension:"mlmodelc")!
+    }
+
+    /**
+        Construct float32_model instance with an existing MLModel object.
+
+        Usually the application does not use this initializer unless it makes a subclass of float32_model.
+        Such application may want to use `MLModel(contentsOfURL:configuration:)` and `float32_model.urlOfModelInThisBundle` to create a MLModel object to pass-in.
+
+        - parameters:
+          - model: MLModel object
+    */
+    init(model: MLModel) {
+        self.model = model
+    }
+
+    /**
+        Construct a model with configuration
+
+        - parameters:
+           - configuration: the desired model configuration
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(configuration: MLModelConfiguration = MLModelConfiguration()) throws {
+        try self.init(contentsOf: type(of:self).urlOfModelInThisBundle, configuration: configuration)
+    }
+
+    /**
+        Construct float32_model instance with explicit path to mlmodelc file
+        - parameters:
+           - modelURL: the file url of the model
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(contentsOf modelURL: URL) throws {
+        try self.init(model: MLModel(contentsOf: modelURL))
+    }
+
+    /**
+        Construct a model with URL of the .mlmodelc directory and configuration
+
+        - parameters:
+           - modelURL: the file url of the model
+           - configuration: the desired model configuration
+
+        - throws: an NSError object that describes the problem
+    */
+    convenience init(contentsOf modelURL: URL, configuration: MLModelConfiguration) throws {
+        try self.init(model: MLModel(contentsOf: modelURL, configuration: configuration))
+    }
+
+    /**
+        Construct float32_model instance asynchronously with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - configuration: the desired model configuration
+          - handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
+    */
+    class func load(configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<float32_model, Error>) -> Void) {
+        return self.load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration, completionHandler: handler)
+    }
+
+    /**
+        Construct float32_model instance asynchronously with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - configuration: the desired model configuration
+    */
+    class func load(configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> float32_model {
+        return try await self.load(contentsOf: self.urlOfModelInThisBundle, configuration: configuration)
+    }
+
+    /**
+        Construct float32_model instance asynchronously with URL of the .mlmodelc directory with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - modelURL: the URL to the model
+          - configuration: the desired model configuration
+          - handler: the completion handler to be called when the model loading completes successfully or unsuccessfully
+    */
+    class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration(), completionHandler handler: @escaping (Swift.Result<float32_model, Error>) -> Void) {
+        MLModel.load(contentsOf: modelURL, configuration: configuration) { result in
+            switch result {
+            case .failure(let error):
+                handler(.failure(error))
+            case .success(let model):
+                handler(.success(float32_model(model: model)))
+            }
+        }
+    }
+
+    /**
+        Construct float32_model instance asynchronously with URL of the .mlmodelc directory with optional configuration.
+
+        Model loading may take time when the model content is not immediately available (e.g. encrypted model). Use this factory method especially when the caller is on the main thread.
+
+        - parameters:
+          - modelURL: the URL to the model
+          - configuration: the desired model configuration
+    */
+    class func load(contentsOf modelURL: URL, configuration: MLModelConfiguration = MLModelConfiguration()) async throws -> float32_model {
+        let model = try await MLModel.load(contentsOf: modelURL, configuration: configuration)
+        return float32_model(model: model)
+    }
+
+    /**
+        Make a prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as float32_modelInput
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as float32_modelOutput
+    */
+    func prediction(input: float32_modelInput) throws -> float32_modelOutput {
+        return try self.prediction(input: input, options: MLPredictionOptions())
+    }
+
+    /**
+        Make a prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as float32_modelInput
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as float32_modelOutput
+    */
+    func prediction(input: float32_modelInput, options: MLPredictionOptions) throws -> float32_modelOutput {
+        let outFeatures = try model.prediction(from: input, options:options)
+        return float32_modelOutput(features: outFeatures)
+    }
+
+    /**
+        Make an asynchronous prediction using the structured interface
+
+        - parameters:
+           - input: the input to the prediction as float32_modelInput
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as float32_modelOutput
+    */
+    @available(macOS 14.0, iOS 17.0, tvOS 17.0, watchOS 10.0, *)
+    func prediction(input: float32_modelInput, options: MLPredictionOptions = MLPredictionOptions()) async throws -> float32_modelOutput {
+        let outFeatures = try await model.prediction(from: input, options:options)
+        return float32_modelOutput(features: outFeatures)
+    }
+
+    /**
+        Make a prediction using the convenience interface
+
+        - parameters:
+            - input_ids: Indices of input sequence tokens in the vocabulary as 1 by 128 matrix of 32-bit integers
+            - attention_mask: Mask to avoid performing attention on padding token indices (1 = not masked, 0 = masked) as 1 by 128 matrix of 32-bit integers
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as float32_modelOutput
+    */
+    func prediction(input_ids: MLMultiArray, attention_mask: MLMultiArray) throws -> float32_modelOutput {
+        let input_ = float32_modelInput(input_ids: input_ids, attention_mask: attention_mask)
+        return try self.prediction(input: input_)
+    }
+
+    /**
+        Make a prediction using the convenience interface
+
+        - parameters:
+            - input_ids: Indices of input sequence tokens in the vocabulary as 1 by 128 matrix of 32-bit integers
+            - attention_mask: Mask to avoid performing attention on padding token indices (1 = not masked, 0 = masked) as 1 by 128 matrix of 32-bit integers
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as float32_modelOutput
+    */
+
+    func prediction(input_ids: MLShapedArray<Int32>, attention_mask: MLShapedArray<Int32>) throws -> float32_modelOutput {
+        let input_ = float32_modelInput(input_ids: input_ids, attention_mask: attention_mask)
+        return try self.prediction(input: input_)
+    }
+
+    /**
+        Make a batch prediction using the structured interface
+
+        - parameters:
+           - inputs: the inputs to the prediction as [float32_modelInput]
+           - options: prediction options 
+
+        - throws: an NSError object that describes the problem
+
+        - returns: the result of the prediction as [float32_modelOutput]
+    */
+    func predictions(inputs: [float32_modelInput], options: MLPredictionOptions = MLPredictionOptions()) throws -> [float32_modelOutput] {
+        let batchIn = MLArrayBatchProvider(array: inputs)
+        let batchOut = try model.predictions(from: batchIn, options: options)
+        var results : [float32_modelOutput] = []
+        results.reserveCapacity(inputs.count)
+        for i in 0..<batchOut.count {
+            let outProvider = batchOut.features(at: i)
+            let result =  float32_modelOutput(features: outProvider)
+            results.append(result)
+        }
+        return results
+    }
+}
diff --git a/Tests/SwiftNLPTests/1. Data Collection/HNSW/DurableHNSWCorpusTests.swift b/Tests/SwiftNLPTests/1. Data Collection/HNSW/DurableHNSWCorpusTests.swift
index 358e0f9e8bd68fb1ff8ace55215d2a905bf8c503..545a525e87afec840e7020cb7b5bf92916450981 100644
--- a/Tests/SwiftNLPTests/1. Data Collection/HNSW/DurableHNSWCorpusTests.swift	
+++ b/Tests/SwiftNLPTests/1. Data Collection/HNSW/DurableHNSWCorpusTests.swift	
@@ -1,230 +1,230 @@
-#if os(macOS)
-import XCTest
-import Foundation
-import CoreLMDB
-import System
-@testable import SwiftNLP
-
-// MARK: These tests are not to be included within the pipeline
-
-final class DurableHNSWCorpusTests: XCTestCase {
-    /// This is used to skip these tests in the GitLab pipeline
-    override class var defaultTestSuite: XCTestSuite {
-        if ProcessInfo.processInfo.environment["SKIP_TESTS"] == "DurableHNSWCorpusTests" {
-            return XCTestSuite(name: "Empty")
-        }
-        return super.defaultTestSuite
-    }
-    
-    /// Setting up constants for environment
-    private let ONE_GB: Int = 1_073_741_824
-    private let ONE_MB: Int = 1_048_576
-    private let ONE_KB: Int = 1_024
-    private let ONE_B:  Int = 1
-    private let DEFAULT_MAXREADERS: UInt32 = 126
-    private let DEFAULT_MAXDBS:     UInt32 = 10
-    
-    /// Setting up working directory
-    private var workingDirectoryPath: FilePath!
-    
-    override func setUpWithError() throws {
-        try super.setUpWithError()
-        
-        let fileManager = FileManager.default
-        let directoryURL = fileManager.homeDirectoryForCurrentUser.appendingPathComponent("/Downloads/lmdb")
-        try fileManager.createDirectory(at: directoryURL, withIntermediateDirectories: true, attributes: nil)
-        workingDirectoryPath = FilePath(directoryURL.path)
-        
-        /// This commented out code alternatively works in the XCode bundle resource environment
-//        guard let resourcesPath = Bundle.module.resourcePath else { fatalError("Failed to find resource path.") }
-//        let resourcesDirectoryURL = URL(fileURLWithPath: resourcesPath).appendingPathComponent("lmdb")
+//#if os(macOS)
+//import XCTest
+//import Foundation
+//import CoreLMDB
+//import System
+//@testable import SwiftNLP
+//
+//// MARK: These tests are not to be included within the pipeline
+//
+//final class DurableHNSWCorpusTests: XCTestCase {
+//    /// This is used to skip these tests in the GitLab pipeline
+//    override class var defaultTestSuite: XCTestSuite {
+//        if ProcessInfo.processInfo.environment["SKIP_TESTS"] == "DurableHNSWCorpusTests" {
+//            return XCTestSuite(name: "Empty")
+//        }
+//        return super.defaultTestSuite
+//    }
+//    
+//    /// Setting up constants for environment
+//    private let ONE_GB: Int = 1_073_741_824
+//    private let ONE_MB: Int = 1_048_576
+//    private let ONE_KB: Int = 1_024
+//    private let ONE_B:  Int = 1
+//    private let DEFAULT_MAXREADERS: UInt32 = 126
+//    private let DEFAULT_MAXDBS:     UInt32 = 10
+//    
+//    /// Setting up working directory
+//    private var workingDirectoryPath: FilePath!
+//    
+//    override func setUpWithError() throws {
+//        try super.setUpWithError()
+//        
 //        let fileManager = FileManager.default
-//        try fileManager.createDirectory(at: resourcesDirectoryURL, withIntermediateDirectories: true, attributes: nil)
-//        print("Resources directory: \(resourcesDirectoryURL)")
-//        workingDirectoryPath = FilePath(resourcesDirectoryURL.path)
-    }
-    
-    func testBuildBasicCorpus() throws {
-        let docs = [
-            "CNTK formerly known as Computational Network Toolkit",
-            "is a free easy-to-use open-source commercial-grade toolkit",
-            "that enable us to train deep learning algorithms to learn like the human brain."
-        ]
-        
-        /// Setting up the environment
-        let env = try Environment()
-        try env.setMapSize(ONE_GB)
-        try env.setMaxReaders(DEFAULT_MAXREADERS)
-        try env.setMaxDBs(DEFAULT_MAXDBS)
-        try env.open(path: workingDirectoryPath)
-        
-        /// Writing to LMDB
-        let transaction = try Transaction.begin(.write, in: env)
-
-        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
-            namespace: "testBasicExample",
-            in: transaction
-        )
-        
-        for doc in docs {
-            try corpus.addUntokenizedDocument(doc, in: transaction)
-        }
-        
-        try transaction.commit()
-        
-        /// Reading from LMDB
-        let readTransaction = try Transaction.begin(.read, in: env)
-        
-        let _ = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
-            namespace: "testBasicExample",
-            in: readTransaction
-        )
-        
-        readTransaction.abort()
-        
-        // XCTAssert(readCorpus.count == 3)
-        /// readCorpus.count == 3 will fail because we have not fixed the bug with setting size upon reads
-        /// This is because size is only incremented when insertion is called but it is not called when read from disk!
-    }
-    
-    func testQueryBasicCorpus() async throws {
-        let docs = [
-            "The quick brown fox jumps over the lazy dog",
-            "I enjoy taking long walks along the beach at sunset",
-            "Advances in neural networks have enabled new AI capabilities",
-            "The stock market experienced a significant downturn last week",
-            "Cooking a good meal can be both an art and a science",
-            "The exploration of space is both challenging and rewarding",
-            "Machine learning models are becoming increasingly sophisticated",
-            "I love reading about history and ancient civilizations"
-        ]
-        
-        let query = "I like to read about new technology and artificial intelligence"
-        //let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
-        
-        /// Setting up the environment
-        let env = try Environment()
-        try env.setMapSize(ONE_GB)
-        try env.setMaxReaders(DEFAULT_MAXREADERS)
-        try env.setMaxDBs(DEFAULT_MAXDBS)
-        try env.open(path: workingDirectoryPath)
-        
-        let transaction = try Transaction.begin(.write, in: env)
-        
-        /// Saving the memory map to disk
-        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
-            namespace: "testBasicQueryExample",
-            in: transaction
-        )
-        
-        for doc in docs {
-            try corpus.addUntokenizedDocument(doc, in: transaction)
-        }
-        
-        corpus.saveDictionaryToDownloads(fileName: "dictionary.mmap")
-        
-        try transaction.commit()
-        
-        do {
-            let queryVector: [Double] = corpus.documentEncoder.encodeToken(query).map { Double($0) }
-            
-            /// Reading the memory map (and dictionary) from disk
-            let readTransaction = try Transaction.begin(.write, in: env)
-            
-            let readCorpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
-                namespace: "testBasicQueryExample",
-                in: readTransaction
-            )
-            
-            readCorpus.dictionary = DurableHNSWCorpus.readDictionaryFromDownloads(fileName: "dictionary.mmap") // TODO: move this to initializer?
-            
-            let result = try readCorpus.index.find(near: queryVector, limit: 8, in: transaction)
-            
-            for result in result {
-                let key = Int(result.id.foreignKey)!
-                print(readCorpus.getUntokenizedDocument(at: key))
-            }
-        } catch {
-            print("Error when trying corpus.encodedDocuments.find(): \(error)")
-        }
-        
-        try transaction.commit()
-    }
-    
-    func testBuildGuelphSubredditCorpus() async throws {
-        /// Generates the LMDB durable storage to disk but runs no tests otherwise
-    
-        /// Setting up the environment
-        let env = try Environment()
-        try env.setMapSize(ONE_GB)
-        try env.setMaxReaders(DEFAULT_MAXREADERS)
-        try env.setMaxDBs(DEFAULT_MAXDBS)
-        try env.open(path: workingDirectoryPath)
-        
-        /// Get subreddit data
-        guard let submissionsURL = Bundle.module.url(forResource: "Guelph_submissions", withExtension: "zst") else {
-            fatalError("Failed to find waterloo_submissions.zst in test bundle.")
-        }
-        guard let submissionsData = try? Data(contentsOf: submissionsURL) else {
-            fatalError("Failed to load waterloo_submissions.zst from test bundle.")
-        }
-
-        let (submissions, _ ): ([Submission],[Data]) = try await loadFromRedditArchive(submissionsData)
-        
-        let transaction = try Transaction.begin(.write, in: env)
-        
-        let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
-        
-        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder>(
-            encoder: documentEncoder,
-            namespace: "subreddit_durable",
-            in: transaction
-        )
-
-        /// Add documents to corpus
-        for submission in submissions {
-            if let text = submission.selftext {
-                try corpus.addUntokenizedDocument(text, in: transaction)
-            }
-        }
-
-        /// Save dictionary to disk
-        corpus.saveDictionaryToDownloads(fileName: "dictionary.mmap")
-        
-        try transaction.commit()
-    }
-    
-    func testQueryGuelphSubredditCorpus() async throws {
-        let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
-        
-        /// Setting up the environment
-        let env = try Environment()
-        try env.setMapSize(ONE_GB)
-        try env.setMaxReaders(DEFAULT_MAXREADERS)
-        try env.setMaxDBs(DEFAULT_MAXDBS)
-        try env.open(path: workingDirectoryPath)
-        
-        /// Reading the memory map (and dictionary) from disk
-        let transaction = try Transaction.begin(.read, in: env)
-        
-        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder>(
-            encoder: documentEncoder,
-            namespace: "subreddit_durable",
-            in: transaction
-        )
-        
-        corpus.dictionary = DurableHNSWCorpus.readDictionaryFromDownloads(fileName: "dictionary.mmap")
-        
-        let query = "I love waterloo and I love the geese."
-        let queryVector: [Double] = documentEncoder.encodeToken(query).map { Double($0) }
-        
-        let result = try corpus.index.find(near: queryVector, limit: 8, in: transaction)
-        
-        for result in result {
-            let key = Int(result.id.foreignKey)!
-            print(corpus.getUntokenizedDocument(at: key))
-        }
-    }
-}
-#endif
-
+//        let directoryURL = fileManager.homeDirectoryForCurrentUser.appendingPathComponent("/Downloads/lmdb")
+//        try fileManager.createDirectory(at: directoryURL, withIntermediateDirectories: true, attributes: nil)
+//        workingDirectoryPath = FilePath(directoryURL.path)
+//        
+//        /// This commented out code alternatively works in the XCode bundle resource environment
+////        guard let resourcesPath = Bundle.module.resourcePath else { fatalError("Failed to find resource path.") }
+////        let resourcesDirectoryURL = URL(fileURLWithPath: resourcesPath).appendingPathComponent("lmdb")
+////        let fileManager = FileManager.default
+////        try fileManager.createDirectory(at: resourcesDirectoryURL, withIntermediateDirectories: true, attributes: nil)
+////        print("Resources directory: \(resourcesDirectoryURL)")
+////        workingDirectoryPath = FilePath(resourcesDirectoryURL.path)
+//    }
+//    
+//    func testBuildBasicCorpus() throws {
+//        let docs = [
+//            "CNTK formerly known as Computational Network Toolkit",
+//            "is a free easy-to-use open-source commercial-grade toolkit",
+//            "that enable us to train deep learning algorithms to learn like the human brain."
+//        ]
+//        
+//        /// Setting up the environment
+//        let env = try Environment()
+//        try env.setMapSize(ONE_GB)
+//        try env.setMaxReaders(DEFAULT_MAXREADERS)
+//        try env.setMaxDBs(DEFAULT_MAXDBS)
+//        try env.open(path: workingDirectoryPath)
+//        
+//        /// Writing to LMDB
+//        let transaction = try Transaction.begin(.write, in: env)
+//
+//        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
+//            namespace: "testBasicExample",
+//            in: transaction
+//        )
+//        
+//        for doc in docs {
+//            try corpus.addUntokenizedDocument(doc, in: transaction)
+//        }
+//        
+//        try transaction.commit()
+//        
+//        /// Reading from LMDB
+//        let readTransaction = try Transaction.begin(.read, in: env)
+//        
+//        let _ = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
+//            namespace: "testBasicExample",
+//            in: readTransaction
+//        )
+//        
+//        readTransaction.abort()
+//        
+//        // XCTAssert(readCorpus.count == 3)
+//        /// readCorpus.count == 3 will fail because we have not fixed the bug with setting size upon reads
+//        /// This is because size is only incremented when insertion is called but it is not called when read from disk!
+//    }
+//    
+//    func testQueryBasicCorpus() async throws {
+//        let docs = [
+//            "The quick brown fox jumps over the lazy dog",
+//            "I enjoy taking long walks along the beach at sunset",
+//            "Advances in neural networks have enabled new AI capabilities",
+//            "The stock market experienced a significant downturn last week",
+//            "Cooking a good meal can be both an art and a science",
+//            "The exploration of space is both challenging and rewarding",
+//            "Machine learning models are becoming increasingly sophisticated",
+//            "I love reading about history and ancient civilizations"
+//        ]
+//        
+//        let query = "I like to read about new technology and artificial intelligence"
+//        //let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
+//        
+//        /// Setting up the environment
+//        let env = try Environment()
+//        try env.setMapSize(ONE_GB)
+//        try env.setMaxReaders(DEFAULT_MAXREADERS)
+//        try env.setMaxDBs(DEFAULT_MAXDBS)
+//        try env.open(path: workingDirectoryPath)
+//        
+//        let transaction = try Transaction.begin(.write, in: env)
+//        
+//        /// Saving the memory map to disk
+//        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
+//            namespace: "testBasicQueryExample",
+//            in: transaction
+//        )
+//        
+//        for doc in docs {
+//            try corpus.addUntokenizedDocument(doc, in: transaction)
+//        }
+//        
+//        corpus.saveDictionaryToDownloads(fileName: "dictionary.mmap")
+//        
+//        try transaction.commit()
+//        
+//        do {
+//            let queryVector: [Double] = corpus.documentEncoder.encodeToken(query).map { Double($0) }
+//            
+//            /// Reading the memory map (and dictionary) from disk
+//            let readTransaction = try Transaction.begin(.write, in: env)
+//            
+//            let readCorpus = try DurableHNSWCorpus<String,ContextFreeEncoder<Double>>(
+//                namespace: "testBasicQueryExample",
+//                in: readTransaction
+//            )
+//            
+//            readCorpus.dictionary = DurableHNSWCorpus.readDictionaryFromDownloads(fileName: "dictionary.mmap") // TODO: move this to initializer?
+//            
+//            let result = try readCorpus.index.find(near: queryVector, limit: 8, in: transaction)
+//            
+//            for result in result {
+//                let key = Int(result.id.foreignKey)!
+//                print(readCorpus.getUntokenizedDocument(at: key))
+//            }
+//        } catch {
+//            print("Error when trying corpus.encodedDocuments.find(): \(error)")
+//        }
+//        
+//        try transaction.commit()
+//    }
+//    
+//    func testBuildGuelphSubredditCorpus() async throws {
+//        /// Generates the LMDB durable storage to disk but runs no tests otherwise
+//    
+//        /// Setting up the environment
+//        let env = try Environment()
+//        try env.setMapSize(ONE_GB)
+//        try env.setMaxReaders(DEFAULT_MAXREADERS)
+//        try env.setMaxDBs(DEFAULT_MAXDBS)
+//        try env.open(path: workingDirectoryPath)
+//        
+//        /// Get subreddit data
+//        guard let submissionsURL = Bundle.module.url(forResource: "Guelph_submissions", withExtension: "zst") else {
+//            fatalError("Failed to find waterloo_submissions.zst in test bundle.")
+//        }
+//        guard let submissionsData = try? Data(contentsOf: submissionsURL) else {
+//            fatalError("Failed to load waterloo_submissions.zst from test bundle.")
+//        }
+//
+//        let (submissions, _ ): ([Submission],[Data]) = try await loadFromRedditArchive(submissionsData)
+//        
+//        let transaction = try Transaction.begin(.write, in: env)
+//        
+//        let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
+//        
+//        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder>(
+//            encoder: documentEncoder,
+//            namespace: "subreddit_durable",
+//            in: transaction
+//        )
+//
+//        /// Add documents to corpus
+//        for submission in submissions {
+//            if let text = submission.selftext {
+//                try corpus.addUntokenizedDocument(text, in: transaction)
+//            }
+//        }
+//
+//        /// Save dictionary to disk
+//        corpus.saveDictionaryToDownloads(fileName: "dictionary.mmap")
+//        
+//        try transaction.commit()
+//    }
+//    
+//    func testQueryGuelphSubredditCorpus() async throws {
+//        let documentEncoder = ContextFreeEncoder<Double>(source: .glove6B50d)
+//        
+//        /// Setting up the environment
+//        let env = try Environment()
+//        try env.setMapSize(ONE_GB)
+//        try env.setMaxReaders(DEFAULT_MAXREADERS)
+//        try env.setMaxDBs(DEFAULT_MAXDBS)
+//        try env.open(path: workingDirectoryPath)
+//        
+//        /// Reading the memory map (and dictionary) from disk
+//        let transaction = try Transaction.begin(.read, in: env)
+//        
+//        let corpus = try DurableHNSWCorpus<String,ContextFreeEncoder>(
+//            encoder: documentEncoder,
+//            namespace: "subreddit_durable",
+//            in: transaction
+//        )
+//        
+//        corpus.dictionary = DurableHNSWCorpus.readDictionaryFromDownloads(fileName: "dictionary.mmap")
+//        
+//        let query = "I love waterloo and I love the geese."
+//        let queryVector: [Double] = documentEncoder.encodeToken(query).map { Double($0) }
+//        
+//        let result = try corpus.index.find(near: queryVector, limit: 8, in: transaction)
+//        
+//        for result in result {
+//            let key = Int(result.id.foreignKey)!
+//            print(corpus.getUntokenizedDocument(at: key))
+//        }
+//    }
+//}
+//#endif
+//
diff --git a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
index 697a75079d5d52d6dfee48d6caa2936d1a93ba19..e1f236ea9e4ddd6455cbb5effca8615d73ba5bbf 100644
--- a/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
+++ b/Tests/SwiftNLPTests/AllMiniLM_sampleTest.swift
@@ -1,68 +1,75 @@
-//#if canImport(CoreML)
-//import XCTest
-//@testable import SwiftNLP
-//@testable import SwiftAnnoy
-//
-//final class BERT_test: XCTestCase {
-//    
-//    
-//    func testBasicExample() async throws {
-//        
-//        let docs = [
-//            "The quick brown fox jumps over the lazy dog",
-//            "I enjoy taking long walks along the beach at sunset",
-//            "Advances in neural networks have enabled new AI capabilities",
-//            "The stock market experienced a significant downturn last week",
-//            "Cooking a good meal can be both an art and a science",
-//            "The exploration of space is both challenging and rewarding",
-//            "Machine learning models are becoming increasingly sophisticated",
-//            "I love reading about history and ancient civilizations"
-//        ]
-//        
-//        let query = [
-//            "I like to read about new technology and artificial intelligence"
-//        ]
-//        //        let docs = ["cat dog", "bee fly"]
-//        
-//        var database_embedding: [[Float]] = []
-//        var query_embedding: [Float] = []
-//        var embedding_dim: Int = 384
-//        
-//        var model = MiniLMEmbeddings()
-//        
-//        query_embedding = await model.encode(sentence: query[0])!
-//        
-//        var i = 1
-//        //append sentence embedding to database_embedding
-//        for string in docs {
-//            if let vector = await model.encode(sentence: string) {
-//                database_embedding.append(vector)
-//                //print(i)
-//                i += 1
-//            } else {
-//                fatalError("Error occurred1")
-//            }
-//            
-//        }
-//        
-//        let index = AnnoyIndex<Float>(itemLength: embedding_dim, metric: .euclidean)
-//        
-//        try? index.addItems(items: &database_embedding)
-//        try? index.build(numTrees: 50)
-//        
-//        let results = index.getNNsForVector(vector: &query_embedding, neighbors: 8)
-//        
-////        if let finalresult = results {
-////            let extractedIndeices = finalresult.indices
-////            for index in extractedIndeices {
-////                if index < docs.count {
-////                    print(docs[index])
-////                } else {
-////                    print("Index \(index) out of range.")
-////                }
-////            }
-////        }
-//        print(results)
-//    }
-//}
-//#endif
+#if canImport(CoreML)
+import XCTest
+
+import Darwin
+
+@testable import SwiftNLP
+@testable import SwiftAnnoy
+
+final class BERT_test: XCTestCase {
+   
+   
+    func testBasicExample() async throws {
+        setbuf(stdout, nil)
+        setbuf(stderr, nil)
+        print("running test")
+       
+        let docs = [
+            "The quick brown fox jumps over the lazy dog",
+            "I enjoy taking long walks along the beach at sunset",
+            "Advances in neural networks have enabled new AI capabilities",
+            "The stock market experienced a significant downturn last week",
+            "Cooking a good meal can be both an art and a science",
+            "The exploration of space is both challenging and rewarding",
+            "Machine learning models are becoming increasingly sophisticated",
+            "I love reading about history and ancient civilizations"
+        ]
+        
+        let query = [
+            "I like to read about new technology and artificial intelligence"
+        ]
+        
+        for model in ["all_MiniLM_L6_v2"] {
+            var database_embedding: [[Float]] = []
+            var query_embedding: [Float] = []
+            var embedding_dim: Int = 384
+           
+            var model = LLMEmbeddings(model_type: model)
+           
+            query_embedding = await model.encode(sentence: query[0])!
+           
+            var i = 1
+
+            for string in docs {
+                if let vector = await model.encode(sentence: string) {
+                    database_embedding.append(vector)
+                    i += 1
+                } else {
+                    fatalError("Error occurred!")
+                }
+            }
+            
+            let index = AnnoyIndex<Float>(itemLength: 384, metric: .euclidean)
+           
+            try? index.addItems(items: &database_embedding)
+            try? index.build(numTrees: 50)
+           
+            let results = index.getNNsForVector(vector: &query_embedding, neighbors: 8)
+           
+            if let finalresult = results {
+                let extractedIndeices = finalresult.indices
+                for index in extractedIndeices {
+                    if index < docs.count {
+                        print(docs[index])
+                    } else {
+                        print("Index \(index) out of range.")
+                    }
+                }
+            }
+            
+            print(results)
+            print(database_embedding)
+        }
+    }
+}
+#endif
diff --git a/adding_new_model.md b/adding_new_model.md
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+CoreML models can be registered and used automatically with the CoreMLEncoder. To add a new model:
+
+1. Add the .mlpackage file to ```Sources/SwiftNLP/Resources```.
+
+2. From the ```swiftnlp``` directory, run the following from the command line:
+    - ```xcrun coremlcompiler generate Sources/SwiftNLP/Resources/[name of mlpackage file]/ --language Swift Sources/SwiftNLPGenericLLMMacros```
+    
+    This should generate a .swift file.
+
+3. Add the following script steps to the ```.compile_models``` item in ```.gitlab-ci.yml``` file:
+    - ```xcrun coremlcompiler compile Sources/SwiftNLP/Resources/[name of mlpackage file]/ Sources/SwiftNLP/Models```
+    - ```xcrun coremlcompiler generate Sources/SwiftNLP/Resources/[name of mlpackage file]/ --language Swift Sources/SwiftNLP/Resources```
+    - ```mv Sources/SwiftNLP/Resources/[name of the generated swift file] Sources/SwiftNLP/2.\ Encoding```
+
+4. Navigate to ```Sources/SwiftNLPGenericLLMMacros/ModelClasses.swift```.
+    - Add the following to the `LLM_MODEL_CLASSES` map:
+        ```
+        "[string name of model]": [
+            LLMModelClassesKey.Input: [name of model input class].self,
+            LLMModelClassesKey.Output: [name of model output class].self,
+            LLMModelClassesKey.Model: [name of model class].self,
+            LLMModelClassesKey.FeatureName: [name of feature to use],
+            LLMModelClassesKey.URL: "[name of ml package file without .mlpackage].mlmodelc",
+            LLMModelClassesKey.InputDimension: [input size]
+        ]
+        ```
+    - Notes on how to perform the above step:
+        - [string name of model] can be any string you want.
+        - the [name of model input class], [name of model output class], and [name of model class] can all be retrieved from the generated swift file. They are the names of the classes present in that file.
+        - the name of the feature to use is present in the model output class. Typically, it's `embeddings` though some models may have other fields like `pooler_output`.
+        - the [input size] can be retrieved from the auto-generated documentation in the model input class.
+
+5. Build the project, and the model can now be used anywhere. To access the model, instantiata an `LLMEmbedings` object with the model's string identifier. If using the CoreMLEncoder, set its `model` field to the model's string identifier.
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