Deploying a Fine-Tuned Llama Model to an iOS App with MLC-LLM#
MLC-LLM is a powerful ML compiler and high-performance deployment engine designed specifically for LLMs. It enables deployment of models across various platforms, including iOS, Android, web browsers, and as a Python or REST API.
This tutorial guides you through the process of fine-tuning and deploying a Llama 3 8B Instruct model to an iOS app using Union and MLC-LLM.
Start by importing the necessary libraries and modules:
Run on Union BYOC
Once you have a Union account, install union:
pip install union
Export the following environment variable to build and push images to your own container registry:
# replace with your registry name
export IMAGE_SPEC_REGISTRY="<your-container-registry>"
Then run the following commands to run the workflow:
git clone https://github.com/unionai/unionai-examples
cd unionai-examples/tutorials/llama_edge_deployment
# create a huggingface key: https://huggingface.co/settings/tokens, then run the following command
union secrets create hf-api-key --value <your_huggingface_api_key>
# create a weights and biases key: https://wandb.ai/settings, then run the following command
union secrets create wandb-api-key --value <your_wandb_api_key>
# ensure you replace the placeholders `WANDB_PROJECT`, `WANDB_ENTITY`, and `HF_REPO_ID` with the actual values.
union register llama_edge_deployment.py
union launchplan finetuning_completion_trigger --activate
# run the fine-tuning workflow through the UI or by using the following command:
union run --remote --copy-all llama_edge_deployment.py finetuning_wf
The source code for this tutorial can be found here .
import subprocess
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Annotated
from flytekit import LaunchPlan, Resources, Secret, current_context, task, workflow
from flytekit.core.artifact import Inputs
from flytekit.extras.accelerators import A100, L4
from flytekit.types.directory import FlyteDirectory
from flytekitplugins.wandb import wandb_init
from union.artifacts import OnArtifact
from .utils import (
ModelArtifact,
download_artifacts_image,
llm_mlc_image,
model_training_image,
modelcard_image,
)
Creating secrets and defining a dataclass#
To securely manage your Weights and Biases and HuggingFace tokens, create secrets using the following commands:
union create secret wandb-api-key
union create secret hf-api-key
Replace the placeholders WANDB_PROJECT, WANDB_ENTITY, and HF_REPO_ID with the actual values for your Weights & Biases
project and entity settings, as well as the Hugging Face repository ID, before running the workflow.
WANDB_SECRET = Secret(key="wandb-api-key")
WANDB_PROJECT = "<WANDB_PROJECT>"
WANDB_ENTITY = "<WANDB_ENTITY>"
We also define a TrainingArguments dataclass that encapsulates the training parameters for fine-tuning the model.
@dataclass
class TrainingArguments:
output_dir: str
per_device_train_batch_size: int = 2
per_device_eval_batch_size: int = 2
gradient_accumulation_steps: int = 4
optim: str = "paged_adamw_8bit"
num_train_epochs: int = 1
evaluation_strategy: str = "steps"
eval_steps: float = 0.2
logging_steps: int = 1
warmup_steps: int = 10
logging_strategy: str = "steps"
learning_rate: float = 8e-6
fp16: bool = True
bf16: bool = False
group_by_length: bool = True
report_to: str = "wandb"
Downloading dataset and model#
First, download the Llama 3 8B Instruct model from the Hugging Face hub, along with the Cohere Aya dataset. This dataset contains a diverse collection of prompts and completions across multiple languages.
The tasks are set up to cache the model and dataset, preventing redundant downloads in future runs.
@task(
cache=True,
cache_version="0.1",
container_image=download_artifacts_image,
)
def download_dataset(dataset: str, language: str) -> FlyteDirectory:
from datasets import load_dataset
working_dir = Path(current_context().working_directory)
cached_dataset_dir = working_dir / "cached_dataset"
load_dataset(dataset, language, cache_dir=cached_dataset_dir)
return cached_dataset_dir
@task(
cache=True,
cache_version="0.1",
requests=Resources(mem="10Gi"),
secret_requests=[Secret(key="hf-api-key")],
container_image=download_artifacts_image,
)
def download_model(model_name: str) -> FlyteDirectory:
from huggingface_hub import login, snapshot_download
ctx = current_context()
working_dir = Path(ctx.working_directory)
cached_model_dir = working_dir / "cached_model"
login(token=ctx.secrets.get(key="hf-api-key"))
snapshot_download(model_name, local_dir=cached_model_dir)
return cached_model_dir
Fine-tuning Llama 3#
We leverage Quantized Low-Rank Adapters (QLoRA) to accelerate the fine-tuning process while minimizing memory usage.
An A100 GPU, available on Union Serverless, will handle the heavy lifting.
We will also set up Weights and Biases to monitor the model’s performance throughout the fine-tuning process.
After training, we will save the adapters and return them as a FlyteDirectory.
For this fine-tuning, we will start with 1,000 samples.
@task(
cache=True,
cache_version="0.2",
container_image=model_training_image,
accelerator=A100,
requests=Resources(mem="20Gi", gpu="1", cpu="5"),
secret_requests=[WANDB_SECRET],
environment={"TOKENIZERS_PARALLELISM": "false"},
)
@wandb_init(project=WANDB_PROJECT, entity=WANDB_ENTITY, secret=WANDB_SECRET)
def train_model(
train_args: TrainingArguments,
dataset_dir: FlyteDirectory,
model_dir: FlyteDirectory,
) -> FlyteDirectory:
import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
)
from trl import SFTTrainer, setup_chat_format
dataset_dir.download()
model_dir.download()
# QLoRA config
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
)
# Load model
model = AutoModelForCausalLM.from_pretrained(
model_dir.path,
quantization_config=bnb_config,
device_map="auto",
attn_implementation="eager",
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_dir.path)
model, tokenizer = setup_chat_format(model, tokenizer)
# LoRA config
peft_config = LoraConfig(
r=16,
lora_alpha=32,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=[
"up_proj",
"down_proj",
"gate_proj",
"k_proj",
"q_proj",
"v_proj",
"o_proj",
],
)
model = get_peft_model(model, peft_config)
# Importing the dataset
dataset = load_dataset(dataset_dir.path)
# Select train and test datasets from the loaded split
train_dataset = dataset["train"]
test_dataset = dataset.get("test", None) # In case there's no test split
# Shuffle and select a subset (optional)
train_dataset = train_dataset.shuffle(seed=65).select(range(1000))
test_dataset = test_dataset.shuffle(seed=65).select(range(1000))
def format_chat_template(row):
row_json = [
{"role": "user", "content": row["inputs"]},
{"role": "assistant", "content": row["targets"]},
]
row["text"] = tokenizer.apply_chat_template(row_json, tokenize=False)
return row
# Apply the format_chat_template function in parallel (num_proc=4)
train_dataset = train_dataset.map(
format_chat_template,
num_proc=4,
)
# Apply format_chat_template to the test dataset if it exists
if test_dataset is not None:
test_dataset = test_dataset.map(
format_chat_template,
num_proc=4,
)
parser = HfArgumentParser(TrainingArguments)
hf_training_args = parser.parse_dict(asdict(train_args))[0]
# Trainer setup
trainer = SFTTrainer(
model=model,
train_dataset=train_dataset,
eval_dataset=test_dataset,
peft_config=peft_config,
max_seq_length=512,
dataset_text_field="text",
tokenizer=tokenizer,
args=hf_training_args,
packing=False,
)
trainer.train()
trainer.model.save_pretrained(train_args.output_dir)
return FlyteDirectory(train_args.output_dir)
Merging adapter with the base model#
This task allows us to serve the fully integrated model as an iOS app later on. We will use an L4 GPU for this step, as the A100 is not necessary for merging the models. The task returns an Artifact that includes both the model and the dataset partitions.
@task(
cache=True,
cache_version="0.3",
container_image=model_training_image,
requests=Resources(mem="20Gi", gpu="1"),
accelerator=L4,
)
def merge_model(
model_name: str,
dataset: str,
model_dir: FlyteDirectory,
adapter_dir: FlyteDirectory,
) -> Annotated[
FlyteDirectory, ModelArtifact(model=Inputs.model_name, dataset=Inputs.dataset)
]:
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
from trl import setup_chat_format
model_dir.download()
adapter_dir.download()
# Reload tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_dir.path)
base_model_reload = AutoModelForCausalLM.from_pretrained(
model_dir.path,
return_dict=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True,
)
base_model_reload, tokenizer = setup_chat_format(base_model_reload, tokenizer)
# Merge adapter with base model
model = PeftModel.from_pretrained(base_model_reload, adapter_dir.path)
model = model.merge_and_unload()
ctx = current_context()
working_dir = Path(ctx.working_directory)
merged_model = working_dir / "merged_model"
model.save_pretrained(merged_model)
tokenizer.save_pretrained(merged_model)
return FlyteDirectory(merged_model)
Convert model weights to MLC format#
To get our merged model operational with MLC-LLM, we need to convert the model weights into the MLC format.
This involves running two commands:
mlc_llm convert_weight to transform the weights, and mlc_llm gen_config to generate the chat configuration and process the tokenizers.
@task(
cache=True,
cache_version="0.1",
requests=Resources(mem="20Gi", gpu="1"),
accelerator=L4,
container_image=llm_mlc_image,
)
def convert_model_weights_to_mlc(
merged_model_dir: FlyteDirectory, conversion_template: str, quantization: str
) -> FlyteDirectory:
merged_model_dir.download()
output_dir = Path(current_context().working_directory)
subprocess.run(
[
"python",
"-m",
"mlc_llm",
"convert_weight",
merged_model_dir.path,
"--quantization",
quantization,
"-o",
str(output_dir / "finetuned-model-MLC"),
],
check=True,
)
subprocess.run(
[
"python",
"-m",
"mlc_llm",
"gen_config",
merged_model_dir.path,
"--quantization",
quantization,
"--conv-template",
conversion_template,
"-o",
str(output_dir / "finetuned-model-MLC"),
],
check=True,
)
return FlyteDirectory(str(output_dir / "finetuned-model-MLC"))
Push the model to HuggingFace#
We upload the model weights to HuggingFace for easy access while building the iOS app.
@task(
cache=True,
cache_version="0.1",
container_image=modelcard_image,
requests=Resources(mem="10Gi", cpu="2"),
secret_requests=[Secret(key="hf-api-key")],
)
def push_to_hf(model_directory: FlyteDirectory, hf_repo_id: str) -> str:
from huggingface_hub import create_repo, repo_exists, upload_folder
hf_token = current_context().secrets.get(key="hf-api-key")
# Check if the repository already exists
if not repo_exists(hf_repo_id):
create_repo(
hf_repo_id,
private=False,
token=hf_token,
repo_type="model",
)
else:
print(f"Repository '{hf_repo_id}' already exists.")
model_directory.download()
upload_folder(
repo_id=hf_repo_id,
folder_path=model_directory.path,
path_in_repo=None,
commit_message="Upload MLC weights",
token=hf_token,
)
model_url = f"https://huggingface.co/{hf_repo_id}"
return model_url
Create workflows and artifact trigger#
We define two workflows: one for fine-tuning and another for generating weights compatible with MLC. We also create a launch plan to execute the conversion workflow once the fine-tuned model artifact is generated.
@workflow
def finetuning_wf(
dataset: str = "CohereForAI/aya_collection_language_split",
language: str = "telugu",
model_name: str = "meta-llama/Meta-Llama-3-8B-Instruct",
train_args: TrainingArguments = TrainingArguments(
output_dir="finetuned-llama-3-8b"
),
) -> FlyteDirectory:
dataset_dir = download_dataset(dataset=dataset, language=language)
model_dir = download_model(model_name=model_name)
adapter_dir = train_model(
train_args=train_args, dataset_dir=dataset_dir, model_dir=model_dir
)
return merge_model(
model_name=model_name,
dataset=dataset,
model_dir=model_dir,
adapter_dir=adapter_dir,
)
@workflow
def convert_to_mlc_wf(
merged_model_dir: FlyteDirectory,
conversion_template: str = "llama-3", # ref: https://github.com/mlc-ai/mlc-llm/tree/main/python/mlc_llm/conversation_template
quantization: str = "q4f16_1", # quantize the model to 4-bit float representation
hf_repo_id: str = "<HF_REPO_ID>",
) -> str:
mlc_weights = convert_model_weights_to_mlc(
merged_model_dir=merged_model_dir,
conversion_template=conversion_template,
quantization=quantization,
)
return push_to_hf(model_directory=mlc_weights, hf_repo_id=hf_repo_id)
LaunchPlan.create(
"finetuning_completion_trigger",
convert_to_mlc_wf,
trigger=OnArtifact(
trigger_on=ModelArtifact,
inputs={
"merged_model_dir": ModelArtifact.query(
model="meta-llama/Meta-Llama-3-8B-Instruct",
dataset="CohereForAI/aya_collection_language_split",
)
},
),
)
Building the iOS app#
To build the iOS app, ensure you are working on macOS, as it has essential dependencies that need to be installed. The installation script and the necessary code are available in the GitHub repository, which you can access via the link in the dropdown menu at the top of the page.
The mlc_llm package command compiles the model, builds the runtime and tokenizer, and creates a dist/ directory inside the MLCChat folder.
We bundle the model weights directly into the app to avoid downloading them from Hugging Face each time the app runs, significantly speeding up the process.
Next, open ./ios/MLCChat/MLCChat.xcodeproj using Xcode (ensure Xcode is installed and you’ve accepted its terms and conditions).
You will also need an active Apple Developer account, as Xcode may prompt you for your developer team credentials and to set up a product bundle identifier.
If you’re testing the app, follow these steps:
Go to Product > Scheme > Edit Scheme and replace “Release” with “Debug” under “Run”.
Skip adding developer certificates.
Use this bundle identifier pattern:
com.yourname.MLCChat.Remove the “Extended Virtual Addressing” capability under the Target section.
Your app should now be ready for testing!