| from typing import Optional |
|
|
|
|
| def create_dummy_lora( |
| model_path: str, |
| checkpoint_job_dir: str, |
| lora_rank: int, |
| lora_alpha: int, |
| target_modules: str, |
| exclude_modules: Optional[str] = None, |
| ) -> str: |
| import torch |
| from peft import LoraConfig, TaskType, get_peft_model |
| from transformers import AutoConfig, AutoModelForCausalLM |
|
|
| config = AutoConfig.from_pretrained(model_path) |
| model = AutoModelForCausalLM.from_config(config) |
| lora_config = { |
| "task_type": TaskType.CAUSAL_LM, |
| "r": lora_rank, |
| "lora_alpha": lora_alpha, |
| "target_modules": target_modules, |
| "exclude_modules": exclude_modules, |
| "bias": "none", |
| } |
| peft_model = get_peft_model(model, LoraConfig(**lora_config)) |
| peft_model.save_pretrained(f"{checkpoint_job_dir}/dummy_lora") |
| del model, peft_model |
| torch.cuda.empty_cache() |
| return f"{checkpoint_job_dir}/dummy_lora" |
|
|
