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handler.py

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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",  # Auto selects device to put model on.
+        )
+        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 = "Below is a conversation between a user and you."
+        END = "Instruction: Write a response appropriate to the conversation."
+        prompt = "<user>:"
+
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        prompt = prompt+inputs
+        # preprocess 
+
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        inputs = self.tokenizer(INTRO+'\n '+prompt+'\n '+END +'\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=100, do_sample=True, temperature=0.1, top_p=0.9, repetition_penalty=1.5)
+        reply = self.tokenizer.batch_decode(output.detach().cpu().numpy(), skip_special_tokens=True)
+        
+        return [{"generated_reply": reply}]

requirements.txt

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+bitsandbytes
+peft
+transformers