Create handler.py

handler.py

@@ -0,0 +1,83 @@
+from typing import Dict, List, Any
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+import torch
+import time
+
+class EndpointHandler:
+    def __init__(self, path="luxmorocco/qiyas-falcon-7b"):
+        # load the model
+        config = PeftConfig.from_pretrained(path)
+
+        bnb_config = BitsAndBytesConfig(
+            load_in_4bit=True,
+            bnb_4bit_use_double_quant=True,
+            bnb_4bit_quant_type="nf4",
+            bnb_4bit_compute_dtype=torch.float16,
+        )      
+        
+        self.model = AutoModelForCausalLM.from_pretrained(
+            config.base_model_name_or_path,
+            return_dict=True,
+            load_in_4bit=True,
+            device_map={"":0},
+            trust_remote_code=True,
+            quantization_config=bnb_config,
+        )
+        
+        self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+        
+        self.model = PeftModel.from_pretrained(self.model, path)
+
+    def __call__(self, data: Any) -> Dict[str, Any]:
+        """
+        Args:
+            inputs :obj:`list`:. The object should be like {"context": "some word", "question": "some word"} containing:
+                - "context": 
+                - "question": 
+        Return:
+            A :obj:`list`:. The object returned should be like {"answer": "some word", time: "..."} containing:
+                - "answer": answer the question based on the context
+                - "time": the time run predict
+        """
+        inputs = data.pop("inputs", data)
+        context = inputs.pop("context", inputs)
+        question = inputs.pop("question", inputs)
+
+        prompt = f"""Answer the question based on the context below. If the question cannot be answered using the information provided answer with 'No answer'. Stop response if end.
+>>TITLE<<: Flawless answer.
+>>CONTEXT<<: {context}
+>>QUESTION<<: {question}
+>>ANSWER<<:
+""".strip()
+
+        batch = self.tokenizer(
+            prompt,
+            padding=True,
+            truncation=True,
+            return_tensors='pt'
+        )
+        batch = batch.to('cuda:0')
+        
+        generation_config = self.model.generation_config
+        generation_config.top_p = 0.7
+        generation_config.temperature = 0.7
+        generation_config.max_new_tokens = 256
+        generation_config.num_return_sequences = 1
+        generation_config.pad_token_id = self.tokenizer.eos_token_id
+        generation_config.eos_token_id = self.tokenizer.eos_token_id
+
+        start = time.time()
+        with torch.cuda.amp.autocast():
+            output_tokens = self.model.generate(
+                input_ids = batch.input_ids,
+                generation_config=generation_config,
+            )
+        end = time.time()
+
+        generated_text = self.tokenizer.decode(output_tokens[0])
+
+        prediction = {'answer': generated_text.split('>>END<<')[0].split('>>ANSWER<<:')[1].strip(), 'time': f"{(end-start):.2f} s"} 
+
+        return prediction