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be85197 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | import gradio as gr
import torch
import coremltools as ct
from huggingface_hub import snapshot_download
from transformers import AutoModel, AutoTokenizer
import os
import tempfile
import shutil
def convert_to_coreml(model_name, input_length=128, ios_version="iOS15"):
try:
print(f"Downloading model {model_name}...")
# Download model from Huggingface
model_path = snapshot_download(repo_id=model_name)
print("Loading model...")
# Load model and tokenizer
model = AutoModel.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
# Set model to evaluation mode
model.eval()
print("Creating example input...")
# Create example input
if hasattr(tokenizer, "pad_token") and tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
sample_text = "This is a sample input for conversion"
inputs = tokenizer(sample_text, return_tensors="pt", padding="max_length", max_length=input_length)
print("Tracing model...")
# Trace the model
traced_model = torch.jit.trace(model, [inputs["input_ids"], inputs["attention_mask"]])
# Define Core ML target version
if ios_version == "iOS15":
target = ct.target.iOS15
elif ios_version == "iOS16":
target = ct.target.iOS16
else:
target = ct.target.iOS17
print(f"Converting to Core ML (targeting {ios_version})...")
# Convert to Core ML
mlmodel = ct.convert(
traced_model,
inputs=[
ct.TensorType(name="input_ids", shape=inputs["input_ids"].shape),
ct.TensorType(name="attention_mask", shape=inputs["attention_mask"].shape)
],
minimum_deployment_target=target
)
# Create a temporary directory to save the model
temp_dir = tempfile.mkdtemp()
model_file = os.path.join(temp_dir, f"{model_name.split('/')[-1]}.mlmodel")
print(f"Saving model to {model_file}...")
# Save the model
mlmodel.save(model_file)
return model_file, "Conversion successful!"
except Exception as e:
return None, f"Error: {str(e)}"
def process(model_name, input_length, ios_version):
model_file, message = convert_to_coreml(model_name, int(input_length), ios_version)
if model_file:
return message, model_file
else:
return message, None
# Create Gradio interface
with gr.Blocks(title="Huggingface to Core ML Converter") as demo:
gr.Markdown("# Huggingface to Core ML Model Converter")
gr.Markdown("Enter a Huggingface model ID and convert it to Core ML format.")
with gr.Row():
with gr.Column():
model_name = gr.Textbox(label="Huggingface Model ID (e.g., 'your-username/your-model')")
input_length = gr.Slider(minimum=16, maximum=512, value=128, step=16, label="Input Length")
ios_version = gr.Dropdown(choices=["iOS15", "iOS16", "iOS17"], value="iOS15", label="Target iOS Version")
convert_button = gr.Button("Convert Model")
with gr.Column():
output_message = gr.Textbox(label="Status")
file_output = gr.File(label="Converted Model")
convert_button.click(
process,
inputs=[model_name, input_length, ios_version],
outputs=[output_message, file_output]
)
demo.launch() |