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import torch |
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from transformers import LlamaTokenizer, MixtralForCausalLM |
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import bitsandbytes, flash_attn |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.tokenizer = LlamaTokenizer.from_pretrained(path, trust_remote_code=True) |
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self.model = MixtralForCausalLM.from_pretrained( |
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path, |
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torch_dtype=torch.float16, |
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device_map="auto", |
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load_in_8bit=False, |
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load_in_4bit=True, |
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use_flash_attention_2=True |
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) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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sys_prompt=data["prompt"] |
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list=data["inputs"] |
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prompt=f"<|im_start|>system\n{sys_prompt}.<|im_end|>\n" |
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for item in list: |
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if item["role"]=="assistant": |
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content=item["content"] |
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prompt+=f"<|im_start|>assistant\n{content}<|im_end|>\n" |
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else: |
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content=item["content"] |
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prompt+=f"<|im_start|>user\n{content}<|im_end|>\n" |
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prompt+="<|im_start|>assistant\n" |
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input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to("cuda") |
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generated_ids = self.model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=self.tokenizer.eos_token_id) |
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response = self.tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True) |
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return (f"Response: {response}") |
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""" |
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encodeds = self.tokenizer.encode(prompt, return_tensors="pt") |
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model_inputs = encodeds.to(device) |
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self.model.to(device) |
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generated_ids = self.model.generate(model_inputs, max_new_tokens=1000, do_sample=True) |
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decoded = self.tokenizer.decode(generated_ids[0]) |
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return decoded |
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""" |
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