| |
| import huggingface_hub |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| import torch |
|
|
| import os |
| import argparse |
|
|
| parser = argparse.ArgumentParser( |
| prog='loraize', |
| description='Apply one or more loras to a model, and then save it', |
| epilog='BOTTOM TEXT') |
| parser.add_argument( |
| 'model', |
| type=str, |
| help='path or HF name of a base model', |
| ) |
| parser.add_argument( |
| 'lora', |
| type=str, |
| help='one or more LORAs to apply', |
| nargs='+') |
| parser.add_argument( |
| 'output_dir', |
| type=str, |
| help='output directory', |
| ) |
| args = parser.parse_args() |
|
|
| print(f"Loading bassoon model:", args.model) |
| base_model = AutoModelForCausalLM.from_pretrained( |
| args.model, |
| return_dict=True, |
| torch_dtype=torch.bfloat16, |
| device_map="cpu", |
| ) |
|
|
| for lora in args.lora: |
| print(f"Loading LORA: ",lora) |
| model = PeftModel.from_pretrained( |
| base_model, |
| lora, |
| device_map="cpu" |
| ) |
| print(f"Good luck, bitches. Unloading.") |
| print("This gon' take a sec.") |
| model = model.merge_and_unload() |
| tokenizer = AutoTokenizer.from_pretrained(args.model) |
|
|
| model.save_pretrained(args.output_dir, safe_serialization=True, max_shard_size='10GB') |
| tokenizer.save_pretrained(args.output_dir) |
|
|
|
|
