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from typing import Dict, List, Any |
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig |
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import torch |
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from peft import PeftModel |
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class EndpointHandler: |
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def __init__(self, path=""): |
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base_model_name = "snorkelai/Snorkel-Mistral-PairRM-DPO" |
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lora_adaptor = "mogaio/Snorkel-Mistral-PairRM-DPO-Freakonomics_MTD-TCD-Lora" |
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self.tokenizer = AutoTokenizer.from_pretrained(base_model_name) |
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self.tokenizer.pad_token = self.tokenizer.eos_token |
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self.bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_use_double_quant=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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) |
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self.model = AutoModelForCausalLM.from_pretrained( |
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base_model_name, |
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quantization_config=self.bnb_config, |
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device_map="auto", |
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) |
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self.model.config.use_cache = False |
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self.inference_model = PeftModel.from_pretrained(self.model, lora_adaptor, from_transformers=True) |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: |
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output = self.inference_model.generate(input_ids=inputs["input_ids"],pad_token_id=self.tokenizer.pad_token_id, max_new_tokens=256, 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) |
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response_raw = self.tokenizer.batch_decode(output.detach().cpu().numpy(), skip_special_tokens=True) |
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response_ls = response_raw[0].split('>>') |
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response_ = response_ls[1].split('<assistant>:')[1] |
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response_ = response_.split('<user>')[0] |
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response_ = response_.split('Instruction:')[0] |
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response_ = response_.replace('\n','') |
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response = '<assistant>:' + response_.strip() |
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return [{"generated_reply": response}] |
