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transformers with glm4v lora adapter

This commit is contained in:
sixsixcoder 2024-09-11 10:34:24 +00:00
parent 4995f03934
commit 188c7956a1
1 changed files with 33 additions and 8 deletions
basic_demo
glm4v_server.py

41
basic_demo/glm4v_server.py
View File

@ -17,8 +17,10 @@ from transformers import (
AutoModel, AutoModel,
TextIteratorStreamer TextIteratorStreamer
) )
from peft import PeftModelForCausalLM
from PIL import Image from PIL import Image
from io import BytesIO from io import BytesIO
from pathlib import Path
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu' DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16 TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16
@ -365,16 +367,39 @@ torch.cuda.empty_cache()
if __name__ == "__main__": if __name__ == "__main__":
MODEL_PATH = sys.argv[1] MODEL_PATH = sys.argv[1]
tokenizer = AutoTokenizer.from_pretrained( model_dir = Path(MODEL_PATH).expanduser().resolve()
if (model_dir / 'adapter_config.json').exists():
import json
with open(model_dir / 'adapter_config.json', 'r', encoding='utf-8') as file:
config = json.load(file)
model = AutoModel.from_pretrained(
config.get('base_model_name_or_path'),
trust_remote_code=True,
device_map='auto',
torch_dtype=TORCH_TYPE
)
model = PeftModelForCausalLM.from_pretrained(
model=model,
model_id=model_dir,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
config.get('base_model_name_or_path'),
trust_remote_code=True,
encode_special_tokens=True
)
model.eval().to(DEVICE)
else:
tokenizer = AutoTokenizer.from_pretrained(
MODEL_PATH, MODEL_PATH,
trust_remote_code=True, trust_remote_code=True,
encode_special_tokens=True encode_special_tokens=True
) )
model = AutoModel.from_pretrained( model = AutoModel.from_pretrained(
MODEL_PATH, MODEL_PATH,
torch_dtype=TORCH_TYPE, torch_dtype=TORCH_TYPE,
trust_remote_code=True, trust_remote_code=True,
device_map="auto", device_map="auto",
).eval().to(DEVICE) ).eval().to(DEVICE)
uvicorn.run(app, host='0.0.0.0', port=8000, workers=1) uvicorn.run(app, host='0.0.0.0', port=8000, workers=1)