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105 lines
3.1 KiB
105 lines
3.1 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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model_name: str = "HuggingFaceH4/zephyr-7b-alpha",
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tokenize: bool = False,
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add_generation_prompt: bool = True,
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system_prompt: str = "You are a friendly chatbot who always responds in the style of a pirate",
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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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do_sample: bool = True,
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*args,
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**kwargs,
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):
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super().__init__()
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self.model_name = model_name
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self.tokenize = tokenize
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self.add_generation_prompt = add_generation_prompt
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self.system_prompt = system_prompt
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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.do_sample = do_sample
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self.pipe = pipeline(
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"text-generation",
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model=self.model_name,
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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": f"{self.system_prompt}\n\nUser:",
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},
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]
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def __call__(self, task: 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,
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tokenize=self.tokenize,
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add_generation_prompt=self.add_generation_prompt,
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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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def chat(self, message: str):
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"""
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Adds a user message to the conversation and generates a chatbot response.
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"""
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# Add the user message to the conversation
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self.messages.append({"role": "user", "content": message})
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# Apply the chat template to format the messages
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prompt = self.pipe.tokenizer.apply_chat_template(
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self.messages,
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tokenize=self.tokenize,
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add_generation_prompt=self.add_generation_prompt,
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)
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# Generate a response
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outputs = self.pipe(
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prompt,
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max_new_tokens=self.max_new_tokens,
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do_sample=self.do_sample,
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temperature=self.temperature,
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top_k=self.top_k,
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top_p=self.top_p,
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)
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# Extract the generated text
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generated_text = outputs[0]["generated_text"]
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# Optionally, you could also add the chatbot's response to the messages list
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# However, the below line should be adjusted to extract the chatbot's response only
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# self.messages.append({"role": "bot", "content": generated_text})
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return generated_text
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