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63 lines
1.6 KiB
63 lines
1.6 KiB
"""Zephyr by HF"""
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import torch
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from transformers import pipeline
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class Zephyr:
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"""
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Zehpyr model from HF
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Args:
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max_new_tokens(int) = Number of max new tokens
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temperature(float) = temperature of the LLM
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top_k(float) = top k of the model set to 50
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top_p(float) = top_p of the model set to 0.95
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Usage:
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>>> model = Zephyr()
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>>> output = model("Generate hello world in python")
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"""
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def __init__(
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self,
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max_new_tokens: int = 300,
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temperature: float = 0.5,
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top_k: float = 50,
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top_p: float = 0.95,
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):
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super().__init__()
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self.max_new_tokens = max_new_tokens
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self.temperature = temperature
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self.top_k = top_k
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self.top_p = top_p
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self.pipe = pipeline(
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"text-generation",
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model="HuggingFaceH4/zephyr-7b-alpha",
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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self.messages = [
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{
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"role": "system",
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"content": "You are a friendly chatbot who always responds in the style of a pirate",
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},
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{
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"role": "user",
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"content": "How many helicopters can a human eat in one sitting?",
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},
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]
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def __call__(self, text: str):
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"""Call the model"""
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prompt = self.pipe.tokenizer.apply_chat_template(
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self.messages, tokenize=False, add_generation_prompt=True
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)
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outputs = self.pipe(prompt, max_new_token=self.max_new_tokens)
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print(outputs[0])["generated_text"]
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