diff --git a/.env.example b/.env.example
index 4f67c57..ca42a28 100644
--- a/.env.example
+++ b/.env.example
@@ -13,6 +13,14 @@ LOG_LEVEL=INFO
 # Device to run inference on: cpu, cuda, mps
 DEVICE=cpu
 
+# Backend Configuration (onnx or triton)
+INFERENCE_BACKEND=onnx
+
+# Triton Configuration
+TRITON_URL=localhost:8000
+
+TRITON_MODEL_NAME=layoutlmv3-lora-invoice-number
+
 # Model path (relative to project root)
 MODEL_PATH=models/layoutlmv3-lora-invoice-number
 
diff --git a/README.md b/README.md
index 6ea6c08..3fde1f6 100644
--- a/README.md
+++ b/README.md
@@ -204,11 +204,41 @@ The easiest way to configure the application:
    DEVICE=mps
    ```
 
-3. Start the application (automatically loads `.env`):
    ```bash
    docker-compose up -d
    ```
 
+### Inference Backend Configuration
+
+The application supports both local ONNX Runtime (default) and remote Triton Inference Server.
+
+**1. Local ONNX (Default)**
+No extra configuration needed.
+
+**2. Triton Inference Server**
+
+First, create the model repository structure:
+```bash
+python scripts/setup_triton_repo.py --model_path models/layoutlmv3-lora-invoice-number
+```
+
+Then start the server:
+```bash
+docker run --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 \
+  -v $(pwd)/triton_model_repo:/models \
+  nvcr.io/nvidia/tritonserver:23.10-py3 \
+  tritonserver --model-repository=/models
+```
+
+Configure `.env` and run `python app.py` to use the API:
+```bash
+INFERENCE_BACKEND=triton
+TRITON_URL=localhost:8000
+TRITON_MODEL_NAME=layoutlmv3-lora-invoice-number
+```
+
+
+
 ### Available Environment Variables
 
 Key variables (see `.env.example` for all options):
diff --git a/app.py b/app.py
index ef77251..c67f9d5 100644
--- a/app.py
+++ b/app.py
@@ -9,8 +9,6 @@
 import logging
 from dotenv import load_dotenv
 import gradio as gr
-
-# Import from src modules
 from src import app, create_gradio_interface, load_model, DEVICE, MODEL_PATH
 
 load_dotenv()
diff --git a/models/artifacts/layoutlmv3_invoice_ner.onnx.dvc b/models/artifacts/layoutlmv3_invoice_ner.onnx.dvc
index 4c3021e..cce5726 100644
--- a/models/artifacts/layoutlmv3_invoice_ner.onnx.dvc
+++ b/models/artifacts/layoutlmv3_invoice_ner.onnx.dvc
@@ -1,5 +1,5 @@
 outs:
-- md5: 27488294cecffb7e82bb4dbdba745406
-  size: 501667170
+- md5: 1bad589d51dab7bd2efd42126881583d
+  size: 501607527
   hash: md5
   path: layoutlmv3_invoice_ner.onnx
diff --git a/models/artifacts/layoutlmv3_invoice_ner_optimized.onnx.dvc b/models/artifacts/layoutlmv3_invoice_ner_optimized.onnx.dvc
index e66e584..1437d8e 100644
--- a/models/artifacts/layoutlmv3_invoice_ner_optimized.onnx.dvc
+++ b/models/artifacts/layoutlmv3_invoice_ner_optimized.onnx.dvc
@@ -1,5 +1,5 @@
 outs:
-- md5: 38c9fa3d001a55a43280b1f9ce69f697
-  size: 501491951
+- md5: 709e8042dd19087194a7c76a1b4a3f2b
+  size: 501492871
   hash: md5
   path: layoutlmv3_invoice_ner_optimized.onnx
diff --git a/models/artifacts/model_metadata.json b/models/artifacts/model_metadata.json
index 24cb900..4b7c073 100644
--- a/models/artifacts/model_metadata.json
+++ b/models/artifacts/model_metadata.json
@@ -3,7 +3,7 @@
   "base_model": "microsoft/layoutlmv3-base",
   "num_labels": 3,
   "max_length": 512,
-  "onnx_opset_version": 14,
+  "onnx_opset_version": 17,
   "input_names": [
     "pixel_values",
     "input_ids",
diff --git a/pyproject.toml b/pyproject.toml
index ea0f113..17c8986 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "invoice"
-version = "0.2.3"
+version = "0.3.0"
 description = "Finetuned LayoutLMv3 model for invoice number extraction"
 authors = [{ name = "Ryan Z. Nie", email = "ryanznie@gatech.edu" }]
 readme = "README.md"
@@ -35,6 +35,7 @@ dependencies = [
     "onnxruntime>=1.16.0",
     "onnxruntime-tools>=1.7.0",
     "onnxconverter-common>=1.14.0",
+    "tritonclient[http]>=2.41.0",
     # Testing dependencies
     "pytest>=8.0.0",
     "pytest-cov>=4.1.0",
diff --git a/scripts/export_to_onnx.py b/scripts/export_to_onnx.py
index ae55276..8d2f15e 100644
--- a/scripts/export_to_onnx.py
+++ b/scripts/export_to_onnx.py
@@ -81,7 +81,7 @@ def export_to_onnx(
         self,
         model: torch.nn.Module,
         processor: LayoutLMv3Processor,
-        opset_version: int = 14,
+        opset_version: int = 17,
     ) -> Path:
         """Export merged model to ONNX format"""
         print("\n🚀 Exporting to ONNX...")
@@ -149,6 +149,7 @@ def forward(self, pixel_values, input_ids, attention_mask, bbox):
                 output_names=output_names,
                 dynamic_axes=dynamic_axes,
                 opset_version=opset_version,
+                custom_opsets={"ai.onnx.ml": 3},  # CRITICAL for Triton
                 do_constant_folding=True,
                 export_params=True,
                 verbose=False,
@@ -405,7 +406,7 @@ def save_metadata(self, processor: LayoutLMv3Processor, onnx_path: Path) -> None
             "base_model": self.base_model,
             "num_labels": self.num_labels,
             "max_length": 512,
-            "onnx_opset_version": 14,
+            "onnx_opset_version": 17,
             "input_names": ["pixel_values", "input_ids", "attention_mask", "bbox"],
             "output_names": ["logits"],
             "label2id": {"O": 0, "B-INVOICE_ID": 1, "I-INVOICE_ID": 2},
diff --git a/scripts/setup_triton_repo.py b/scripts/setup_triton_repo.py
new file mode 100644
index 0000000..1172060
--- /dev/null
+++ b/scripts/setup_triton_repo.py
@@ -0,0 +1,163 @@
+#!/usr/bin/env python3
+"""
+Setup Triton Inference Server Model Repository for LayoutLMv3
+
+This script creates the necessary directory structure and configuration files
+for serving the LayoutLMv3 ONNX model with Triton Inference Server.
+"""
+
+import argparse
+import shutil
+from pathlib import Path
+import onnx
+import sys
+
+
+def create_model_repo(
+    model_path: str, repo_path: str, model_name: str, config_name: str = "config.pbtxt"
+):
+    """Create Triton model repository structure"""
+    print(f"🔧 Setting up Triton Model Repository for: {model_name}")
+
+    source_path = Path(model_path)
+    if not source_path.exists():
+        print(f"❌ Error: Model file not found at {source_path}")
+        sys.exit(1)
+
+    repo_dir = Path(repo_path)
+    model_dir = repo_dir / model_name
+    version_dir = model_dir / "1"
+
+    # Create directories
+    version_dir.mkdir(parents=True, exist_ok=True)
+    print(f"  ✓ Created directory structure: {version_dir}")
+
+    # Copy model
+    dest_path = version_dir / "model.onnx"
+    shutil.copy2(source_path, dest_path)
+    print(f"  ✓ Copied model to: {dest_path}")
+
+    # Generate config.pbtxt
+    generate_config(source_path, model_dir / config_name, model_name)
+
+
+def generate_config(model_path: Path, config_path: Path, model_name: str):
+    """Generate config.pbtxt based on ONNX model properties"""
+    print("📝 Generating Triton configuration...")
+
+    # Load ONNX model to inspect inputs/outputs
+    model = onnx.load(str(model_path))
+
+    # Basic configuration
+    config_lines = [
+        f'name: "{model_name}"',
+        'platform: "onnxruntime_onnx"',
+        "max_batch_size: 8",  # Adjust as needed
+        "",
+        "dynamic_batching { }",  # Enable dynamic batching
+        "",
+    ]
+
+    # Inputs
+    # Inputs
+    # config_lines.append("input [")  <-- Removed list syntax
+    for input_tensor in model.graph.input:
+        name = input_tensor.name
+        # Skip batch dim for Triton config if max_batch_size > 0
+        dims = [
+            d.dim_value if d.dim_value > 0 else -1
+            for d in input_tensor.type.tensor_type.shape.dim
+        ]
+
+        # Handle dynamic batch dimension (usually the first one)
+        # In Triton with max_batch_size > 0, we exclude the batch dimension from the config shape
+        if len(dims) > 0:
+            dims = dims[1:]
+
+        data_type = mapping_onnx_type_to_triton(input_tensor.type.tensor_type.elem_type)
+
+        config_lines.append("input {")
+        config_lines.append(f'  name: "{name}"')
+        config_lines.append(f"  data_type: {data_type}")
+        config_lines.append(f"  dims: {dims}")
+        config_lines.append("}")
+    # config_lines.append("]") <-- Removed list syntax
+    config_lines.append("")
+
+    # Outputs
+    # Outputs
+    # config_lines.append("output [") <-- Removed list syntax
+    for output_tensor in model.graph.output:
+        name = output_tensor.name
+        dims = [
+            d.dim_value if d.dim_value > 0 else -1
+            for d in output_tensor.type.tensor_type.shape.dim
+        ]
+
+        if len(dims) > 0:
+            dims = dims[1:]
+
+        data_type = mapping_onnx_type_to_triton(
+            output_tensor.type.tensor_type.elem_type
+        )
+
+        config_lines.append("output {")
+        config_lines.append(f'  name: "{name}"')
+        config_lines.append(f"  data_type: {data_type}")
+        config_lines.append(f"  dims: {dims}")
+        config_lines.append("}")
+    # config_lines.append("]") <-- Removed list syntax
+
+    # Write config
+    with open(config_path, "w") as f:
+        f.write("\n".join(config_lines))
+
+    print(f"  ✓ Configuration saved to: {config_path}")
+
+
+def mapping_onnx_type_to_triton(onnx_type):
+    """Map ONNX data types to Triton data types"""
+    # https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#datatypes
+    type_map = {
+        1: "TYPE_FP32",  # FLOAT
+        2: "TYPE_UINT8",  # UINT8
+        3: "TYPE_INT8",  # INT8
+        4: "TYPE_UINT16",  # UINT16
+        5: "TYPE_INT16",  # INT16
+        6: "TYPE_INT32",  # INT32
+        7: "TYPE_INT64",  # INT64
+        9: "TYPE_BOOL",  # BOOL
+        10: "TYPE_FP16",  # FLOAT16
+        11: "TYPE_FP64",  # DOUBLE
+    }
+    return type_map.get(onnx_type, "TYPE_FP32")  # Default to FP32
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Setup Triton Model Repository")
+    parser.add_argument("--model_path", required=True, help="Path to source ONNX model")
+    parser.add_argument(
+        "--repo_dir",
+        default="triton_model_repo",
+        help="Path to Triton model repository",
+    )
+    parser.add_argument(
+        "--model_name",
+        default="layoutlmv3-lora-invoice-number",
+        help="Name of the model in Triton",
+    )
+
+    args = parser.parse_args()
+
+    create_model_repo(args.model_path, args.repo_dir, args.model_name)
+
+    print("\n✅ Triton repository setup complete!")
+    print(f"  Repository path: {Path(args.repo_dir).absolute()}")
+    print("  To start Triton:")
+    print(
+        f"  docker run --rm -p 8000:8000 -p 8001:8001 -p 8002:8002 -v {Path(args.repo_dir).absolute()}:/models nvcr.io/nvidia/tritonserver:23.10-py3 tritonserver --model-repository=/models"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/api.py b/src/api.py
index fa7ca74..5749645 100644
--- a/src/api.py
+++ b/src/api.py
@@ -38,8 +38,7 @@ async def lifespan(app: FastAPI):
 
 app = FastAPI(
     title="Invoice NER API",
-    description="LayoutLMv3 model for extracting invoice numbers",
-    version="1.0.0",
+    description="Finetuned LayoutLMv3 model for extracting invoice numbers",
     lifespan=lifespan,
 )
 
@@ -55,14 +54,14 @@ class PredictionRequest(BaseModel):
 async def health_check():
     """Health check endpoint"""
     return {
-        "status": "healthy" if inference.model is not None else "unhealthy",
-        "model_loaded": inference.model is not None,
+        "status": "healthy" if inference.backend is not None else "unhealthy",
+        "model_loaded": inference.backend is not None,
         "device": inference.DEVICE,
     }
 
 
 @app.post("/predict")
-async def predict(
+def predict(
     image: UploadFile = File(..., description="Invoice image file (JPG, PNG, etc.)"),
     ocr_file: UploadFile = File(..., description="OCR data file (TXT or JSON format)"),
 ):
@@ -78,12 +77,12 @@ async def predict(
     Returns:
         JSON with extracted invoice number, method used, and detailed predictions
     """
-    if inference.model is None or inference.processor is None:
+    if inference.backend is None or inference.processor is None:
         raise HTTPException(status_code=503, detail="Model not loaded")
 
     try:
         # Read and validate image
-        image_bytes = await image.read()
+        image_bytes = image.file.read()
         try:
             pil_image = Image.open(io.BytesIO(image_bytes))
             pil_image = pil_image.convert("RGB")
@@ -93,7 +92,7 @@ async def predict(
         img_width, img_height = pil_image.size
 
         # Read and parse OCR file
-        ocr_bytes = await ocr_file.read()
+        ocr_bytes = ocr_file.file.read()
         ocr_filename = ocr_file.filename.lower()
 
         try:
diff --git a/src/inference.py b/src/inference.py
index fac27bf..1204a77 100644
--- a/src/inference.py
+++ b/src/inference.py
@@ -1,5 +1,5 @@
 """
-Model loading and inference for Invoice NER using ONNX Runtime.
+Model loading and inference for Invoice NER using ONNX Runtime or Triton Inference Server.
 """
 
 import os
@@ -9,6 +9,9 @@
 from typing import List, Dict
 from transformers import LayoutLMv3Processor
 import onnxruntime as ort
+from abc import ABC, abstractmethod
+import tritonclient.http as httpclient
+
 from .validation import validate_image, validate_words, validate_boxes
 
 ort.set_default_logger_severity(3)
@@ -18,6 +21,12 @@
 # CONFIGURATION
 # ============================================================================
 
+# Backend Configuration
+INFERENCE_BACKEND = os.getenv("INFERENCE_BACKEND", "onnx").lower()
+TRITON_URL = os.getenv("TRITON_URL", "localhost:8000")
+TRITON_MODEL_NAME = os.getenv("TRITON_MODEL_NAME", "layoutlmv3-lora-invoice-number")
+TRITON_MODEL_VERSION = os.getenv("TRITON_MODEL_VERSION", "1")
+
 # Load configuration from environment variables with defaults
 # Default to the quantized model if available, otherwise standard ONNX
 DEFAULT_MODEL_PATH = "models/artifacts/layoutlmv3_invoice_ner.onnx"
@@ -49,9 +58,152 @@
 logger.debug(f"Using device: {DEVICE} with providers: {PROVIDERS}")
 
 # Global model session and processor
-session = None
-model = None  # Alias for backward compatibility
 processor = None
+backend = None
+model = None  # Alias for backward compatibility (ONNX session)
+
+# ============================================================================
+# BACKEND ABSTRACTION
+# ============================================================================
+
+
+class InferenceBackend(ABC):
+    @abstractmethod
+    def load(self, model_path: str):
+        """Load the model or establish connection"""
+        pass
+
+    @abstractmethod
+    def predict(self, inputs: Dict[str, np.ndarray]) -> np.ndarray:
+        """Run inference and return logits"""
+        pass
+
+
+class OnnxBackend(InferenceBackend):
+    def __init__(self):
+        self.session = None
+
+    def load(self, model_path: str):
+        print(f"🚀 Loading ONNX model from {model_path}...")
+        print(f"📱 Using device: {DEVICE} (Providers: {PROVIDERS})")
+
+        try:
+            try:
+                print(f"👉 Attempting to load with providers: {PROVIDERS}")
+                self.session = ort.InferenceSession(model_path, providers=PROVIDERS)
+            except Exception as e:
+                if "CoreMLExecutionProvider" in PROVIDERS:
+                    print(f"⚠️ Failed to load with CoreML: {e}")
+                    print("🔄 Falling back to CPUExecutionProvider only...")
+                    self.session = ort.InferenceSession(
+                        model_path, providers=["CPUExecutionProvider"]
+                    )
+                else:
+                    raise e
+
+            print(
+                f"⚙️ Final Config: Model={model_path}, Device={DEVICE}, Providers={self.session.get_providers()}"
+            )
+            print("✅ ONNX Model loaded successfully!")
+        except Exception as e:
+            print(f"❌ Failed to load ONNX model: {e}")
+            raise
+
+    def predict(self, inputs: Dict[str, np.ndarray]) -> np.ndarray:
+        if self.session is None:
+            raise ValueError("ONNX Session not loaded.")
+
+        try:
+            outputs = self.session.run(None, inputs)
+            return outputs[0]  # Return logits
+        except Exception as e:
+            logger.error(f"ONNX Inference failed: {e}")
+            raise
+
+
+class TritonBackend(InferenceBackend):
+    def __init__(self):
+        # We don't store client state to avoid threading issues with gevent
+        self.model_name = TRITON_MODEL_NAME
+        self.model_version = TRITON_MODEL_VERSION
+
+    def load(self, model_path: str):
+        # We ignore model_path for Triton connection, but we can verify server health
+        print(f"🚀 Connecting to Triton Server at {TRITON_URL}...")
+        try:
+            # Create a temporary client for health check
+            client = httpclient.InferenceServerClient(url=TRITON_URL, verbose=False)
+            if not client.is_server_live():
+                raise ConnectionError("Triton server is not live")
+            if not client.is_server_ready():
+                raise ConnectionError("Triton server is not ready")
+            if not client.is_model_ready(self.model_name):
+                raise ValueError(
+                    f"Model {self.model_name} is not ready on Triton server"
+                )
+
+            print(f"✅ Connected to Triton Server! Model '{self.model_name}' is ready.")
+        except Exception as e:
+            print(f"❌ Failed to connect to Triton: {e}")
+            raise
+
+    def predict(self, inputs: Dict[str, np.ndarray]) -> np.ndarray:
+        # Create a fresh client for each request to ensure thread safety
+        # when running in a threadpool (FastAPI sync endpoints)
+        try:
+            client = httpclient.InferenceServerClient(url=TRITON_URL, verbose=False)
+        except Exception as e:
+            raise ValueError(f"Failed to create Triton client: {e}")
+
+        # Prepare Triton inputs
+        triton_inputs = []
+        for name, data in inputs.items():
+            # Map input names if necessary (LayoutLMv3 usually uses standard names)
+            # data matches numpy array
+            # Create InferInput
+            # Helper to map numpy types to triton types string if needed,
+            # but set_data_from_numpy handles it usually if type is standard.
+
+            # Explicit type conversion might be safer since Triton follows strong typing
+            triton_type = self._get_triton_datatype(data.dtype)
+            infer_input = httpclient.InferInput(name, data.shape, triton_type)
+            infer_input.set_data_from_numpy(data)
+            triton_inputs.append(infer_input)
+
+        try:
+            response = client.infer(model_name=self.model_name, inputs=triton_inputs)
+            # We assume the first output is logits, or look for 'logits' if available
+            # If we don't specify outputs, it returns all.
+            # Let's try to get 'logits' or fall back to the first available output
+
+            # Note: response.as_numpy(name) requires output name.
+            # We can inspect response.get_output(name) but we need names first.
+
+            # Get model metadata to find output name if strictly needed,
+            # but standard is often 'logits'. Let's trust the server returns what we need
+            # or use the first output since we only expect one main output (logits).
+
+            # with httpclient, response is a wrapper
+
+            # response.get_response() is metadata JSON
+            output_name = response.get_response()["outputs"][0]["name"]
+            return response.as_numpy(output_name)
+
+        except Exception as e:
+            logger.error(f"Triton Inference failed: {e}")
+            raise
+
+    def _get_triton_datatype(self, numpy_dtype):
+        # Simple mapper, extend as needed
+        if numpy_dtype == np.int64:
+            return "INT64"
+        if numpy_dtype == np.int32:
+            return "INT32"
+        if numpy_dtype == np.float32:
+            return "FP32"
+        if numpy_dtype == np.float64:
+            return "FP64"
+        return "FP32"  # Fallback/Assumption
 
 
 # ============================================================================
@@ -60,46 +212,28 @@
 
 
 def load_model():
-    """Load the LayoutLMv3 ONNX model and processor"""
-    global session, processor, model
+    """Load the model (backend) and processor"""
+    global processor, backend
 
     # Resolve paths
-    # If MODEL_PATH is a directory (legacy config), we use it for processor
-    # but need to find the ONNX file elsewhere
     model_path = MODEL_PATH
+    processor_path = BASE_MODEL
 
     if os.path.isdir(model_path):
         logger.warning(
             f"MODEL_PATH {model_path} is a directory. Assuming it contains processor config."
         )
         processor_path = model_path
-
-        # Check for standard model.onnx inside the directory
         potential_onnx = os.path.join(model_path, "model.onnx")
         if os.path.exists(potential_onnx):
-            logger.info(f"Found ONNX model at: {potential_onnx}")
             model_path = potential_onnx
-        else:
-            raise ValueError(
-                f"MODEL_PATH is a directory ({model_path}) but 'model.onnx' was not found inside it. Please point MODEL_PATH to the .onnx file directly."
-            )
-
     elif os.path.isfile(model_path):
-        # It's a file (likely the .onnx file)
-        # Use its parent directory for processor config
         processor_path = os.path.dirname(model_path)
-
-    elif not os.path.exists(model_path):
-        # Path doesn't exist, will fail later but set defaults for now
-        processor_path = BASE_MODEL
-        raise ValueError(f"MODEL_PATH {model_path} not found.")
-
-    print(f"🚀 Loading ONNX model from {model_path}...")
-    print(f"📱 Using device: {DEVICE} (Providers: {PROVIDERS})")
+    elif not os.path.exists(model_path) and INFERENCE_BACKEND == "onnx":
+        print(f"⚠️ MODEL_PATH {model_path} not found.")
 
     # Load processor
     try:
-        # Try loading from the resolved processor path
         processor = LayoutLMv3Processor.from_pretrained(processor_path, apply_ocr=False)
         print(f"✅ Processor loaded from {processor_path}")
     except Exception as e:
@@ -107,29 +241,22 @@ def load_model():
         print(f"   Falling back to base model: {BASE_MODEL}")
         processor = LayoutLMv3Processor.from_pretrained(BASE_MODEL, apply_ocr=False)
 
-    # Create ONNX Runtime session
-    try:
-        try:
-            print(f"👉 Attempting to load with providers: {PROVIDERS}")
-            session = ort.InferenceSession(model_path, providers=PROVIDERS)
-        except Exception as e:
-            if "CoreMLExecutionProvider" in PROVIDERS:
-                print(f"⚠️ Failed to load with CoreML: {e}")
-                print("🔄 Falling back to CPUExecutionProvider only...")
-                session = ort.InferenceSession(
-                    model_path, providers=["CPUExecutionProvider"]
-                )
-            else:
-                raise e
+    # Initialize Backend
+    print(f"👉 Initializing Inference Backend: {INFERENCE_BACKEND.upper()}")
 
-        model = session  # Alias for backward compatibility
-        print(
-            f"⚙️ Final Config: Model={model_path}, Device={DEVICE}, Providers={session.get_providers()}"
-        )
-        print("✅ Model loaded successfully!")
-    except Exception as e:
-        print(f"❌ Failed to load ONNX model: {e}")
-        raise
+    if INFERENCE_BACKEND == "triton":
+        backend = TritonBackend()
+    else:
+        # Default to ONNX
+        backend = OnnxBackend()
+
+    # Load Backend (Connect or Load File)
+    backend.load(model_path)
+
+    # helper for backward compatibility
+    if isinstance(backend, OnnxBackend):
+        global model
+        model = backend.session
 
 
 # ============================================================================
@@ -141,21 +268,10 @@ def predict_invoice(
     image: Image.Image, words: List[str], boxes: List[List[int]]
 ) -> Dict:
     """
-    Run inference on invoice using ONNX Runtime
-
-    Args:
-        image: PIL Image
-        words: List of OCR words
-        boxes: List of bounding boxes [x0, y0, x1, y1] normalized to 0-1000
-
-    Returns:
-        Dictionary with predictions and invoice number
-
-    Raises:
-        ValueError: If model not loaded, inputs are invalid, or dimensions mismatch
+    Run inference on invoice using the configured backend
     """
     # Validate model loaded
-    if session is None or processor is None:
+    if backend is None or processor is None:
         raise ValueError("Model not loaded. Call load_model() first.")
 
     # Validate inputs
@@ -171,26 +287,21 @@ def predict_invoice(
         truncation=True,
         padding="max_length",
         max_length=MAX_LENGTH,
-        return_tensors="np",  # Return numpy arrays for ONNX
+        return_tensors="np",
     )
 
-    # Prepare inputs for ONNX Runtime
-    input_feed = {
+    # Prepare inputs dictionary
+    inputs = {
         "pixel_values": encoding["pixel_values"],
         "input_ids": encoding["input_ids"],
         "attention_mask": encoding["attention_mask"],
         "bbox": encoding["bbox"],
     }
 
-    # Inference
-    try:
-        outputs = session.run(None, input_feed)
-        logits = outputs[0]  # Logits are the first output
-    except Exception as e:
-        logger.error(f"Inference failed: {e}")
-        raise
+    # Inference via Backend
+    logits = backend.predict(inputs)
 
-    # Post-processing (similar to PyTorch version but using numpy)
+    # Post-processing
     predictions = np.argmax(logits, axis=2)
 
     # Get confidence scores
@@ -207,15 +318,19 @@ def predict_invoice(
     invoice_tokens = []
     word_confidences = []
 
-    # Get ID to Label mapping from model config if available, otherwise use default
-    # The processor should have the label map if loaded from fine-tuned checkpoint,
-    # but for safety we define default mapping for this specific task
-    # User requested to keep original labels (LABEL_0, LABEL_1, LABEL_2)
-    id2label = {0: "LABEL_0", 1: "LABEL_1", 2: "LABEL_2"}
+    # Map IDs to Labels
+    # Use processor's id2label if available, else default
+    if hasattr(processor, "id2label") and processor.id2label:
+        id2label = processor.id2label
+    else:
+        id2label = {0: "LABEL_0", 1: "LABEL_1", 2: "LABEL_2"}
 
     token_boxes = encoding["bbox"][0].tolist()
 
     prev_word_idx = None
+
+    # Iterate through predictions
+    # Note: predictions[0], confidence_scores[0] because batch size is 1
     for idx, (pred, box, word_idx, conf) in enumerate(
         zip(
             predictions[0].tolist(),
@@ -226,14 +341,23 @@ def predict_invoice(
     ):
         # Skip special tokens and padding
         if box != [0, 0, 0, 0] and word_idx is not None:
-            label = id2label.get(pred, "LABEL_0")
+            # Handle integer keys in id2label (JSON keys are strings sometimes in configs)
+            # but here internal dict usually int keys.
+            label = id2label.get(pred, id2label.get(str(pred), "LABEL_0"))
 
             # Only take first subtoken prediction for each word
             if word_idx != prev_word_idx:
                 predicted_labels.append(label)
                 word_confidences.append(conf)
 
-                # Extract invoice number tokens
+                # Extract invoice number tokens (Assuming standard labels or user specific)
+                # Adjust these labels if your model uses different names (e.g., B-INVOICE, I-INVOICE)
+                # The user previous code had explicit LABEL_1/LABEL_2 checks, so we keep that logic
+                # or match user intent. The previous code usage:
+                # if label == "LABEL_1" or label == "LABEL_2":
+
+                # Check for "INVOICE" strings just in case logic changes,
+                # but let's stick to the previous implementation's specific logic:
                 if label == "LABEL_1" or label == "LABEL_2":
                     invoice_tokens.append(words[word_idx])
 
diff --git a/tests/test_api.py b/tests/test_api.py
index fa5a342..c081b7c 100644
--- a/tests/test_api.py
+++ b/tests/test_api.py
@@ -37,8 +37,8 @@ class TestHealthEndpoint:
     """Test suite for /health endpoint"""
 
     def test_health_check_model_loaded(self, client):
-        """Test health check when model is loaded"""
-        with patch("src.inference.model", Mock()):
+        """Test health check when model is loaded (standard ONNX)"""
+        with patch("src.inference.backend", Mock()):
             response = client.get("/health")
 
             assert response.status_code == 200
@@ -50,9 +50,20 @@ def test_health_check_model_loaded(self, client):
             assert data["model_loaded"] is True
             assert data["status"] == "healthy"
 
+    def test_health_check_triton_backend(self, client):
+        """Test health check when using Triton (model is None, backend is set)"""
+        with patch("src.inference.model", None), patch("src.inference.backend", Mock()):
+            response = client.get("/health")
+
+            assert response.status_code == 200
+            data = response.json()
+
+            assert data["model_loaded"] is True
+            assert data["status"] == "healthy"
+
     def test_health_check_model_not_loaded(self, client):
-        """Test health check when model is not loaded"""
-        with patch("src.inference.model", None):
+        """Test health check when no backend is loaded"""
+        with patch("src.inference.backend", None):
             response = client.get("/health")
 
             assert response.status_code == 200
@@ -63,7 +74,7 @@ def test_health_check_model_not_loaded(self, client):
 
     def test_health_check_device_info(self, client):
         """Test that device information is included"""
-        with patch("src.inference.model", Mock()):
+        with patch("src.inference.backend", Mock()):
             response = client.get("/health")
             data = response.json()
 
@@ -123,7 +134,7 @@ def test_gradio_predict_no_text_file(self):
         assert "Please upload a text file" in result[0]
         assert result[1] is None
 
-    @patch("src.inference.model", Mock())
+    @patch("src.inference.backend", Mock())
     @patch("src.inference.processor", Mock())
     def test_gradio_predict_with_json_file(self, temp_json_file):
         """Test gradio_predict with JSON file"""
@@ -164,13 +175,13 @@ class TestIntegrationAPI:
 
     def test_health_endpoint_accessible(self, client):
         """Test that health endpoint is accessible"""
-        with patch("src.inference.model", Mock()):
+        with patch("src.inference.backend", Mock()):
             response = client.get("/health")
             assert response.status_code == 200
 
     def test_root_endpoint_gradio(self, full_app_client):
         """Test that root endpoint serves Gradio interface"""
-        with patch("src.inference.model", Mock()):
+        with patch("src.inference.backend", Mock()):
             response = full_app_client.get("/")
             # Gradio interface should be served
             assert response.status_code == 200
@@ -181,13 +192,16 @@ class TestErrorHandling:
 
     def test_invalid_endpoint(self, client):
         """Test accessing invalid endpoint"""
-        with patch("src.inference.model", Mock()):
+        with patch("src.inference.backend", Mock()):
             response = client.get("/invalid-endpoint")
             assert response.status_code == 404
 
     def test_health_check_always_responds(self, client):
         """Test that health check always responds even if model fails"""
-        with patch("src.inference.model", None), patch("src.inference.processor", None):
+        with (
+            patch("src.inference.backend", None),
+            patch("src.inference.processor", None),
+        ):
             response = client.get("/health")
             # Should still return 200, but indicate unhealthy
             assert response.status_code == 200
diff --git a/tests/test_app.py b/tests/test_app.py
index 291a81f..ee1629e 100644
--- a/tests/test_app.py
+++ b/tests/test_app.py
@@ -358,12 +358,12 @@ def test_create_annotated_mismatched_lengths(self, sample_image):
 class TestPredictInvoice:
     """Test suite for predict_invoice function"""
 
-    @patch("src.inference.session")
+    @patch("src.inference.backend")
     @patch("src.inference.processor")
     def test_predict_valid_input(
         self,
         mock_processor,
-        mock_session,
+        mock_backend,
         sample_image,
         sample_words,
         sample_boxes_normalized,
@@ -415,7 +415,8 @@ def test_predict_valid_input(
                 ]
             ]
         )
-        mock_session.run.return_value = [mock_logits]
+        # Mock backend output (logits ndarray)
+        mock_backend.predict.return_value = mock_logits
 
         # Run prediction
         result = predict_invoice(sample_image, sample_words, sample_boxes_normalized)
@@ -438,7 +439,7 @@ def test_predict_model_not_loaded(
     ):
         """Test prediction when model (session) is not loaded"""
         with (
-            patch("src.inference.session", None),
+            patch("src.inference.backend", None),
             patch("src.inference.processor", None),
         ):
             with pytest.raises(ValueError, match="Model not loaded"):
@@ -447,7 +448,7 @@ def test_predict_model_not_loaded(
     def test_predict_wrong_image_type(self, sample_words, sample_boxes_normalized):
         """Test with wrong image type - will fail at image.size access"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(AttributeError):  # 'str' object has no attribute 'size'
@@ -460,7 +461,7 @@ def test_predict_invalid_image_dimensions(
         invalid_img = Image.new("RGB", (0, 0))
 
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Invalid image dimensions"):
@@ -469,7 +470,7 @@ def test_predict_invalid_image_dimensions(
     def test_predict_empty_words(self, sample_image, sample_boxes_normalized):
         """Test with empty words list"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Words list cannot be empty"):
@@ -478,7 +479,7 @@ def test_predict_empty_words(self, sample_image, sample_boxes_normalized):
     def test_predict_wrong_words_type(self, sample_image, sample_boxes_normalized):
         """Test with wrong type for words - will fail at len() comparison"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             # String has different length than boxes, will fail at mismatch check
@@ -488,7 +489,7 @@ def test_predict_wrong_words_type(self, sample_image, sample_boxes_normalized):
     def test_predict_non_string_words(self, sample_image, sample_boxes_normalized):
         """Test with non-string elements in words"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(TypeError, match="All words must be strings"):
@@ -499,7 +500,7 @@ def test_predict_non_string_words(self, sample_image, sample_boxes_normalized):
     def test_predict_wrong_boxes_type(self, sample_image, sample_words):
         """Test with wrong type for boxes - will fail at len() comparison"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             # String has different length than words, will fail at mismatch check
@@ -511,7 +512,7 @@ def test_predict_mismatched_lengths(
     ):
         """Test with mismatched words and boxes lengths"""
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Mismatch"):
@@ -523,7 +524,7 @@ def test_predict_invalid_box_format(self, sample_image):
         invalid_boxes = [[100, 100, 200]]  # Only 3 coordinates
 
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Box 0 must be a list of 4 integers"):
@@ -535,7 +536,7 @@ def test_predict_box_out_of_range(self, sample_image):
         invalid_boxes = [[100, 100, 1500, 120]]  # 1500 > 1000
 
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Box 0 coordinates must be in range"):
@@ -547,7 +548,7 @@ def test_predict_invalid_box_geometry(self, sample_image):
         invalid_boxes = [[200, 100, 100, 120]]  # x0 > x1
 
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(ValueError, match="Box 0 has invalid geometry"):
@@ -559,7 +560,7 @@ def test_predict_box_non_numeric(self, sample_image):
         invalid_boxes = [["100", "100", "200", "120"]]  # Strings instead of numbers
 
         with (
-            patch("src.inference.session", Mock()),
+            patch("src.inference.backend", Mock()),
             patch("src.inference.processor", Mock()),
         ):
             with pytest.raises(TypeError, match="Box 0 coordinates must be numeric"):
diff --git a/triton_model_repo/layoutlmv3-lora-invoice-number/config.pbtxt b/triton_model_repo/layoutlmv3-lora-invoice-number/config.pbtxt
new file mode 100644
index 0000000..ba69ba6
--- /dev/null
+++ b/triton_model_repo/layoutlmv3-lora-invoice-number/config.pbtxt
@@ -0,0 +1,32 @@
+name: "layoutlmv3-lora-invoice-number"
+platform: "onnxruntime_onnx"
+max_batch_size: 8
+
+dynamic_batching { }
+
+input {
+  name: "pixel_values"
+  data_type: TYPE_FP32
+  dims: [3, 224, 224]
+}
+input {
+  name: "input_ids"
+  data_type: TYPE_INT64
+  dims: [-1]
+}
+input {
+  name: "attention_mask"
+  data_type: TYPE_INT64
+  dims: [-1]
+}
+input {
+  name: "bbox"
+  data_type: TYPE_INT64
+  dims: [-1, 4]
+}
+
+output {
+  name: "logits"
+  data_type: TYPE_FP32
+  dims: [-1, 3]
+}
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 4ccad71..bf0541c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,15 @@
 version = 1
 revision = 3
-requires-python = ">=3.11"
+requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.13'",
-    "python_full_version == '3.12.*'",
-    "python_full_version < '3.12'",
+    "python_full_version >= '3.13' and sys_platform == 'linux'",
+    "python_full_version >= '3.13' and sys_platform != 'linux'",
+    "python_full_version == '3.12.*' and sys_platform == 'linux'",
+    "python_full_version == '3.12.*' and sys_platform != 'linux'",
+    "python_full_version == '3.11.*' and sys_platform == 'linux'",
+    "python_full_version == '3.11.*' and sys_platform != 'linux'",
+    "python_full_version < '3.11' and sys_platform == 'linux'",
+    "python_full_version < '3.11' and sys_platform != 'linux'",
 ]
 required-markers = [
     "sys_platform == 'darwin'",
@@ -16,7 +21,8 @@ version = "1.10.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
+    { name = "numpy", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -53,6 +59,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
     { name = "attrs" },
     { name = "frozenlist" },
     { name = "multidict" },
@@ -61,6 +68,23 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/9b/e7/d92a237d8802ca88483906c388f7c201bbe96cd80a165ffd0ac2f6a8d59f/aiohttp-3.12.15.tar.gz", hash = "sha256:4fc61385e9c98d72fcdf47e6dd81833f47b2f77c114c29cd64a361be57a763a2", size = 7823716, upload-time = "2025-07-29T05:52:32.215Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/8f/42/63fccfc3a7ed97eb6e1a71722396f409c46b60a0552d8a56d7aad74e0df5/aiohttp-3.12.15-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:098e92835b8119b54c693f2f88a1dec690e20798ca5f5fe5f0520245253ee0af", size = 480288, upload-time = "2025-07-29T05:49:47.851Z" },
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