swift-format!

Libraries/LLM/Load.swift

@@ -14,7 +14,8 @@ struct LLMError: Error {
 
 /// Load and return the model and tokenizer
 public func load(
-    hub: HubApi = HubApi(), configuration: ModelConfiguration, progressHandler: @escaping (Progress) -> Void = { _ in }
+    hub: HubApi = HubApi(), configuration: ModelConfiguration,
+    progressHandler: @escaping (Progress) -> Void = { _ in }
 ) async throws -> (LLMModel, Tokenizer) {
     // note: this doesn't have a way to pass the HubApi
     let tokenizer = try await loadTokenizer(configuration: configuration)

Libraries/LLM/Models.swift

@@ -19,7 +19,9 @@ public struct ModelConfiguration {
     /// format
     private let preparePrompt: ((String) -> String)?
 
-    public init(id: String, overrideTokenizer: String? = nil, preparePrompt: ((String) -> String)? = nil) {
+    public init(
+        id: String, overrideTokenizer: String? = nil, preparePrompt: ((String) -> String)? = nil
+    ) {
         self.id = id
         self.overrideTokenizer = overrideTokenizer
         self.preparePrompt = preparePrompt
@@ -56,15 +58,13 @@ extension ModelConfiguration {
 
     static let codeLlama13b4bit = ModelConfiguration(
         id: "mlx-community/CodeLlama-13b-Instruct-hf-4bit-MLX",
-        overrideTokenizer: "PreTrainedTokenizer")
-    { prompt in
+        overrideTokenizer: "PreTrainedTokenizer"
+    ) { prompt in
         // given the prompt: func sortArray(_ array: [Int]) -> String { <FILL_ME> }
         // the python code produces this (via its custom tokenizer):
         // <PRE> func sortArray(_ array: [Int]) -> String {  <SUF> } <MID>
 
-        "<PRE> " +
-        prompt.replacingOccurrences(of: "<FILL_ME>", with: "<SUF>") +
-        " <MID>"
+        "<PRE> " + prompt.replacingOccurrences(of: "<FILL_ME>", with: "<SUF>") + " <MID>"
     }
 
     static let phi4bit = ModelConfiguration(id: "mlx-community/phi-2-hf-4bit-mlx") { prompt in
@@ -73,7 +73,8 @@ extension ModelConfiguration {
 
     static let gemma2bQuantized = ModelConfiguration(
         id: "mlx-community/quantized-gemma-2b-it",
-        overrideTokenizer: "PreTrainedTokenizer") { prompt in
+        overrideTokenizer: "PreTrainedTokenizer"
+    ) { prompt in
         "<start_of_turn>user \(prompt)<end_of_turn><start_of_turn>model"
     }
 

Tools/llm-tool/LLMTool.swift

@@ -57,7 +57,8 @@ struct SyncGenerator: AsyncParsableCommand {
         var tokens = [Int]()
         var printed = 0
 
-        for token in TokenIterator(prompt: MLXArray(promptTokens), model: model, temp: temperature) {
+        for token in TokenIterator(prompt: MLXArray(promptTokens), model: model, temp: temperature)
+        {
             if tokens.isEmpty {
                 eval(token)
                 let now = Date.timeIntervalSinceReferenceDate
@@ -145,7 +146,8 @@ struct AsyncGenerator: AsyncParsableCommand {
         var tokens = [Int]()
         var printed = 0
 
-        let (task, channel) = generate(prompt: MLXArray(promptTokens), model: model, temp: temperature)
+        let (task, channel) = generate(
+            prompt: MLXArray(promptTokens), model: model, temp: temperature)
 
         for await token in channel {
             if tokens.isEmpty {