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