diff --git a/llama_cpp/server/app.py b/llama_cpp/server/app.py
index ba2ca2f..48dfc5e 100644
--- a/llama_cpp/server/app.py
+++ b/llama_cpp/server/app.py
@@ -13,18 +13,41 @@ from sse_starlette.sse import EventSourceResponse
 
 
 class Settings(BaseSettings):
-    model: str
-    n_ctx: int = 2048
-    n_batch: int = 512
-    n_threads: int = max((os.cpu_count() or 2) // 2, 1)
-    f16_kv: bool = True
-    use_mlock: bool = False  # This causes a silent failure on platforms that don't support mlock (e.g. Windows) took forever to figure out...
-    use_mmap: bool = True
-    embedding: bool = True
-    last_n_tokens_size: int = 64
-    logits_all: bool = False
-    cache: bool = False  # WARNING: This is an experimental feature
-    vocab_only: bool = False
+    model: str = Field(
+        description="The path to the model to use for generating completions."
+    )
+    n_ctx: int = Field(default=2048, ge=1, description="The context size.")
+    n_batch: int = Field(
+        default=512, ge=1, description="The batch size to use per eval."
+    )
+    n_threads: int = Field(
+        default=max((os.cpu_count() or 2) // 2, 1),
+        ge=1,
+        description="The number of threads to use.",
+    )
+    f16_kv: bool = Field(default=True, description="Whether to use f16 key/value.")
+    use_mlock: bool = Field(
+        default=bool(llama_cpp.llama_mlock_supported().value),
+        description="Use mlock.",
+    )
+    use_mmap: bool = Field(
+        default=bool(llama_cpp.llama_mmap_supported().value),
+        description="Use mmap.",
+    )
+    embedding: bool = Field(default=True, description="Whether to use embeddings.")
+    last_n_tokens_size: int = Field(
+        default=64,
+        ge=0,
+        description="Last n tokens to keep for repeat penalty calculation.",
+    )
+    logits_all: bool = Field(default=True, description="Whether to return logits.")
+    cache: bool = Field(
+        default=False,
+        description="Use a cache to reduce processing times for evaluated prompts.",
+    )
+    vocab_only: bool = Field(
+        default=False, description="Whether to only return the vocabulary."
+    )
 
 
 router = APIRouter()
@@ -74,79 +97,75 @@ def get_llama():
     with llama_lock:
         yield llama
 
-model_field = Field(
-    description="The model to use for generating completions."
-)
+
+model_field = Field(description="The model to use for generating completions.")
 
 max_tokens_field = Field(
-    default=16,
-    ge=1,
-    le=2048,
-    description="The maximum number of tokens to generate."
+    default=16, ge=1, le=2048, description="The maximum number of tokens to generate."
 )
 
 temperature_field = Field(
     default=0.8,
     ge=0.0,
     le=2.0,
-    description="Adjust the randomness of the generated text.\n\n" +
-    "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run."
+    description="Adjust the randomness of the generated text.\n\n"
+    + "Temperature is a hyperparameter that controls the randomness of the generated text. It affects the probability distribution of the model's output tokens. A higher temperature (e.g., 1.5) makes the output more random and creative, while a lower temperature (e.g., 0.5) makes the output more focused, deterministic, and conservative. The default value is 0.8, which provides a balance between randomness and determinism. At the extreme, a temperature of 0 will always pick the most likely next token, leading to identical outputs in each run.",
 )
 
 top_p_field = Field(
     default=0.95,
     ge=0.0,
     le=1.0,
-    description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n" +
-    "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text."
+    description="Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P.\n\n"
+    + "Top-p sampling, also known as nucleus sampling, is another text generation method that selects the next token from a subset of tokens that together have a cumulative probability of at least p. This method provides a balance between diversity and quality by considering both the probabilities of tokens and the number of tokens to sample from. A higher value for top_p (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text.",
 )
 
 stop_field = Field(
     default=None,
-    description="A list of tokens at which to stop generation. If None, no stop tokens are used."
+    description="A list of tokens at which to stop generation. If None, no stop tokens are used.",
 )
 
 stream_field = Field(
     default=False,
-    description="Whether to stream the results as they are generated. Useful for chatbots."
+    description="Whether to stream the results as they are generated. Useful for chatbots.",
 )
 
 top_k_field = Field(
     default=40,
     ge=0,
-    description="Limit the next token selection to the K most probable tokens.\n\n" +
-    "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text."
+    description="Limit the next token selection to the K most probable tokens.\n\n"
+    + "Top-k sampling is a text generation method that selects the next token only from the top k most likely tokens predicted by the model. It helps reduce the risk of generating low-probability or nonsensical tokens, but it may also limit the diversity of the output. A higher value for top_k (e.g., 100) will consider more tokens and lead to more diverse text, while a lower value (e.g., 10) will focus on the most probable tokens and generate more conservative text.",
 )
 
 repeat_penalty_field = Field(
     default=1.0,
     ge=0.0,
-    description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n" +
-    "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient."
+    description="A penalty applied to each token that is already generated. This helps prevent the model from repeating itself.\n\n"
+    + "Repeat penalty is a hyperparameter used to penalize the repetition of token sequences during text generation. It helps prevent the model from generating repetitive or monotonous text. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient.",
 )
 
+
 class CreateCompletionRequest(BaseModel):
     prompt: Optional[str] = Field(
-        default="",
-        description="The prompt to generate completions for."
+        default="", description="The prompt to generate completions for."
     )
     suffix: Optional[str] = Field(
         default=None,
-        description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots."
+        description="A suffix to append to the generated text. If None, no suffix is appended. Useful for chatbots.",
     )
     max_tokens: int = max_tokens_field
     temperature: float = temperature_field
     top_p: float = top_p_field
     echo: bool = Field(
         default=False,
-        description="Whether to echo the prompt in the generated text. Useful for chatbots."
+        description="Whether to echo the prompt in the generated text. Useful for chatbots.",
     )
     stop: Optional[List[str]] = stop_field
     stream: bool = stream_field
     logprobs: Optional[int] = Field(
         default=None,
         ge=0,
-        description="The number of logprobs to generate. If None, no logprobs are generated."
+        description="The number of logprobs to generate. If None, no logprobs are generated.",
     )
 
     # ignored or currently unsupported
@@ -204,9 +223,7 @@ def create_completion(
 
 class CreateEmbeddingRequest(BaseModel):
     model: Optional[str] = model_field
-    input: str = Field(
-        description="The input to embed."
-    )
+    input: str = Field(description="The input to embed.")
     user: Optional[str]
 
     class Config:
@@ -239,8 +256,7 @@ class ChatCompletionRequestMessage(BaseModel):
 
 class CreateChatCompletionRequest(BaseModel):
     messages: List[ChatCompletionRequestMessage] = Field(
-        default=[],
-        description="A list of messages to generate completions for."
+        default=[], description="A list of messages to generate completions for."
     )
     max_tokens: int = max_tokens_field
     temperature: float = temperature_field