Hello,
I was successfully able to compile TKDKid1000/TinyLlama-1.1B-Chat-v0.3-CoreML using Core ML, and it's working well. However, I’m now trying to compile the same model using Swift Transformers.
With the limited documentation available on the swift-chat and Hugging Face repositories, I’m finding it difficult to understand the correct process for compiling a model via Swift Transformers. I attempted the following approach, but I’m fairly certain it’s not the recommended or correct method.
Could someone guide me on the proper way to compile and use models like TinyLlama with Swift Transformers? Any official workflow, example, or best practice would be very helpful.
Thanks in advance!
This is the approach I have used:
import Foundation
import CoreML
import Tokenizers
@main
struct HopeApp {
static func main() async {
print(" Running custom decoder loop...")
do {
let tokenizer = try await AutoTokenizer.from(pretrained: "PY007/TinyLlama-1.1B-Chat-v0.3")
var inputIds = tokenizer("this is the test of the prompt")
print("🧠 Prompt token IDs:", inputIds)
let model = try float16_model(configuration: .init())
let maxTokens = 30
for _ in 0..<maxTokens {
let input = try MLMultiArray(shape: [1, 128], dataType: .int32)
let mask = try MLMultiArray(shape: [1, 128], dataType: .int32)
for i in 0..<inputIds.count {
input[i] = NSNumber(value: inputIds[i])
mask[i] = 1
}
for i in inputIds.count..<128 {
input[i] = 0
mask[i] = 0
}
let output = try model.prediction(input_ids: input, attention_mask: mask)
let logits = output.logits // shape: [1, seqLen, vocabSize]
let lastIndex = inputIds.count - 1
let lastLogitsStart = lastIndex * 32003 // vocab size = 32003
var nextToken = 0
var maxLogit: Float32 = -Float.greatestFiniteMagnitude
for i in 0..<32003 {
let logit = logits[lastLogitsStart + i].floatValue
if logit > maxLogit {
maxLogit = logit
nextToken = i
}
}
inputIds.append(nextToken)
if nextToken == 32002 { break }
let partialText = try await tokenizer.decode(tokens:inputIds)
print(partialText)
}
} catch {
print("❌ Error: \(error)")
}
}
}
Topic:
Machine Learning & AI
SubTopic:
Core ML