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Model: serving-d-cause/writing-roleplay-20k-context-nemo-12b-v1.0
Source: Original Platform

handler.py

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+import os
+from typing import Any, Dict, List
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextGenerationPipeline
+
+# Ensure your template includes escaped newlines correctly
+CHAT_TEMPLATE = (
+    "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}"
+    "{% for message in messages %}"
+    "{{'<|im_start|>' + message['role'] + '\\n' + message['content'] + '<|im_end|>' + '\\n'}}"
+    "{% endfor %}"
+    "{% if add_generation_prompt %}{{ '<|im_start|>assistant\\n' }}{% endif %}"
+)
+
+class EndpointHandler:
+    def __init__(self, model_dir: str, **kwargs: Any):
+        # Load tokenizer and model from provided model_dir
+        self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
+        # Assign the chat_template under 'default'
+        self.tokenizer.chat_template = {"default": CHAT_TEMPLATE}
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_dir, trust_remote_code=True, device_map="auto"
+        )
+        self.pipeline = TextGenerationPipeline(
+            model=self.model, tokenizer=self.tokenizer, return_full_text=False
+        )
+
+    def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        # Validate input structure
+        messages: List[Dict[str, str]] = data.get("messages")
+        if messages is None:
+            raise ValueError("Request body must include 'messages' array.")
+
+        # Format prompt with proper controlling flag
+        inputs = self.tokenizer.apply_chat_template(
+            messages=messages, add_generation_prompt=True, return_tensors=None
+        )
+        # Generate text
+        gen = self.pipeline(inputs, max_new_tokens=data.get("parameters", {}).get("max_new_tokens", 128))
+        content = gen[0]["generated_text"]
+
+        return {"choices": [{"message": {"role": "assistant", "content": content}}]}
+