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Finetuning

Adapting Foundation Models for specific tasks.

When to Finetune

DO Finetune

• Improve quality on specific domain
• Reduce latency (smaller model)
• Reduce cost (fewer tokens)
• Ensure consistent style
• Add specialized capabilities

DON'T Finetune

• Prompt engineering is enough
• Insufficient data (<1000 examples)
• Need frequent updates
• RAG can solve the problem

Memory Requirements

def training_memory_gb(num_params_billion, precision="fp16"):
    bytes_per = {"fp32": 4, "fp16": 2, "int8": 1}

    model = num_params_billion * 1e9 * bytes_per[precision]
    optimizer = num_params_billion * 1e9 * 4 * 2  # AdamW states
    gradients = num_params_billion * 1e9 * bytes_per[precision]

    return (model + optimizer + gradients) / 1e9

# 7B model full finetuning: ~112 GB!
# With LoRA: ~16 GB
# With QLoRA: ~6 GB

LoRA (Low-Rank Adaptation)

from peft import LoraConfig, get_peft_model

config = LoraConfig(
    r=8,                          # Rank (lower = fewer params)
    lora_alpha=32,                # Scaling factor
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    task_type="CAUSAL_LM"
)

model = get_peft_model(base_model, config)

# ~0.06% of 7B trainable!
trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)

QLoRA (4-bit + LoRA)

from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

model = get_peft_model(model, lora_config)
# 7B on 16GB GPU!

Training

from transformers import Trainer, TrainingArguments

args = TrainingArguments(
    output_dir="./results",
    num_train_epochs=3,
    per_device_train_batch_size=4,
    gradient_accumulation_steps=4,
    learning_rate=2e-5,
    warmup_steps=100,
    fp16=True,
    gradient_checkpointing=True,
    optim="paged_adamw_8bit"
)

trainer = Trainer(
    model=model,
    args=args,
    train_dataset=train_data,
    eval_dataset=eval_data
)

trainer.train()

# Merge LoRA back
merged = model.merge_and_unload()
merged.save_pretrained("./finetuned")

Model Merging

Task Arithmetic

def task_vector_merge(base, finetuned_models, scale=0.3):
    merged = base.state_dict()
    for ft in finetuned_models:
        for key in merged:
            task_vector = ft.state_dict()[key] - merged[key]
            merged[key] += scale * task_vector
    return merged

Best Practices

1. Start with small rank (r=8)
2. Use QLoRA for limited GPU
3. Monitor validation loss
4. Test merged models carefully
5. Keep base model for comparison

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Repository

doanchienthangdev/omgkit

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