diff --git a/python/llm/src/bigdl/llm/ggml/model/llama/llama.py b/python/llm/src/bigdl/llm/ggml/model/llama/llama.py
index b40c66e9..16d49f16 100644
--- a/python/llm/src/bigdl/llm/ggml/model/llama/llama.py
+++ b/python/llm/src/bigdl/llm/ggml/model/llama/llama.py
@@ -14,6 +14,33 @@
 # limitations under the License.
 #
 
+# ===========================================================================
+#
+# This file is adapted from
+# https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama.py
+#
+# MIT License
+#
+# Copyright (c) 2023 Andrei Betlen
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
 # This would makes sure Python is aware there is more than one sub-package within bigdl,
 # physically located elsewhere.
 # Otherwise there would be module not found error in non-pip's setting as Python would
@@ -27,7 +54,7 @@ import math
 import multiprocessing
 from typing import List, Optional, Union, Generator, Sequence, Iterator, Deque, Tuple
 from collections import deque, OrderedDict
-
+from bigdl.llm.utils.common import invalidInputError
 from . import llama_cpp
 from .llama_types import *
 
@@ -61,8 +88,7 @@ class LlamaCache:
     def __getitem__(self, key: Sequence[int]) -> "LlamaState":
         key = tuple(key)
         _key = self._find_longest_prefix_key(key)
-        if _key is None:
-            raise KeyError(f"Key not found")
+        invalidInputError(_key is not None, "Key not found.")
         value = self.cache_state[_key]
         self.cache_state.move_to_end(_key)
         return value
@@ -122,7 +148,8 @@ class Llama:
         Args:
             model_path: Path to the model.
             n_ctx: Maximum context size.
-            n_parts: Number of parts to split the model into. If -1, the number of parts is automatically determined.
+            n_parts: Number of parts to split the model into. If -1, the number of parts
+            is automatically determined.
             seed: Random seed. 0 for random.
             f16_kv: Use half-precision for key/value cache.
             logits_all: Return logits for all tokens, not just the last token.
@@ -130,10 +157,12 @@ class Llama:
             use_mmap: Use mmap if possible.
             use_mlock: Force the system to keep the model in RAM.
             embedding: Embedding mode only.
-            n_threads: Number of threads to use. If None, the number of threads is automatically determined.
+            n_threads: Number of threads to use. If None, the number of threads is
+            automatically determined.
             n_batch: Maximum number of prompt tokens to batch together when calling llama_eval.
             last_n_tokens_size: Maximum number of tokens to keep in the last_n_tokens deque.
-            lora_base: Optional path to base model, useful if using a quantized base model and you want to apply LoRA to an f16 model.
+            lora_base: Optional path to base model, useful if using a quantized base model and
+            you want to apply LoRA to an f16 model.
             lora_path: Path to a LoRA file to apply to the model.
             verbose: Print verbose output to stderr.
 
@@ -169,18 +198,17 @@ class Llama:
         self.lora_base = lora_base
         self.lora_path = lora_path
 
-        ### DEPRECATED ###
+        # DEPRECATED
         self.n_parts = n_parts
-        ### DEPRECATED ###
+        # DEPRECATED
 
-        if not os.path.exists(model_path):
-            raise ValueError(f"Model path does not exist: {model_path}")
+        invalidInputError(os.path.exists(model_path), f"Model path does not exist: {model_path}.")
 
         self.ctx = llama_cpp.llama_init_from_file(
             self.model_path.encode("utf-8"), self.params
         )
 
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
 
         if self.lora_path:
             if llama_cpp.llama_apply_lora_from_file(
@@ -191,9 +219,9 @@ class Llama:
                 else llama_cpp.c_char_p(0),
                 llama_cpp.c_int(self.n_threads),
             ):
-                raise RuntimeError(
-                    f"Failed to apply LoRA from lora path: {self.lora_path} to base path: {self.lora_base}"
-                )
+                invalidInputError(False,
+                                  "Failed to apply LoRA from lora path: "
+                                  f"{self.lora_path} to base path: {self.lora_base}")
 
         if self.verbose:
             print(llama_cpp.llama_print_system_info().decode("utf-8"), file=sys.stderr)
@@ -233,7 +261,7 @@ class Llama:
         Returns:
             A list of tokens.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         n_ctx = llama_cpp.llama_n_ctx(self.ctx)
         tokens = (llama_cpp.llama_token * int(n_ctx))()
         n_tokens = llama_cpp.llama_tokenize(
@@ -253,10 +281,8 @@ class Llama:
                 llama_cpp.c_int(n_tokens),
                 llama_cpp.c_bool(add_bos),
             )
-            if n_tokens < 0:
-                raise RuntimeError(
-                    f'Failed to tokenize: text="{text}" n_tokens={n_tokens}'
-                )
+            invalidInputError(n_tokens >= 0,
+                              f'Failed to tokenize: text="{text}" n_tokens={n_tokens}')
         return list(tokens[:n_tokens])
 
     def detokenize(self, tokens: List[int]) -> bytes:
@@ -268,7 +294,7 @@ class Llama:
         Returns:
             The detokenized string.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         output = b""
         for token in tokens:
             output += llama_cpp.llama_token_to_str(
@@ -295,10 +321,10 @@ class Llama:
         Args:
             tokens: The list of tokens to evaluate.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         n_ctx = int(llama_cpp.llama_n_ctx(self.ctx))
         for i in range(0, len(tokens), self.n_batch):
-            batch = tokens[i : min(len(tokens), i + self.n_batch)]
+            batch = tokens[i: min(len(tokens), i + self.n_batch)]
             n_past = min(n_ctx - len(batch), len(self.eval_tokens))
             n_tokens = len(batch)
             return_code = llama_cpp.llama_eval(
@@ -308,8 +334,7 @@ class Llama:
                 n_past=llama_cpp.c_int(n_past),
                 n_threads=llama_cpp.c_int(self.n_threads),
             )
-            if int(return_code) != 0:
-                raise RuntimeError(f"llama_eval returned {return_code}")
+            invalidInputError(int(return_code) == 0, f"llama_eval returned {return_code}.")
             # Save tokens
             self.eval_tokens.extend(batch)
             # Save logits
@@ -338,8 +363,9 @@ class Llama:
         mirostat_eta: llama_cpp.c_float,
         penalize_nl: bool = True,
     ):
-        assert self.ctx is not None
-        assert len(self.eval_logits) > 0
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
+        invalidInputError(len(self.eval_logits) > 0,
+                          "The attribute `eval_logits` of `Llama` object is None.")
         n_vocab = self.n_vocab()
         n_ctx = self.n_ctx()
         top_k = llama_cpp.c_int(n_vocab) if top_k.value <= 0 else top_k
@@ -467,10 +493,10 @@ class Llama:
         Returns:
             The sampled token.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         last_n_tokens_data = [llama_cpp.llama_token(0)] * max(
             0, self.last_n_tokens_size - len(self.eval_tokens)
-        ) + list(self.eval_tokens)[-self.last_n_tokens_size :]
+        ) + list(self.eval_tokens)[-self.last_n_tokens_size:]
         return self._sample(
             last_n_tokens_data=(llama_cpp.llama_token * self.last_n_tokens_size)(
                 *last_n_tokens_data
@@ -509,7 +535,8 @@ class Llama:
         Examples:
             >>> llama = Llama("models/ggml-7b.bin")
             >>> tokens = llama.tokenize(b"Hello, world!")
-            >>> for token in llama.generate(tokens, top_k=40, top_p=0.95, temp=1.0, repeat_penalty=1.1):
+            >>> for token in llama.generate(tokens, top_k=40, top_p=0.95,
+            >>>                             temp=1.0, repeat_penalty=1.1):
             ...     print(llama.detokenize([token]))
 
         Args:
@@ -523,7 +550,7 @@ class Llama:
         Yields:
             The generated tokens.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
 
         if reset and len(self.eval_tokens) > 0:
             longest_prefix = 0
@@ -577,13 +604,11 @@ class Llama:
         Returns:
             An embedding object.
         """
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         model_name: str = model if model is not None else self.model_path
 
-        if self.params.embedding == False:
-            raise RuntimeError(
-                "Llama model must be created with embedding=True to call this method"
-            )
+        invalidInputError(self.params.embedding,
+                          "Llama model must be created with embedding=True to call this method.")
 
         if self.verbose:
             llama_cpp.llama_reset_timings(self.ctx)
@@ -645,7 +670,7 @@ class Llama:
         top_p: float = 0.95,
         logprobs: Optional[int] = None,
         echo: bool = False,
-        stop: Optional[Union[str, List[str]]] = [],
+        stop: Optional[Union[str, List[str]]]=[],
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         repeat_penalty: float = 1.1,
@@ -657,7 +682,7 @@ class Llama:
         mirostat_eta: float = 0.1,
         model: Optional[str] = None,
     ) -> Union[Iterator[Completion], Iterator[CompletionChunk]]:
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         completion_id: str = f"cmpl-{str(uuid.uuid4())}"
         created: int = int(time.time())
         completion_tokens: List[int] = []
@@ -673,10 +698,9 @@ class Llama:
         if self.verbose:
             llama_cpp.llama_reset_timings(self.ctx)
 
-        if len(prompt_tokens) + max_tokens > int(llama_cpp.llama_n_ctx(self.ctx)):
-            raise ValueError(
-                f"Requested tokens exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
-            )
+        invalidInputError(len(prompt_tokens) + max_tokens <= int(llama_cpp.llama_n_ctx(self.ctx)),
+                          "Requested tokens exceed context window of "
+                          f"{llama_cpp.llama_n_ctx(self.ctx)}.")
 
         if stop != []:
             stop_sequences = [s.encode("utf-8") for s in stop]
@@ -684,9 +708,8 @@ class Llama:
             stop_sequences = []
 
         if logprobs is not None and self.params.logits_all is False:
-            raise ValueError(
-                "logprobs is not supported for models created with logits_all=False"
-            )
+            invalidInputError(False,
+                              "logprobs is not supported for models created with logits_all=False")
 
         if self.cache:
             try:
@@ -1023,7 +1046,7 @@ class Llama:
         top_p: float = 0.95,
         logprobs: Optional[int] = None,
         echo: bool = False,
-        stop: Optional[Union[str, List[str]]] = [],
+        stop: Optional[Union[str, List[str]]]=[],
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         repeat_penalty: float = 1.1,
@@ -1092,7 +1115,7 @@ class Llama:
         top_p: float = 0.95,
         logprobs: Optional[int] = None,
         echo: bool = False,
-        stop: Optional[Union[str, List[str]]] = [],
+        stop: Optional[Union[str, List[str]]]=[],
         frequency_penalty: float = 0.0,
         presence_penalty: float = 0.0,
         repeat_penalty: float = 1.1,
@@ -1212,7 +1235,7 @@ class Llama:
         top_p: float = 0.95,
         top_k: int = 40,
         stream: bool = False,
-        stop: Optional[Union[str, List[str]]] = [],
+        stop: Optional[Union[str, List[str]]]=[],
         max_tokens: int = 256,
         presence_penalty: float = 0.0,
         frequency_penalty: float = 0.0,
@@ -1294,9 +1317,9 @@ class Llama:
             n_threads=self.n_threads,
             lora_base=self.lora_base,
             lora_path=self.lora_path,
-            ### DEPRECATED ###
+            # DEPRECATED
             n_parts=self.n_parts,
-            ### DEPRECATED ###
+            # DEPRECATED
         )
 
     def __setstate__(self, state):
@@ -1321,12 +1344,11 @@ class Llama:
         )
 
     def save_state(self) -> LlamaState:
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         state_size = llama_cpp.llama_get_state_size(self.ctx)
         llama_state = (llama_cpp.c_uint8 * int(state_size))()
         n_bytes = llama_cpp.llama_copy_state_data(self.ctx, llama_state)
-        if int(n_bytes) > int(state_size):
-            raise RuntimeError("Failed to copy llama state data")
+        invalidInputError(int(n_bytes) <= int(state_size), "Failed to copy llama state data.")
         llama_state_compact = (llama_cpp.c_uint8 * int(n_bytes))()
         llama_cpp.ctypes.memmove(llama_state_compact, llama_state, int(n_bytes))
         if self.verbose:
@@ -1342,26 +1364,27 @@ class Llama:
         )
 
     def load_state(self, state: LlamaState) -> None:
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         self.eval_tokens = state.eval_tokens.copy()
         self.eval_logits = state.eval_logits.copy()
         state_size = state.llama_state_size
-        if llama_cpp.llama_set_state_data(self.ctx, state.llama_state) != state_size:
-            raise RuntimeError("Failed to set llama state data")
+        invalidInputError(llama_cpp.llama_set_state_data(self.ctx,
+                                                         state.llama_state) == state_size,
+                          "Failed to set llama state data.")
 
     def n_ctx(self) -> int:
         """Return the context window size."""
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         return llama_cpp.llama_n_ctx(self.ctx)
 
     def n_embd(self) -> int:
         """Return the embedding size."""
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         return llama_cpp.llama_n_embd(self.ctx)
 
     def n_vocab(self) -> int:
         """Return the vocabulary size."""
-        assert self.ctx is not None
+        invalidInputError(self.ctx is not None, "The attribute `ctx` of `Llama` object is None.")
         return llama_cpp.llama_n_vocab(self.ctx)
 
     @staticmethod
diff --git a/python/llm/src/bigdl/llm/ggml/model/llama/llama_cpp.py b/python/llm/src/bigdl/llm/ggml/model/llama/llama_cpp.py
index 1667cc25..7f9e50ff 100644
--- a/python/llm/src/bigdl/llm/ggml/model/llama/llama_cpp.py
+++ b/python/llm/src/bigdl/llm/ggml/model/llama/llama_cpp.py
@@ -13,6 +13,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+# ===========================================================================
+#
+# This file is adapted from
+# https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama_cpp.py
+#
+# MIT License
+#
+# Copyright (c) 2023 Andrei Betlen
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
 
 # This would makes sure Python is aware there is more than one sub-package within bigdl,
 # physically located elsewhere.
@@ -36,6 +62,7 @@ from ctypes import (
     c_size_t,
 )
 import pathlib
+from bigdl.llm.utils.common import invalidInputError
 
 
 # Load the library
@@ -48,7 +75,7 @@ def _load_shared_library(lib_base_name: str):
     elif sys.platform == "win32":
         lib_ext = ".dll"
     else:
-        raise RuntimeError("Unsupported platform")
+        invalidInputError(False, "Unsupported platform.")
 
     # Construct the paths to the possible shared library names
     _base_path = pathlib.Path(__file__).parent.parent.parent.parent.resolve()
@@ -81,11 +108,9 @@ def _load_shared_library(lib_base_name: str):
             try:
                 return ctypes.CDLL(str(_lib_path), **cdll_args)
             except Exception as e:
-                raise RuntimeError(f"Failed to load shared library '{_lib_path}': {e}")
+                invalidInputError(False, f"Failed to load shared library '{_lib_path}': {e}.")
 
-    raise FileNotFoundError(
-        f"Shared library with base name '{lib_base_name}' not found"
-    )
+    invalidInputError(False, f"Shared library with base name '{lib_base_name}' not found.")
 
 
 # Specify the base name of the shared library to load
@@ -300,7 +325,8 @@ _lib.llama_free.restype = None
 
 # TODO: not great API - very likely to change
 # Returns 0 on success
-# nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
+# nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(),
+# else the number given
 # LLAMA_API int llama_model_quantize(
 #         const char * fname_inp,
 #         const char * fname_out,
@@ -399,7 +425,8 @@ _lib.llama_set_state_data.restype = c_size_t
 
 
 # Save/load session file
-# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
+# LLAMA_API bool llama_load_session_file(struct llama_context * ctx, const char * path_session,
+# llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out);
 def llama_load_session_file(
     ctx: llama_context_p,
     path_session: bytes,
@@ -422,7 +449,8 @@ _lib.llama_load_session_file.argtypes = [
 _lib.llama_load_session_file.restype = c_size_t
 
 
-# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count);
+# LLAMA_API bool llama_save_session_file(struct llama_context * ctx, const char * path_session,
+# const llama_token * tokens, size_t n_token_count);
 def llama_save_session_file(
     ctx: llama_context_p,
     path_session: bytes,
@@ -601,8 +629,10 @@ _lib.llama_init_candidates.argtypes = [
 _lib.llama_init_candidates.restype = None
 
 
-# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
-# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
+# @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858,
+# with negative logit fix.
+# LLAMA_API void llama_sample_repetition_penalty(struct llama_context * ctx, llama_token_data_array
+# * candidates, const llama_token * last_tokens, size_t last_tokens_size, float penalty);
 def llama_sample_repetition_penalty(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -625,8 +655,11 @@ _lib.llama_sample_repetition_penalty.argtypes = [
 _lib.llama_sample_repetition_penalty.restype = None
 
 
-# @details Frequency and presence penalties described in OpenAI API https://platform.openai.com/docs/api-reference/parameter-details.
-# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx, llama_token_data_array * candidates, const llama_token * last_tokens, size_t last_tokens_size, float alpha_frequency, float alpha_presence);
+# @details Frequency and presence penalties described in OpenAI API
+# https://platform.openai.com/docs/api-reference/parameter-details.
+# LLAMA_API void llama_sample_frequency_and_presence_penalties(struct llama_context * ctx,
+# llama_token_data_array * candidates, const llama_token * last_tokens,
+# size_t last_tokens_size, float alpha_frequency, float alpha_presence);
 def llama_sample_frequency_and_presence_penalties(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -656,8 +689,10 @@ _lib.llama_sample_frequency_and_presence_penalties.argtypes = [
 _lib.llama_sample_frequency_and_presence_penalties.restype = None
 
 
-# @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
-# LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);
+# @details Sorts candidate tokens by their logits in descending order and calculate probabilities
+# based on logits.
+# LLAMA_API void llama_sample_softmax(struct llama_context * ctx,
+# llama_token_data_array * candidates);
 def llama_sample_softmax(
     ctx: llama_context_p, candidates  # type: _Pointer[llama_token_data]
 ):
@@ -671,8 +706,10 @@ _lib.llama_sample_softmax.argtypes = [
 _lib.llama_sample_softmax.restype = None
 
 
-# @details Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-# LLAMA_API void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int k, size_t min_keep);
+# @details Top-K sampling described in academic paper
+# "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+# LLAMA_API void llama_sample_top_k(struct llama_context * ctx,
+# llama_token_data_array * candidates, int k, size_t min_keep);
 def llama_sample_top_k(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -691,8 +728,10 @@ _lib.llama_sample_top_k.argtypes = [
 _lib.llama_sample_top_k.restype = None
 
 
-# @details Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
-# LLAMA_API void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+# @details Nucleus sampling described in academic paper
+# "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+# LLAMA_API void llama_sample_top_p(struct llama_context * ctx,
+# llama_token_data_array * candidates, float p, size_t min_keep);
 def llama_sample_top_p(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -711,8 +750,10 @@ _lib.llama_sample_top_p.argtypes = [
 _lib.llama_sample_top_p.restype = None
 
 
-# @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
-# LLAMA_API void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * candidates, float z, size_t min_keep);
+# @details Tail Free Sampling described in
+# https://www.trentonbricken.com/Tail-Free-Sampling/.
+# LLAMA_API void llama_sample_tail_free(struct llama_context * ctx,
+# llama_token_data_array * candidates, float z, size_t min_keep);
 def llama_sample_tail_free(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -731,8 +772,10 @@ _lib.llama_sample_tail_free.argtypes = [
 _lib.llama_sample_tail_free.restype = None
 
 
-# @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
-# LLAMA_API void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * candidates, float p, size_t min_keep);
+# @details Locally Typical Sampling implementation described in the paper
+# https://arxiv.org/abs/2202.00666.
+# LLAMA_API void llama_sample_typical(struct llama_context * ctx,
+# llama_token_data_array * candidates, float p, size_t min_keep);
 def llama_sample_typical(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -751,7 +794,8 @@ _lib.llama_sample_typical.argtypes = [
 _lib.llama_sample_typical.restype = None
 
 
-# LLAMA_API void llama_sample_temperature(struct llama_context * ctx, llama_token_data_array * candidates, float temp);
+# LLAMA_API void llama_sample_temperature(struct llama_context * ctx,
+# llama_token_data_array * candidates, float temp);
 def llama_sample_temperature(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -768,13 +812,25 @@ _lib.llama_sample_temperature.argtypes = [
 _lib.llama_sample_temperature.restype = None
 
 
-# @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-# @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-# @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-# @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`. In the paper, they use `m = 100`, but you can experiment with different values to see how it affects the performance of the algorithm.
-# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-# LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
+# @details Mirostat 1.0 algorithm described in the paper https://arxiv.org/abs/2007.14966.
+# Uses tokens instead of words.
+# @param candidates A vector of `llama_token_data` containing the candidate tokens,
+# their probabilities (p), and log-odds (logit) for the current position in the generated text.
+# @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated
+# text. A higher value corresponds to more surprising or less predictable text, while a lower
+# value corresponds to less surprising or more predictable text.
+# @param eta The learning rate used to update `mu` based on the error between the target and
+# observed surprisal of the sampled word. A larger learning rate will cause `mu` to be
+# updated more quickly, while a smaller learning rate will result in slower updates.
+# @param m The number of tokens considered in the estimation of `s_hat`. This is an arbitrary value
+# that is used to calculate `s_hat`, which in turn helps to calculate the value of `k`.
+# In the paper, they use `m = 100`, but you can experiment with different values to see
+# how it affects the performance of the algorithm.
+# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy
+# (`2 * tau`) and is updated in the algorithm based on the error between the target and
+# observed surprisal.
+# LLAMA_API llama_token llama_sample_token_mirostat(struct llama_context * ctx,
+# llama_token_data_array * candidates, float tau, float eta, int m, float * mu);
 def llama_sample_token_mirostat(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -797,12 +853,21 @@ _lib.llama_sample_token_mirostat.argtypes = [
 _lib.llama_sample_token_mirostat.restype = llama_token
 
 
-# @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966. Uses tokens instead of words.
-# @param candidates A vector of `llama_token_data` containing the candidate tokens, their probabilities (p), and log-odds (logit) for the current position in the generated text.
-# @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated text. A higher value corresponds to more surprising or less predictable text, while a lower value corresponds to less surprising or more predictable text.
-# @param eta The learning rate used to update `mu` based on the error between the target and observed surprisal of the sampled word. A larger learning rate will cause `mu` to be updated more quickly, while a smaller learning rate will result in slower updates.
-# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy (`2 * tau`) and is updated in the algorithm based on the error between the target and observed surprisal.
-# LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu);
+# @details Mirostat 2.0 algorithm described in the paper https://arxiv.org/abs/2007.14966.
+# Uses tokens instead of words.
+# @param candidates A vector of `llama_token_data` containing the candidate tokens,
+# their probabilities (p), and log-odds (logit) for the current position in the generated text.
+# @param tau  The target cross-entropy (or surprise) value you want to achieve for the generated
+# text. A higher value corresponds to more surprising or less predictable text, while a lower value
+# corresponds to less surprising or more predictable text.
+# @param eta The learning rate used to update `mu` based on the error between the target and
+# observed surprisal of the sampled word. A larger learning rate will cause `mu` to be
+# updated more quickly, while a smaller learning rate will result in slower updates.
+# @param mu Maximum cross-entropy. This value is initialized to be twice the target cross-entropy
+# (`2 * tau`) and is updated in the algorithm based on the error between the target
+# and observed surprisal.
+# LLAMA_API llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx,
+# llama_token_data_array * candidates, float tau, float eta, float * mu);
 def llama_sample_token_mirostat_v2(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -824,7 +889,8 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
 
 
 # @details Selects the token with the highest probability.
-# LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx, llama_token_data_array * candidates);
+# LLAMA_API llama_token llama_sample_token_greedy(struct llama_context * ctx,
+# llama_token_data_array * candidates);
 def llama_sample_token_greedy(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
@@ -840,7 +906,8 @@ _lib.llama_sample_token_greedy.restype = llama_token
 
 
 # @details Randomly selects a token from the candidates based on their probabilities.
-# LLAMA_API llama_token llama_sample_token(struct llama_context * ctx, llama_token_data_array * candidates);
+# LLAMA_API llama_token llama_sample_token(struct llama_context * ctx,
+# llama_token_data_array * candidates);
 def llama_sample_token(
     ctx: llama_context_p,
     candidates,  # type: _Pointer[llama_token_data_array]
diff --git a/python/llm/src/bigdl/llm/ggml/model/llama/llama_types.py b/python/llm/src/bigdl/llm/ggml/model/llama/llama_types.py
index d7ab32d9..f3ddd6d5 100644
--- a/python/llm/src/bigdl/llm/ggml/model/llama/llama_types.py
+++ b/python/llm/src/bigdl/llm/ggml/model/llama/llama_types.py
@@ -13,6 +13,32 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
+# ===========================================================================
+#
+# This file is adapted from
+# https://github.com/abetlen/llama-cpp-python/blob/main/llama_cpp/llama_types.py
+#
+# MIT License
+#
+# Copyright (c) 2023 Andrei Betlen
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
 
 # This would makes sure Python is aware there is more than one sub-package within bigdl,
 # physically located elsewhere.