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import modal
import os
app = modal.App("finetune-census-phi3-simple")
# Volumes
vol_dataset = modal.Volume.from_name("finetune-dataset")
vol_checkpoints = modal.Volume.from_name("model-checkpoints", create_if_missing=True)
# Simple image without Unsloth - just use standard HuggingFace + PEFT
image = modal.Image.debian_slim(python_version="3.10") \
.pip_install(
"torch==2.4.0",
"transformers==4.41.2",
"peft==0.11.1",
"trl==0.9.4",
"accelerate==0.30.1",
"bitsandbytes==0.43.1",
"datasets==2.19.2",
"scipy==1.13.1"
)
@app.function(
image=image,
volumes={
"/data/dataset": vol_dataset,
"/data/checkpoints": vol_checkpoints
},
gpu="A100-80GB",
timeout=86400,
)
def finetune():
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
from trl import SFTTrainer
from datasets import load_dataset
import torch
print("🚀 Starting Fine-tuning Job...")
# Configuration
model_name = "microsoft/Phi-3-mini-4k-instruct"
max_seq_length = 2048
# Load model with 4-bit quantization
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
)
print(f"Loading model: {model_name}")
model = AutoModelForCausalLM.from_pretrained(
model_name,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
# Prepare model for training
model = prepare_model_for_kbit_training(model)
# LoRA configuration
peft_config = LoraConfig(
r=16,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Load dataset
print("Loading dataset...")
dataset = load_dataset("json", data_files={
"train": "/data/dataset/train.jsonl",
"test": "/data/dataset/val.jsonl"
})
# Formatting function
def formatting_prompts_func(examples):
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
instructions = examples["instruction"]
inputs = examples["input"]
outputs = examples["output"]
texts = []
for instruction, input_text, output in zip(instructions, inputs, outputs):
text = alpaca_prompt.format(instruction, input_text, output) + tokenizer.eos_token
texts.append(text)
return {"text": texts}
dataset = dataset.map(formatting_prompts_func, batched=True)
# Training arguments
training_args = TrainingArguments(
output_dir="/tmp/outputs",
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
learning_rate=2e-4,
logging_steps=10,
max_steps=100, # Short test run
save_steps=50,
save_total_limit=2,
fp16=True,
optim="paged_adamw_8bit",
warmup_steps=5,
lr_scheduler_type="linear",
)
# Trainer
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset["train"],
eval_dataset=dataset["test"],
dataset_text_field="text",
max_seq_length=max_seq_length,
tokenizer=tokenizer,
packing=False,
)
# Train
print("Starting training...")
trainer.train()
# Save
print("Saving model...")
model.save_pretrained("/data/checkpoints/phi3-census-lora")
tokenizer.save_pretrained("/data/checkpoints/phi3-census-lora")
vol_checkpoints.commit()
print("✅ Fine-tuning Complete!")
@app.local_entrypoint()
def main():
finetune.remote()
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