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49 lines
1.6 KiB
Python
49 lines
1.6 KiB
Python
from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModelForCausalLM
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from read import read_config
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from argparse import ArgumentParser
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import torch
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import time
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def generate(tokenizer, prompt, model, config):
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
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outputs = model.generate(input_ids=input_ids, max_new_tokens=config["max_new_tokens"], temperature=config["temperature"])
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decoded = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
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return decoded[len(prompt):]
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def setup_model(config):
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model = AutoModelForCausalLM.from_pretrained(config["model_name"], device_map="auto", torch_dtype=torch.float16)
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tokenizer = AutoTokenizer.from_pretrained(config["tokenizer_name"])
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if config["lora"]:
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model = PeftModelForCausalLM.from_pretrained(model, config["lora_path"], device_map="auto", torch_dtype=torch.float16)
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model.to(dtype=torch.float16)
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print(f"Mem needed: {model.get_memory_footprint() / 1024 / 1024 / 1024:.2f} GB")
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return model, tokenizer
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--config", type=str, required=True)
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parser.add_argument("--prompt", type=str, required=True)
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args = parser.parse_args()
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config = read_config(args.config)
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print("setting up model")
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model, tokenizer = setup_model(config)
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print("generating")
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start = time.time()
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generation = generate(tokenizer, args.prompt, model, config)
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print(f"done in {time.time() - start:.2f}s")
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print(generation) |