From 978b6daf9313a11367d0a9393226379173fdb688 Mon Sep 17 00:00:00 2001
From: Lucas Doyle <lucas.p.doyle@gmail.com>
Date: Sat, 29 Apr 2023 14:37:36 -0700
Subject: [PATCH] llama_cpp server: add some more information to fields for
 completions

---
 llama_cpp/server/app.py | 70 ++++++++++++++++++++++++++++++++++-------
 1 file changed, 59 insertions(+), 11 deletions(-)

diff --git a/llama_cpp/server/app.py b/llama_cpp/server/app.py
index e1045af..e168485 100644
--- a/llama_cpp/server/app.py
+++ b/llama_cpp/server/app.py
@@ -71,22 +71,70 @@ model_field = Field(
 )
 
 class CreateCompletionRequest(BaseModel):
-    prompt: Union[str, List[str]]
-    suffix: Optional[str] = Field(None)
-    max_tokens: int = 16
-    temperature: float = 0.8
-    top_p: float = 0.95
-    echo: bool = False
-    stop: Optional[List[str]] = []
-    stream: bool = False
-    logprobs: Optional[int] = Field(None)
+    prompt: Union[str, List[str]] = Field(
+        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."
+    )
+    max_tokens: int = Field(
+        default=16,
+        ge=1,
+        le=2048,
+        description="The maximum number of tokens to generate."
+    )
+    temperature: float = 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."
+    )
+    top_p: float = 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."
+    )
+    echo: bool = Field(
+        default=False,
+        description="Whether to echo the prompt in the generated text. Useful for chatbots."
+    )
+    stop: Optional[List[str]] = Field(
+        default=None,
+        description="A list of tokens at which to stop generation. If None, no stop tokens are used."
+    )
+    stream: bool = Field(
+        default=False,
+        description="Whether to stream the results as they are generated. Useful for chatbots."
+    )
+    logprobs: Optional[int] = Field(
+        default=None,
+        ge=0,
+        description="The number of logprobs to generate. If None, no logprobs are generated."
+    )
+
+
 
     # ignored, but marked as required for the sake of compatibility with openai's api
     model: str = model_field
 
     # llama.cpp specific parameters
-    top_k: int = 40
-    repeat_penalty: float = 1.1
+    top_k: int = 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."
+    )
+    repeat_penalty: float = 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."
+    )
 
     class Config:
         schema_extra = {