Spaces:
Running
Running
File size: 5,188 Bytes
e81015c |
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 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
# Copyright 2025 the LlamaFactory team.
#
# 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
import shutil
import fire
from peft import PeftModel
from transformers import AutoModel, AutoProcessor, Qwen2_5OmniThinkerForConditionalGeneration # type: ignore
def merge_lora(
base_model_path: str,
lora_checkpoint_path: str,
extra_file: str = "spk_dict.pt",
submodule_name: str = "thinker",
save_path: str = "./merged_model_checkpoint",
):
"""Load the original model, tokenizer, and processor configuration, merge the LoRA weights.
For a specified submodule, and save the final merged model along with its configurations.
Args:
base_model_path (str): Path to the original model directory.
lora_checkpoint_path (str): Path to the directory containing LoRA weights.
extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
submodule_name (str): Name of the submodule to merge (default: "thinker").
save_path (str): Directory where the merged model and configurations will be saved.
"""
# 1. Load the original model, tokenizer, and processor
model = AutoModel.from_pretrained(base_model_path, torch_dtype="auto", device_map="cpu")
processor = AutoProcessor.from_pretrained(base_model_path)
print("Successfully loaded the original model and tokenizer.")
# 2. Extract the submodule to be merged (e.g., model.thinker)
if not hasattr(model, submodule_name):
raise AttributeError(f"The model does not have a submodule named '{submodule_name}'.")
base_submodule = getattr(model, submodule_name)
print(f"Successfully extracted submodule: {submodule_name}.")
# 3. Load the LoRA weights onto the extracted submodule
lora_model = PeftModel.from_pretrained(base_submodule, lora_checkpoint_path)
print("LoRA weights loaded successfully.")
# 4. Merge the LoRA weights into the submodule and unload the LoRA modules
merged_submodule = lora_model.merge_and_unload()
print("LoRA weights merged successfully.")
# 5. Replace the original submodule with the merged submodule in the model
setattr(model, submodule_name, merged_submodule)
# 6. Save the final merged model along with the tokenizer and processor configuration
model.save_pretrained(save_path)
processor.save_pretrained(save_path)
print(f"Merged model and tokenizer saved to {save_path}.")
source_file = os.path.join(base_model_path, extra_file)
target_file = os.path.join(save_path, extra_file)
if os.path.exists(source_file):
shutil.copy(source_file, target_file)
print(f"File '{extra_file}' copied from {base_model_path} to {save_path}.")
else:
print(f"File '{extra_file}' not found in {base_model_path}, skipping copy.")
def save_full_model(
saved_thinker_path: str,
base_model_path: str,
save_path: str = "./merged_model_checkpoint",
extra_file: str = "spk_dict.pt",
):
"""Load the saved thinker module and the original model, replace the thinker in the original model.
Then save the complete model along with its tokenizer and processor configuration.
Args:
saved_thinker_path (str): Path to the saved thinker weights.
base_model_path (str): Directory path of the original model.
save_path (str): Directory where the merged model and configurations will be saved.
extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
"""
# 1. Load the saved thinker module and the original model
thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(
saved_thinker_path, torch_dtype="auto", device_map="cpu"
)
base_model = AutoModel.from_pretrained(base_model_path, torch_dtype="auto", device_map="cpu")
base_model.thinker = thinker
# 2. Save the complete model along with its tokenizer and processor configuration
processor = AutoProcessor.from_pretrained(base_model_path)
base_model.save_pretrained(save_path)
processor.save_pretrained(save_path)
print(f"Merged model and tokenizer saved to {save_path}.")
# 3. Copy the extra file from the base model directory to the save_path
source_file = os.path.join(base_model_path, extra_file)
target_file = os.path.join(save_path, extra_file)
if os.path.exists(source_file):
shutil.copy(source_file, target_file)
print(f"File '{extra_file}' copied from {base_model_path} to {save_path}.")
else:
print(f"File '{extra_file}' not found in {base_model_path}, skipping copy.")
if __name__ == "__main__":
fire.Fire({"save_full": save_full_model, "merge_lora": merge_lora})
|