Fine-tuning efisien dalam RLHF

Reinforcement Learning from Human Feedback (RLHF)

Mina Parham

AI Engineer

Fine-tuning hemat parameter

  • Fine-tuning seluruh model

Ilustrasi jaringan saraf.

Reinforcement Learning from Human Feedback (RLHF)

Fine-tuning hemat parameter

  • Fine-tuning dengan PEFT

Ilustrasi jaringan saraf dengan sebagian besar parameter dibekukan.

  • LoRA: menyesuaikan beberapa lapisan saja
  • Kuantisasi: menurunkan presisi tipe data
Reinforcement Learning from Human Feedback (RLHF)

Langkah 1: muat model aktif dalam presisi 8-bit

from peft import prepare_model_for_int8_training


pretrained_model = AutoModelForCausalLM.from_pretrained(
                                                        model_name, 
                                                        load_in_8bit=True
                                                       )

pretrained_model_8bit = prepare_model_for_int8_training(pretrained_model)
Reinforcement Learning from Human Feedback (RLHF)

Langkah 2: tambahkan adapter terlatih tambahan dengan peft

from peft import LoraConfig, get_peft_model


config = LoraConfig(

    r=32, # Rank of the low-rank matrices

    lora_alpha=32, # Scaling factor for the LoRA updates

    lora_dropout=0.1, # Dropout rate for LoRA layers

    bias="lora_only"# Only update bias terms for LoRA layers, others remain frozen

)


lora_model = get_peft_model(pretrained_model_8bit, config)
model = AutoModelForCausalLMWithValueHead.from_pretrained(lora_model)
Reinforcement Learning from Human Feedback (RLHF)

Langkah 3: gunakan satu model untuk logit referensi dan aktif

ppo_trainer = PPOTrainer(
    config, # The config we just defined
    model, # Our PPO model
    ref_model=None, 
    tokenizer=tokenizer, 
    dataset=dataset, 
    data_collator=collator, 
    optimizer=optimizer
)
Reinforcement Learning from Human Feedback (RLHF)

Ayo berlatih!

Reinforcement Learning from Human Feedback (RLHF)

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