mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-22 16:17:57 +01:00
Remove T5 support (it sucks)
This commit is contained in:
parent
89fd0180b7
commit
b2a2ddcb15
13
server.py
13
server.py
@ -8,8 +8,7 @@ from pathlib import Path
|
||||
import gradio as gr
|
||||
import transformers
|
||||
from html_generator import *
|
||||
from transformers import AutoTokenizer, T5Tokenizer
|
||||
from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
|
||||
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
@ -37,8 +36,6 @@ def load_model(model_name):
|
||||
model = torch.load(Path(f"torch-dumps/{model_name}.pt"))
|
||||
elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')) and any(size in model_name.lower() for size in ('13b', '20b', '30b')):
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), device_map='auto', load_in_8bit=True)
|
||||
elif model_name in ['flan-t5', 't5-large']:
|
||||
model = T5ForConditionalGeneration.from_pretrained(Path(f"models/{model_name}")).cuda()
|
||||
else:
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
|
||||
|
||||
@ -46,11 +43,7 @@ def load_model(model_name):
|
||||
else:
|
||||
settings = ["low_cpu_mem_usage=True"]
|
||||
cuda = ""
|
||||
|
||||
if model_name in ['flan-t5', 't5-large']:
|
||||
command = f"T5ForConditionalGeneration.from_pretrained"
|
||||
else:
|
||||
command = "AutoModelForCausalLM.from_pretrained"
|
||||
command = "AutoModelForCausalLM.from_pretrained"
|
||||
|
||||
if args.cpu:
|
||||
settings.append("torch_dtype=torch.float32")
|
||||
@ -72,8 +65,6 @@ def load_model(model_name):
|
||||
# Loading the tokenizer
|
||||
if model_name.lower().startswith('gpt4chan') and Path(f"models/gpt-j-6B/").exists():
|
||||
tokenizer = AutoTokenizer.from_pretrained(Path("models/gpt-j-6B/"))
|
||||
elif model_name in ['flan-t5', 't5-large']:
|
||||
tokenizer = T5Tokenizer.from_pretrained(Path(f"models/{model_name}/"))
|
||||
else:
|
||||
tokenizer = AutoTokenizer.from_pretrained(Path(f"models/{model_name}/"))
|
||||
|
||||
|
Loading…
Reference in New Issue
Block a user