partial fix for #1

- handle loading models with different names for the safetensors files (gemma)
- handle merge tokens that can't be split
- organize code into Load/Evaluate

Libraries/LLM/Evaluate.swift

@@ -0,0 +1,72 @@
+// Copyright © 2024 Apple Inc.
+
+import AsyncAlgorithms
+import Foundation
+import MLX
+import MLXRandom
+
+private func sample(logits: MLXArray, temp: Float) -> MLXArray {
+    if temp == 0 {
+        return argMax(logits, axis: -1)
+    } else {
+        return categorical(logits * (1 / temp))
+    }
+}
+
+/// Synchronous generator of tokens.
+///
+/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py
+public struct TokenIterator: Sequence, IteratorProtocol {
+    let model: LLMModel
+    let temp: Float
+
+    var y: MLXArray
+    var cache: [(MLXArray, MLXArray)]
+
+    var first = true
+
+    public init(prompt: MLXArray, model: LLMModel, temp: Float = 0.0) {
+        self.model = model
+        self.temp = temp
+        self.y = prompt
+        self.cache = []
+    }
+
+    mutating public func next() -> MLXArray? {
+        var logits: MLXArray
+        (logits, cache) = model(expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
+        y = sample(logits: logits[-1, axis: 1], temp: temp)
+
+        return y
+    }
+}
+
+/// Async generator of tokens.
+///
+/// Port of `generate_step()` from https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/utils.py.
+///
+/// Note that because MLXArray is not thread safe this eval's the result and sends the TokenId back
+/// to the caller.
+public func generate(prompt: MLXArray, model: LLMModel, temp: Float = 0.0) -> (
+    Task<Void, Never>, AsyncBufferSequence<AsyncChannel<Int>>
+) {
+    let channel = AsyncChannel<Int>()
+    let buffer = channel.buffer(policy: .bounded(10))
+
+    let task = Task {
+        var y = prompt
+        var cache = [(MLXArray, MLXArray)]()
+
+        while !Task.isCancelled {
+            var logits: MLXArray
+            (logits, cache) = model(
+                expandedDimensions(y, axis: 0), cache: cache.isEmpty ? nil : cache)
+            y = sample(logits: logits[-1, axis: 1], temp: temp)
+            eval(y)
+
+            await channel.send(y.item(Int.self))
+        }
+    }
+
+    return (task, buffer)
+}