初始化项目，由ModelHub XC社区提供模型

Model: mariadelcarmenramirez/Llama-3.1-8B-ArtTherapy
Source: Original Platform

handler.py

@@ -0,0 +1,51 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+class EndpointHandler:
+    def __init__(self, path="/repository"):
+        self.tokenizer = AutoTokenizer.from_pretrained(path)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+        )
+        self.model.eval()
+
+    def __call__(self, data):
+        inputs = data.get("inputs", data)
+        parameters = data.get("parameters", {})
+
+        max_new_tokens = parameters.get("max_new_tokens", 256)
+        temperature = parameters.get("temperature", 0.7)
+
+        if isinstance(inputs, dict) and "messages" in inputs:
+            messages = inputs["messages"]
+            # Step 1: apply template to get a STRING (tokenize=False)
+            prompt = self.tokenizer.apply_chat_template(
+                messages,
+                tokenize=False,
+                add_generation_prompt=True,
+            )
+            # Step 2: tokenize the string
+            input_ids = self.tokenizer.encode(
+                prompt, return_tensors="pt"
+            ).to(self.model.device)
+        else:
+            text = inputs if isinstance(inputs, str) else str(inputs)
+            input_ids = self.tokenizer.encode(
+                text, return_tensors="pt"
+            ).to(self.model.device)
+
+        with torch.no_grad():
+            output = self.model.generate(
+                input_ids,
+                max_new_tokens=max_new_tokens,
+                temperature=temperature,
+                do_sample=True,
+            )
+
+        result = self.tokenizer.decode(
+            output[0][input_ids.shape[-1]:], skip_special_tokens=True
+        )
+        return [{"generated_text": result}]