File size: 3,321 Bytes
83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 | # Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is based on the HuggingFace's PEFT library.
# https://github.com/huggingface/peft/blob/v0.10.0/examples/loftq_finetuning/quantize_save_load.py
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import TYPE_CHECKING
import fire
from peft import LoftQConfig, LoraConfig, TaskType, get_peft_model
from transformers import AutoModelForCausalLM, AutoTokenizer
if TYPE_CHECKING:
from transformers import PreTrainedModel
def quantize_loftq(
model_name_or_path: str,
output_dir: str,
loftq_bits: int = 4,
loftq_iter: int = 4,
lora_alpha: int = None,
lora_rank: int = 16,
lora_dropout: float = 0,
lora_target: tuple = ("q_proj", "v_proj"),
save_safetensors: bool = True,
):
r"""Initialize LoRA weights with LoRA-fine-tuning-aware Quantization (LoftQ).
Usage: python loftq_init.py --model_name_or_path path_to_model --output_dir output_dir
"""
if isinstance(lora_target, str):
lora_target = [name.strip() for name in lora_target.split(",")]
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name_or_path, trust_remote_code=True, torch_dtype="auto")
loftq_config = LoftQConfig(loftq_bits=loftq_bits, loftq_iter=loftq_iter)
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
inference_mode=True,
r=lora_rank,
lora_alpha=lora_alpha if lora_alpha is not None else lora_rank * 2,
lora_dropout=lora_dropout,
target_modules=lora_target,
init_lora_weights="loftq",
loftq_config=loftq_config,
)
# Init LoftQ model
print("Initializing LoftQ weights, it may be take several minutes, wait patiently.")
peft_model = get_peft_model(model, lora_config)
loftq_dir = os.path.join(output_dir, "loftq_init")
# Save LoftQ model
setattr(peft_model.peft_config["default"], "base_model_name_or_path", os.path.abspath(output_dir))
setattr(peft_model.peft_config["default"], "init_lora_weights", True) # don't apply loftq again
peft_model.save_pretrained(loftq_dir, safe_serialization=save_safetensors)
print(f"Adapter weights saved in {loftq_dir}")
# Save base model
base_model: PreTrainedModel = peft_model.unload()
base_model.save_pretrained(output_dir, safe_serialization=save_safetensors)
tokenizer.save_pretrained(output_dir)
print(f"Model weights saved in {output_dir}")
print("- Fine-tune this model with:")
print(f"model_name_or_path: {output_dir}")
print(f"adapter_name_or_path: {loftq_dir}")
print("finetuning_type: lora")
print(f"quantization_bit: {loftq_bits}")
if __name__ == "__main__":
fire.Fire(quantize_loftq)
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