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--- |
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license: apache-2.0 |
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--- |
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# Neural Krishna DPO |
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## Fine-tuning + lnegth(choose) |
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- Training Args: |
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´´´python |
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# LoRA configuration |
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peft_config = LoraConfig( |
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r=16, |
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lora_alpha=16, |
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lora_dropout=0.05, |
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bias="none", |
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task_type="CAUSAL_LM", |
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target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj'] |
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) |
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# Model to fine-tune |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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load_in_4bit=True |
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) |
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model.config.use_cache = False |
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# Training arguments |
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training_args = TrainingArguments( |
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per_device_train_batch_size=4, |
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gradient_accumulation_steps=4, |
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gradient_checkpointing=True, |
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learning_rate=5e-5, |
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lr_scheduler_type="cosine", |
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max_steps=120, |
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save_strategy="no", |
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logging_steps=1, |
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output_dir=new_model, |
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optim="paged_adamw_32bit", |
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warmup_steps=50, |
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bf16=True, |
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report_to="wandb", |
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) |
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# Create DPO trainer |
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dpo_trainer = DPOTrainer( |
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model, |
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args=training_args, |
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train_dataset=dataset, |
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tokenizer=tokenizer, |
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peft_config=peft_config, |
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beta=0.1, |
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max_prompt_length=1024, |
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max_length=1536, |
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) |
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# Fine-tune model with DPO |
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dpo_trainer.train() |
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´´´ |
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