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from typing import Dict, List, Any
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch
from peft import PeftModel
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
base_model_name = "snorkelai/Snorkel-Mistral-PairRM-DPO"
lora_adaptor = "mogaio/Snorkel-Mistral-PairRM-DPO-Freakonomics_MTD-TCD-Lora"
self.tokenizer = AutoTokenizer.from_pretrained(base_model_name)
self.tokenizer.pad_token = self.tokenizer.eos_token
self.bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
)
self.model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=self.bnb_config,
device_map="auto",
)
self.model.config.use_cache = False
self.inference_model = PeftModel.from_pretrained(self.model, lora_adaptor, from_transformers=True)
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
INTRO = "A chat between a curious user and a human like artificial intelligence assistant. The assistant gives helpful, intelligent, detailed, and polite answers to the user's questions."
prompt = ""
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
chat_history = ' \n '.join(str(x) for x in inputs)
prompt = INTRO+'\n ' + chat_history
# preprocess
device = "cuda" if torch.cuda.is_available() else "cpu"
inputs = self.tokenizer(prompt+' \n >> <assistant>:', return_tensors="pt").to(device)
inputs = {k: v.to('cuda') for k, v in inputs.items()}
output = self.inference_model.generate(input_ids=inputs["input_ids"],pad_token_id=self.tokenizer.pad_token_id, max_new_tokens=64, do_sample=True, temperature=0.9, top_p=0.9, repetition_penalty=1.5, early_stopping=True, length_penalty = -0.3, num_beams=5, num_return_sequences=1)
response_raw = self.tokenizer.batch_decode(output.detach().cpu().numpy(), skip_special_tokens=True)
response_ls = response_raw[0].split('>>')
response_ = response_ls[1].split('<assistant>:')[1]
response_ = response_.split('<user>')[0]
response_ = response_.split('Instruction:')[0]
response_ = response_.replace('\n','')
response = '<assistant>:' + response_.strip()
return [{"generated_reply": response}] |