diff --git a/examples/llama_cpp.py b/examples/llama_cpp.py
index f2366effe..fce7fce1c 100644
--- a/examples/llama_cpp.py
+++ b/examples/llama_cpp.py
@@ -17,7 +17,7 @@ import pathlib
 
 
 # Load the library
-def _load_shared_library(lib_base_name):
+def _load_shared_library(lib_base_name: str):
     # Determine the file extension based on the platform
     if sys.platform.startswith("linux"):
         lib_ext = ".so"
@@ -252,7 +252,9 @@ _lib.llama_get_state_size.restype = c_size_t
 # Copies the state to the specified destination address.
 # Destination needs to have allocated enough memory.
 # Returns the number of bytes copied
-def llama_copy_state_data(ctx: llama_context_p, dest) -> c_size_t:
+def llama_copy_state_data(
+    ctx: llama_context_p, dest  # type: Array[c_uint8]
+) -> c_size_t:
     return _lib.llama_copy_state_data(ctx, dest)
 
 
@@ -262,7 +264,9 @@ _lib.llama_copy_state_data.restype = c_size_t
 
 # Set the state reading from the specified address
 # Returns the number of bytes read
-def llama_set_state_data(ctx: llama_context_p, src) -> c_size_t:
+def llama_set_state_data(
+    ctx: llama_context_p, src  # type: Array[c_uint8]
+) -> c_size_t:
     return _lib.llama_set_state_data(ctx, src)
 
 
@@ -274,9 +278,9 @@ _lib.llama_set_state_data.restype = c_size_t
 def llama_load_session_file(
     ctx: llama_context_p,
     path_session: bytes,
-    tokens_out,
+    tokens_out,  # type: Array[llama_token]
     n_token_capacity: c_size_t,
-    n_token_count_out,
+    n_token_count_out,  # type: Array[c_size_t]
 ) -> c_size_t:
     return _lib.llama_load_session_file(
         ctx, path_session, tokens_out, n_token_capacity, n_token_count_out
@@ -294,7 +298,10 @@ _lib.llama_load_session_file.restype = c_size_t
 
 
 def llama_save_session_file(
-    ctx: llama_context_p, path_session: bytes, tokens, n_token_count: c_size_t
+    ctx: llama_context_p,
+    path_session: bytes,
+    tokens,  # type: Array[llama_token]
+    n_token_count: c_size_t,
 ) -> c_size_t:
     return _lib.llama_save_session_file(ctx, path_session, tokens, n_token_count)
 
@@ -433,8 +440,8 @@ _lib.llama_token_nl.restype = llama_token
 # @details Repetition penalty described in CTRL academic paper https://arxiv.org/abs/1909.05858, with negative logit fix.
 def llama_sample_repetition_penalty(
     ctx: llama_context_p,
-    candidates,
-    last_tokens_data,
+    candidates, # type: Array[llama_token_data]
+    last_tokens_data, # type: Array[llama_token]
     last_tokens_size: c_int,
     penalty: c_float,
 ):
@@ -456,8 +463,8 @@ _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.
 def llama_sample_frequency_and_presence_penalties(
     ctx: llama_context_p,
-    candidates,
-    last_tokens_data,
+    candidates, # type: Array[llama_token_data]
+    last_tokens_data, # type: Array[llama_token]
     last_tokens_size: c_int,
     alpha_frequency: c_float,
     alpha_presence: c_float,
@@ -484,7 +491,10 @@ _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.
-def llama_sample_softmax(ctx: llama_context_p, candidates):
+def llama_sample_softmax(
+    ctx: llama_context_p,
+    candidates # type: Array[llama_token_data]
+):
     return _lib.llama_sample_softmax(ctx, candidates)
 
 
@@ -497,7 +507,10 @@ _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
 def llama_sample_top_k(
-    ctx: llama_context_p, candidates, k: c_int, min_keep: c_size_t = c_size_t(1)
+    ctx: llama_context_p,
+    candidates,  # type: Array[llama_token_data]
+    k: c_int,
+    min_keep: c_size_t = c_size_t(1)
 ):
     return _lib.llama_sample_top_k(ctx, candidates, k, min_keep)
 
@@ -513,7 +526,10 @@ _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
 def llama_sample_top_p(
-    ctx: llama_context_p, candidates, p: c_float, min_keep: c_size_t = c_size_t(1)
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    p: c_float,
+    min_keep: c_size_t = c_size_t(1)
 ):
     return _lib.llama_sample_top_p(ctx, candidates, p, min_keep)
 
@@ -529,7 +545,10 @@ _lib.llama_sample_top_p.restype = None
 
 # @details Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
 def llama_sample_tail_free(
-    ctx: llama_context_p, candidates, z: c_float, min_keep: c_size_t = c_size_t(1)
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    z: c_float,
+    min_keep: c_size_t = c_size_t(1)
 ):
     return _lib.llama_sample_tail_free(ctx, candidates, z, min_keep)
 
@@ -545,7 +564,10 @@ _lib.llama_sample_tail_free.restype = None
 
 # @details Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
 def llama_sample_typical(
-    ctx: llama_context_p, candidates, p: c_float, min_keep: c_size_t = c_size_t(1)
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    p: c_float,
+    min_keep: c_size_t = c_size_t(1)
 ):
     return _lib.llama_sample_typical(ctx, candidates, p, min_keep)
 
@@ -559,7 +581,11 @@ _lib.llama_sample_typical.argtypes = [
 _lib.llama_sample_typical.restype = None
 
 
-def llama_sample_temperature(ctx: llama_context_p, candidates, temp: c_float):
+def llama_sample_temperature(
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    temp: c_float
+):
     return _lib.llama_sample_temperature(ctx, candidates, temp)
 
 
@@ -578,7 +604,12 @@ _lib.llama_sample_temperature.restype = None
 # @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.
 def llama_sample_token_mirostat(
-    ctx: llama_context_p, candidates, tau: c_float, eta: c_float, m: c_int, mu
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    tau: c_float,
+    eta: c_float,
+    m: c_int,
+    mu # type: Array[c_float]
 ) -> llama_token:
     return _lib.llama_sample_token_mirostat(ctx, candidates, tau, eta, m, mu)
 
@@ -600,7 +631,11 @@ _lib.llama_sample_token_mirostat.restype = llama_token
 # @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.
 def llama_sample_token_mirostat_v2(
-    ctx: llama_context_p, candidates, tau: c_float, eta: c_float, mu
+    ctx: llama_context_p,
+    candidates, # type: Array[llama_token_data]
+    tau: c_float,
+    eta: c_float,
+    mu # type: Array[c_float]
 ) -> llama_token:
     return _lib.llama_sample_token_mirostat_v2(ctx, candidates, tau, eta, mu)
 
@@ -616,7 +651,10 @@ _lib.llama_sample_token_mirostat_v2.restype = llama_token
 
 
 # @details Selects the token with the highest probability.
-def llama_sample_token_greedy(ctx: llama_context_p, candidates) -> llama_token:
+def llama_sample_token_greedy(
+    ctx: llama_context_p,
+    candidates # type: Array[llama_token_data]
+) -> llama_token:
     return _lib.llama_sample_token_greedy(ctx, candidates)
 
 
@@ -628,7 +666,10 @@ _lib.llama_sample_token_greedy.restype = llama_token
 
 
 # @details Randomly selects a token from the candidates based on their probabilities.
-def llama_sample_token(ctx: llama_context_p, candidates) -> llama_token:
+def llama_sample_token(
+    ctx: llama_context_p,
+    candidates # type: Array[llama_token_data]
+) -> llama_token:
     return _lib.llama_sample_token(ctx, candidates)