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75 lines
2.0 KiB
Python
75 lines
2.0 KiB
Python
2 years ago
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import textwrap
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import colorama
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from transformers import LlamaForCausalLM, LlamaTokenizer
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from transformers import logging as t_logging
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from transformers import pipeline
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# Configure logging level for transformers library
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t_logging.logging.set_verbosity_info()
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# Utility Functions
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def get_prompt(human_prompt):
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prompt_template = f"### Human: {human_prompt} \n### Assistant:"
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return prompt_template
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def remove_human_text(text):
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return text.split("### Human:", 1)[0]
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def parse_text(data):
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for item in data:
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text = item["generated_text"]
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assistant_text_index = text.find("### Assistant:")
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if assistant_text_index != -1:
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assistant_text = text[
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assistant_text_index + len("### Assistant:") :
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].strip()
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assistant_text = remove_human_text(assistant_text)
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wrapped_text = textwrap.fill(assistant_text, width=100)
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print(wrapped_text)
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# Reasoning question
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EXAMPLE_REASONING = "Answer the following question by reasoning step by step. \
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The cafeteria had 22 apples. If they used 20 for lunch, and bought 6 more, \
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how many apple do they have?"
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# User interface
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def main(model_dir):
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# Model loading for inference
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tokenizer = LlamaTokenizer.from_pretrained(model_dir)
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base_model = LlamaForCausalLM.from_pretrained(
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model_dir,
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load_in_8bit=True,
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device_map="auto",
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)
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pipe = pipeline(
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"text-generation",
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model=base_model,
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tokenizer=tokenizer,
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max_length=512,
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temperature=0.7,
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top_p=0.95,
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repetition_penalty=1.15,
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)
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print("Reading for inference!")
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while True:
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input_prompt = ""
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input_prompt = input("USER:")
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print(colorama.Style.DIM + f"You are submitting: {input_prompt}")
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print(colorama.Style.RESET_ALL)
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raw_output = pipe(get_prompt(input_prompt))
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parse_text(raw_output)
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if __name__ == "__main__":
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print("Warming up the engines...")
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main()
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