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

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+
+- **Developed by:** [More Information Needed]
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+## How to Get Started with the Model
+
+Use the code below to get started with the model.
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+## Training Details
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+### Training Data
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+#### Training Hyperparameters
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+## Environmental Impact
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+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
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config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "float16",
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_parameters": {
+    "rope_theta": 500000.0,
+    "rope_type": "default"
+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "vocab_size": 139336
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "5.0.0"
+}

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()}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6e6668b966d67118e811bc5c4ce7986bff9247c48d420c91f16737bdc3c04b74
+size 16242091056

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:549f579ab85fa6f63fe7d9a4d9e62918011c35bebf45c6bb8000a6ae89a9a3d7
+size 19338079

tokenizer_config.json

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+{
+  "backend": "tokenizers",
+  "bos_token": "<|begin_of_text|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|end_of_text|>",
+  "is_local": false,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "TokenizersBackend"
+}