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Better defaults while loading models

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oobabooga 2023-01-06 02:54:33 -03:00
parent dd70f7edd5
commit ee650343bc
2 changed files with 12 additions and 5 deletions
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15
server.py
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@ -12,8 +12,8 @@ from transformers import GPTJForCausalLM, AutoModelForCausalLM, AutoModelForSeq2
#model_name = 'gpt-j-6B-float16' #model_name = 'gpt-j-6B-float16'
#model_name = "opt-6.7b" #model_name = "opt-6.7b"
#model_name = 'opt-13b' #model_name = 'opt-13b'
#model_name = "gpt4chan_model_float16" model_name = "gpt4chan_model_float16"
model_name = 'galactica-6.7b' #model_name = 'galactica-6.7b'
#model_name = 'gpt-neox-20b' #model_name = 'gpt-neox-20b'
#model_name = 'flan-t5' #model_name = 'flan-t5'
#model_name = 'OPT-13B-Erebus' #model_name = 'OPT-13B-Erebus'
@ -24,17 +24,24 @@ def load_model(model_name):
print(f"Loading {model_name}...") print(f"Loading {model_name}...")
t0 = time.time() t0 = time.time()
# Loading the model
if os.path.exists(f"torch-dumps/{model_name}.pt"): if os.path.exists(f"torch-dumps/{model_name}.pt"):
print("Loading in .pt format...") print("Loading in .pt format...")
model = torch.load(f"torch-dumps/{model_name}.pt").cuda() model = torch.load(f"torch-dumps/{model_name}.pt").cuda()
elif model_name.lower().startswith(('gpt-neo', 'opt-')): elif model_name.lower().startswith(('gpt-neo', 'opt-', 'galactica')):
if any(size in model_name for size in ('13b', '20b', '30b')):
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True) model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", device_map='auto', load_in_8bit=True)
else:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
elif model_name in ['gpt-j-6B']: elif model_name in ['gpt-j-6B']:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda() model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
elif model_name in ['flan-t5', 't5-large']: elif model_name in ['flan-t5', 't5-large']:
model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda() model = T5ForConditionalGeneration.from_pretrained(f"models/{model_name}").cuda()
else:
model = AutoModelForCausalLM.from_pretrained(f"models/{model_name}", low_cpu_mem_usage=True, torch_dtype=torch.float16).cuda()
if model_name in ['gpt4chan_model_float16']: # Loading the tokenizer
if model_name.startswith('gpt4chan'):
tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/") tokenizer = AutoTokenizer.from_pretrained("models/gpt-j-6B/")
elif model_name in ['flan-t5']: elif model_name in ['flan-t5']:
tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/") tokenizer = T5Tokenizer.from_pretrained(f"models/{model_name}/")

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torch-dumps/place-your-pt-models-here.txt
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