add custom pipeline

pipleline.py

@@ -0,0 +1,29 @@
+from typing import  Dict, List, Any
+from transformers import AutoTokenizer, TextGenerationPipeline, pipeline
+from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
+
+
+class PreTrainedPipeline():
+	def __init__(self, path=""):
+		tokenizer = AutoTokenizer.from_pretrained(path)
+        model = AutoGPTQForCausalLM.from_quantized(path, device="cuda:0", use_safetensors=True)
+
+
+	def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`list`:. The list contains the embeddings of the inference inputs
+        """
+        inputs = data.get("inputs", data)
+        parameters = data.get("parameters", {})
+
+        # tokenize the input
+        input_ids = self.tokenizer(inputs,return_tensors="pt").input_ids.to(self.model.device)
+        # run the model
+        logits = self.model.generate(input_ids, **parameters)
+        # Perform pooling
+        # postprocess the prediction
+        return {"generated_text": self.tokenizer.decode(logits[0].tolist())}