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[data] fix qwen2.5 omni plugin (#7573)

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* align key with qwen2vl

* nit && change scripts
This commit is contained in:
Kingsley
2025-04-02 21:28:52 +08:00
committed by GitHub
parent 7b9deb9410
commit d32c6c014d
4 changed files with 47 additions and 6 deletions
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48
scripts/lora_part_merge.py → scripts/qwen_omni_merge.py
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@@ -19,7 +19,7 @@ import shutil
import fire
from peft import PeftModel
from transformers import AutoModel, AutoProcessor, AutoTokenizer
from transformers import AutoModel, AutoProcessor, AutoTokenizer, Qwen2_5OmniThinkerForConditionalGeneration
def merge_lora(
@@ -31,7 +31,7 @@ def merge_lora(
):
"""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.
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.
@@ -86,5 +86,47 @@ def merge_lora(
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,
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 final complete model will be saved.
extra_file (str): Name of the extra file to be copied (default: "spk_dict.pt").
"""
# Load the thinker module
thinker = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained(saved_thinker_path, device_map="cpu")
# Load the original model
base_model = AutoModel.from_pretrained(base_model_path, device_map="cpu")
# Replace the thinker module in the original model
base_model.thinker = thinker
# Load the processor and tokenizer
processor = AutoProcessor.from_pretrained(base_model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
# Save the complete model along with its configurations
base_model.save_pretrained(save_path)
tokenizer.save_pretrained(save_path)
processor.save_pretrained(save_path)
print(f"Complete model, tokenizer, and processor configuration have been 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.")
if __name__ == "__main__":
fire.Fire(merge_lora)
fire.Fire({"save_full": save_full_model, "merge_lora": merge_lora})