diff --git a/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama.py b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama.py
new file mode 100644
index 00000000..25607b88
--- /dev/null
+++ b/python/llm/example/NPU/HF-Transformers-AutoModels/LLM/Pipeline-Models/llama.py
@@ -0,0 +1,90 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+
+import torch
+import time
+import argparse
+from ipex_llm.transformers.npu_pipeline_model import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+def get_prompt(message: str, chat_history: list[tuple[str, str]],
+               system_prompt: str) -> str:
+    texts = [f'<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n']
+    # The first user input is _not_ stripped
+    do_strip = False
+    for user_input, response in chat_history:
+        user_input = user_input.strip() if do_strip else user_input
+        do_strip = True
+        texts.append(f'{user_input} [/INST] {response.strip()} </s><s>[INST] ')
+    message = message.strip() if do_strip else message
+    texts.append(f'{message} [/INST]')
+    return ''.join(texts)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Predict Tokens using `generate()` API for npu model"
+    )
+    parser.add_argument(
+        "--repo-id-or-model-path",
+        type=str,
+        default=r"C:\\Llama2-converted-weights\\",
+        help="The folder path of converted model blobs",
+    )
+    parser.add_argument('--prompt', type=str, default="What is AI?",
+                        help='Prompt to infer')
+    parser.add_argument("--n-predict", type=int, default=32, help="Max tokens to predict")
+    parser.add_argument("--max-output-len", type=int, default=1024)
+
+    args = parser.parse_args()
+    model_path = args.repo_id_or_model_path
+
+    model = AutoModelForCausalLM.from_pretrained(model_path,
+                                                 ov_model=True,
+                                                 max_output_len=args.max_output_len,
+                                                 model_name="Model70")
+
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+    DEFAULT_SYSTEM_PROMPT = """\
+    """
+
+    print("-" * 80)
+    print("done")
+    with torch.inference_mode():
+        print("finish to load")
+        prompt = get_prompt(args.prompt, [], system_prompt=DEFAULT_SYSTEM_PROMPT)
+        _input_ids = tokenizer.encode(prompt, return_tensors="pt")
+        print("input length:", len(_input_ids[0]))
+        st = time.time()
+        output = model.generate(
+            _input_ids, max_new_tokens=args.n_predict,
+        )
+        end = time.time()
+        print(f"Inference time: {end-st} s")
+        input_str = tokenizer.decode(_input_ids[0], skip_special_tokens=False)
+        print("-" * 20, "Input", "-" * 20)
+        print(input_str)
+        output_str = tokenizer.decode(output[0], skip_special_tokens=False)
+        print("-" * 20, "Output", "-" * 20)
+        print(output_str)
+
+    print("-" * 80)
+    print("done")
+    print("success shut down")
diff --git a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/__init__.py b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/__init__.py
new file mode 100644
index 00000000..50632808
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/__init__.py
@@ -0,0 +1,17 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+from .pipeline_model import *
diff --git a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_cpp.py b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_cpp.py
new file mode 100644
index 00000000..4bd8f579
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_cpp.py
@@ -0,0 +1,64 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import os
+import sys
+import ctypes
+import pathlib
+from ipex_llm.utils.common import invalidInputError
+
+
+def get_shared_lib_info(lib_base_name: str):
+    # Determine the file extension based on the platform
+    if sys.platform.startswith("linux") or sys.platform == "darwin":
+        lib_ext = ".so"
+    elif sys.platform == "win32":
+        lib_ext = ".dll"
+    else:
+        invalidInputError(False, "Unsupported platform.")
+
+    # Construct the paths to the possible shared library names (python/llm/src/ipex-llm/llm/libs)
+    _base_path = pathlib.Path(__file__).parent.parent.parent.resolve()
+    _base_path = _base_path / 'libs'
+
+    lib_path = os.path.join(_base_path, lib_base_name + lib_ext)
+
+    return _base_path, lib_path
+
+_, _lib_path = get_shared_lib_info("pipeline")
+
+# Load the library
+_lib = ctypes.cdll.LoadLibrary(_lib_path)
+
+_lib.InitLLMPipeline.argtypes = [ctypes.c_int] * 5 + [ctypes.c_char_p] * 5
+_lib.InitLLMPipeline.restype = ctypes.c_int
+
+_lib.generate_serve.argtypes = [ctypes.c_int] * 5
+_lib.generate_serve.restype = ctypes.c_int
+
+
+def InitLLMPipeline(kv_len: int, num_head: int, head_dim: int, num_layers: int, vocab_size: int,
+                    model_weight_dir: str, model_name: str,
+                    first_blob_name: str, last_blob_name: str, rest_blob_name: str):
+    return _lib.InitLLMPipeline(kv_len, num_head, head_dim, num_layers, vocab_size,
+                                model_weight_dir.encode('utf-8'), model_name.encode('utf-8'),
+                                first_blob_name.encode('utf-8'), last_blob_name.encode('utf-8'),
+                                rest_blob_name.encode('utf-8'))
+
+
+def generate_serve(kv_len: int, num_head: int, head_dim: int, num_layers: int,
+                   param_n_output: int):
+    _lib.generate_serve(kv_len, num_head, head_dim, num_layers, param_n_output)
diff --git a/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_model.py b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_model.py
new file mode 100644
index 00000000..25bfd30a
--- /dev/null
+++ b/python/llm/src/ipex_llm/transformers/npu_pipeline_model/pipeline_model.py
@@ -0,0 +1,246 @@
+#
+# Copyright 2016 The BigDL Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+import time
+import numpy
+import warnings
+import torch
+import sys
+import transformers
+from typing import List
+from unittest.mock import patch
+from transformers.dynamic_module_utils import get_imports
+from .pipeline_cpp import InitLLMPipeline, generate_serve
+from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
+from transformers import GenerationConfig, \
+    LogitsProcessorList, StoppingCriteriaList
+import threading
+from ipex_llm.utils.common import invalidInputError
+import os
+from transformers import PretrainedConfig
+
+
+def patch_flash_attn_import(filename: str) -> List[str]:
+    """Work around for https://huggingface.co/microsoft/phi-1_5/discussions/72."""
+    imports = get_imports(filename)
+    if "flash_attn" in imports:
+        imports.remove("flash_attn")
+    return imports
+
+
+def ignore_argument(kwargs: dict, key: "str"):
+    arg = kwargs.pop(key, None)
+    if arg is not None:
+        warnings.warn(f"argument `{key}={arg}` will be ignored")
+
+
+def generate(
+    self,
+    inputs: Optional[torch.Tensor] = None,
+    generation_config: Optional[GenerationConfig] = None,
+    logits_processor: Optional[LogitsProcessorList] = None,
+    stopping_criteria: Optional[StoppingCriteriaList] = None,
+    prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]]=None,
+    synced_gpus: Optional[bool] = None,
+    assistant_model: Optional["PreTrainedModel"] = None,
+    streamer: Optional["BaseStreamer"] = None,
+    **kwargs,
+):
+    new_generate_kwargs = {}
+    for var in ['max_new_tokens', 'attention_mask', 'eos_token_id']:
+        value = kwargs.pop(var, None)
+        if value is not None:
+            new_generate_kwargs[var] = value
+
+    if isinstance(inputs[0], torch.Tensor):
+        numpy_input = inputs[0].numpy()
+    else:
+        numpy_input = inputs[0]
+    input_length = numpy.size(numpy_input)
+
+    new_tokens = new_generate_kwargs['max_new_tokens']
+    invalidInputError(input_length + new_tokens <= self.kv_len + 1,
+                      "Input plus output tokens should not exceed max_output_len.")
+
+    # start generate_serve by Thread
+    thread = threading.Thread(target=generate_serve,
+                              args=(self.kv_len, self.num_head,
+                                    self.head_dim, self.num_layers,
+                                    new_tokens))
+    thread.start()
+
+    in_pipe_path = "\\\\.\\pipe\\llminputpipe"
+    out_pipe_path = "\\\\.\\pipe\\llmoutputpipe"
+
+    while True:
+        try:
+            input_pipe = open(in_pipe_path, "wb")
+        except:
+            print('Waiting for input pipe')
+            time.sleep(1)
+        else:
+            break
+
+    while True:
+        try:
+            output_pipe = open(out_pipe_path, "rb")
+        except:
+            print('Waiting for output pipe')
+            time.sleep(1)
+        else:
+            break
+
+    bdata = b''
+    for i in range(0, input_length):
+        d = int(numpy_input[i])
+        bdata = bdata + d.to_bytes(4, sys.byteorder)
+
+    if "eos_token_id" not in new_generate_kwargs:
+        eos = 0xffffffff
+    else:
+        eos = new_generate_kwargs["eos_token_id"]
+
+    bdata = bdata + eos.to_bytes(4, sys.byteorder)
+
+    input_pipe.write(bytearray(bdata))
+    input_pipe.flush()
+
+    buffersize = 4
+    output_tokens = []
+    while True:
+        data = output_pipe.read(buffersize)
+        if len(data) == 0:
+            break
+        token = int.from_bytes(data, sys.byteorder)
+        output_tokens.append(torch.tensor([token]))
+        if streamer is not None:
+            streamer.put(torch.tensor([token]))
+        if token == eos:
+            break
+
+    output = torch.stack(output_tokens, dim=1)
+    if streamer is not None:
+        streamer.end()
+
+    thread.join()
+    return output
+
+
+class NPUModel():
+    def __init__(self):
+        pass
+
+
+class _BaseAutoModelClass:
+    HF_MODEL = None
+
+    @classmethod
+    @patch("transformers.dynamic_module_utils.get_imports", patch_flash_attn_import)
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        """
+        Load a model from a directory or the HF Hub.
+        The loaded model will run supported OPs on NPU, then run other OPs on CPU.
+
+        Three new arguments are added to extend Hugging Face's from_pretrained method as follows:
+        :param ov_model: boolean value, whether load blob files from specified directory.
+                         If it's False, will convert HF model to specified blob format,
+                         but which is not supported now. Default to True.
+        :param max_output_len: Maximum context length for whole generation, default to 1024.
+        :param model_name: Name prefix of the model weight bin file.
+        :return: a model instance
+        """
+        ov_model = kwargs.get("ov_model", True)
+        max_output_len = kwargs.pop("max_output_len", 1024)
+
+        invalidInputError(ov_model,
+                          "Original HF model is not supported now.")
+        invalidInputError(os.path.exists(pretrained_model_name_or_path),
+                          "This directory does not exist, please double check it.")
+
+        config_json = os.path.join(pretrained_model_name_or_path, "config.json")
+        invalidInputError(os.path.exists(config_json),
+                          "config.json is not found in current directory, please double check it.")
+        config = PretrainedConfig.from_json_file(config_json)
+        model = NPUModel()
+        model.kv_len = max_output_len - 1
+        model.num_head = config.num_attention_heads
+        model.head_dim = config.hidden_size // config.num_attention_heads
+        model.num_layers = config.num_hidden_layers
+        model.vocab_size = config.vocab_size
+
+        model_weight_dir = os.path.join(pretrained_model_name_or_path, "model_layer_weights")
+        model_name = kwargs.get("model_name", "Model")
+        first_blob_name = os.path.join(pretrained_model_name_or_path, "first_model.blob")
+        last_blob_name = os.path.join(pretrained_model_name_or_path, "last_model.blob")
+        rest_blob_name = os.path.join(pretrained_model_name_or_path, "rest_model.blob")
+
+        for path in [model_weight_dir, first_blob_name, last_blob_name, rest_blob_name]:
+            invalidInputError(os.path.exists(path),
+                              f"{path} is not found in current directory, please double check it.")
+
+        try:
+            res = InitLLMPipeline(model.kv_len, model.num_head, model.head_dim, model.num_layers,
+                                  model.vocab_size, model_weight_dir, model_name,
+                                  first_blob_name, last_blob_name, rest_blob_name)
+        except:
+            invalidInputError(False,
+                              "False to InitLLMPipeline.")
+            exit(0)
+
+        # patch generate function
+        import types
+        model.generate = types.MethodType(generate, model)
+        return model
+
+
+class AutoModelForCausalLM(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForCausalLM
+
+
+class AutoModel(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModel
+
+
+class AutoModelForSpeechSeq2Seq(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForSpeechSeq2Seq
+
+
+class AutoModelForSeq2SeqLM(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForSeq2SeqLM
+
+
+class AutoModelForSequenceClassification(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForSequenceClassification
+
+
+class AutoModelForMaskedLM(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForMaskedLM
+
+
+class AutoModelForQuestionAnswering(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForQuestionAnswering
+
+
+class AutoModelForNextSentencePrediction(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForNextSentencePrediction
+
+
+class AutoModelForMultipleChoice(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForMultipleChoice
+
+
+class AutoModelForTokenClassification(_BaseAutoModelClass):
+    HF_Model = transformers.AutoModelForTokenClassification