Initial GPTQ model commit

handler.py

@@ -0,0 +1,49 @@
+from typing import Any, Dict
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from peft import PeftConfig, PeftModel
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # load model and processor from path
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        try:
+            config = PeftConfig.from_pretrained(path)
+            model = AutoModelForCausalLM.from_pretrained(
+                config.base_model_name_or_path,
+                return_dict=True,
+                load_in_8bit=True,
+                device_map="auto",
+                torch_dtype=torch.float16,
+                trust_remote_code=True,
+            )
+            model.resize_token_embeddings(len(self.tokenizer))
+            model = PeftModel.from_pretrained(model, path)
+        except Exception:
+            model = AutoModelForCausalLM.from_pretrained(
+                path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True
+            )
+        self.model = model
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
+        # process input
+        inputs = data.pop("inputs", data)
+        parameters = data.pop("parameters", None)
+
+        # preprocess
+        inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device)
+
+        # pass inputs with all kwargs in data
+        if parameters is not None:
+            outputs = self.model.generate(**inputs, **parameters)
+        else:
+            outputs = self.model.generate(**inputs)
+
+        # postprocess the prediction
+        prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+        return [{"generated_text": prediction}]