90 lines
2.7 KiB
Python
Executable File
90 lines
2.7 KiB
Python
Executable File
#!/usr/bin/env python3
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import argparse
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import logging
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import sys
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import torch
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import transformers
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logger = logging.getLogger(__name__)
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logging.basicConfig()
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def main() -> None:
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#
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# CLI argument handling.
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#
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parser = argparse.ArgumentParser()
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parser.add_argument("-m",
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"--model-name",
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default="facebook/opt-350m",
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help="HuggingFace Transformers model name.")
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parser.add_argument(
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"-c",
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"--checkpoint",
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help="Fine-tune checkpoint to load into the base model.")
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args = parser.parse_args()
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#
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# Model setup.
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#
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logger.info(f"Loading tokenizer for {args.model_name}")
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tokenizer = transformers.AutoTokenizer.from_pretrained(args.model_name)
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state_dict = None
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if args.checkpoint is not None:
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logger.info(f"Loading checkpoint from {args.checkpoint}")
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state_dict = torch.load(args.checkpoint,
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map_location="cuda").pop("model")
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logger.info(f"Loading the {args.model_name} model")
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bad_words_ids = [
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tokenizer(bad_word, add_prefix_space=True,
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add_special_tokens=False).input_ids
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for bad_word in ["_", "__", "___", "____", "_____", "Description:"]
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]
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model = transformers.AutoModelForCausalLM.from_pretrained(
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args.model_name, state_dict=state_dict, bad_words_ids=bad_words_ids)
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model.eval().half().to("cuda")
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#
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# We're good to go! Main loop.
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#
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try:
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while True:
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prompt = input("Prompt: ").replace("<EOL>", "\n")
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# First, sampling-based generation.
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input_ids = tokenizer(prompt,
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return_tensors='pt').input_ids.to("cuda")
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logits = model.generate(
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input_ids,
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do_sample=True,
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max_new_tokens=8,
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top_k=50,
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top_p=0.90,
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)
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output = tokenizer.decode(logits[0], skip_special_tokens=True)
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# Then, contrastive search.
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input_ids = tokenizer(output,
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return_tensors="pt").input_ids.to("cuda")
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logits = model.generate(input_ids,
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max_new_tokens=128,
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penalty_alpha=0.6,
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top_k=6)
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print(f"Output:\n{64 * '-'}")
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output = tokenizer.decode(logits[0], skip_special_tokens=True)
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cleaned_output = output.replace(prompt, "")
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print(cleaned_output) # print(output)
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print(64 * "-")
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except EOFError:
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sys.exit()
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if __name__ == "__main__":
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main()
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