handler.py

# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages

import torch
#from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import LlamaTokenizer, MixtralForCausalLM
import bitsandbytes, flash_attn

class EndpointHandler:
    def __init__(self, path=""):
        self.tokenizer = LlamaTokenizer.from_pretrained(path, trust_remote_code=True)
        self.model = MixtralForCausalLM.from_pretrained(
            path,
            torch_dtype=torch.float16,
            device_map="auto",
            load_in_8bit=False,
            load_in_4bit=True,
            use_flash_attention_2=True
            )
    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        sys_prompt=data["prompt"]
        list=data["inputs"]
        prompt=f"<|im_start|>system\n{sys_prompt}.<|im_end|>\n"
        for item in list:
            if item["role"]=="assistant":
                content=item["content"]
                prompt+=f"<|im_start|>assistant\n{content}<|im_end|>\n"
            else:
                content=item["content"]
                prompt+=f"<|im_start|>user\n{content}<|im_end|>\n"
        prompt+="<|im_start|>assistant\n"

        #for chat in prompts:
            #print(chat)
        input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
        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)
        response = self.tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
        return (f"Response: {response}")

        """
        encodeds = self.tokenizer.encode(prompt, return_tensors="pt")
        model_inputs = encodeds.to(device)
        self.model.to(device)
        generated_ids = self.model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
        decoded = self.tokenizer.decode(generated_ids[0])
        return decoded
        """