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Model: itsjorigo/sinllama-mcq-merged-2.0
Source: Original Platform

handler.py

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+"""
+HuggingFace Inference Endpoint custom handler for sinllama-mcq-merged-2.0.
+
+The deployed model (itsjorigo/sinllama-mcq-merged) is a fully merged
+AutoModelForCausalLM — NOT a PEFT adapter stack. Load it directly.
+
+The tokenizer must come from polyglots/SinLlama_v01 (trust_remote_code=True)
+because that repo defines the custom TokenizersBackend class.
+
+Request:  {"inputs": {"passage": "<Sinhala text>", "entity_block": "<optional>"}}
+Response: {"mcq": "ප්‍රශ්නය: ..."}
+"""
+
+import re
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+SINLLAMA_ID = "polyglots/SinLlama_v01"
+
+PROMPT_TEMPLATE = (
+    "පහත ඉතිහාස ඡේදය කියවා, ඒ ගැන බහු-විකල්ප ප්‍රශ්නයක් සාදන්න.\n\n"
+    "ඡේදය: {passage}\n\n"
+    "{entity_block}"
+    "MCQ:"
+)
+
+
+class EndpointHandler:
+    def __init__(self, path=""):
+        # Tokenizer from SinLlama — defines the TokenizersBackend class
+        print("Loading tokenizer from SinLlama repo...", flush=True)
+        self.tokenizer = AutoTokenizer.from_pretrained(SINLLAMA_ID, trust_remote_code=True)
+
+        # Model loaded directly — itsjorigo/sinllama-mcq-merged is already fully merged
+        print(f"Loading merged model from {path!r}...", flush=True)
+        self.model = AutoModelForCausalLM.from_pretrained(
+            path,
+            torch_dtype=torch.float16,
+            device_map="auto",
+            low_cpu_mem_usage=True,
+            attn_implementation="sdpa",
+        )
+        self.model.eval()
+        print("EndpointHandler ready.", flush=True)
+
+    def __call__(self, data: dict) -> dict:
+        inputs       = data.get("inputs", {})
+        passage      = inputs.get("passage", "")
+        entity_block = inputs.get("entity_block", "")
+
+        if not passage:
+            return {"error": 'No passage provided. Send {"inputs": {"passage": "..."}}'}
+
+        prompt = PROMPT_TEMPLATE.format(
+            passage=passage.strip(),
+            entity_block=entity_block,
+        )
+
+        enc = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
+
+        with torch.no_grad():
+            out = self.model.generate(
+                **enc,
+                max_new_tokens=280,
+                temperature=0.7,
+                do_sample=True,
+                repetition_penalty=1.1,
+                eos_token_id=self.tokenizer.eos_token_id,
+                pad_token_id=self.tokenizer.pad_token_id,
+            )
+
+        new_ids = out[0][enc.input_ids.shape[1]:]
+        text    = self.tokenizer.decode(new_ids, skip_special_tokens=True).strip()
+
+        # Ensure each option starts on its own line
+        for tag in ["A)", "B)", "C)", "D)", "නිවැරදි පිළිතුර:"]:
+            text = re.sub(rf"(?<!\n)({re.escape(tag)})", r"\n\1", text)
+
+        return {"mcq": text.strip()}