docs: Update completion and chat_completion parameter docstrings

Author: abetlen

llama_cpp/llama.py

@@ -1863,13 +1863,27 @@ def create_completion(
             suffix: A suffix to append to the generated text. If None, no suffix is appended.
             max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
             temperature: The temperature to use for sampling.
-            top_p: The top-p value to use for sampling.
+            top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+            min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+            typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
             logprobs: The number of logprobs to return. If None, no logprobs are returned.
             echo: Whether to echo the prompt.
             stop: A list of strings to stop generation when encountered.
+            frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
+            presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
             repeat_penalty: The penalty to apply to repeated tokens.
-            top_k: The top-k value to use for sampling.
+            top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
             stream: Whether to stream the results.
+            seed: The seed to use for sampling.
+            tfs_z: The tail-free sampling parameter. Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
+            mirostat_mode: The mirostat sampling mode.
+            mirostat_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.
+            mirostat_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.
+            model: The name to use for the model in the completion object.
+            stopping_criteria: A list of stopping criteria to use.
+            logits_processor: A list of logits processors to use.
+            grammar: A grammar to use for constrained sampling.
+            logit_bias: A logit bias to use.
 
         Raises:
             ValueError: If the requested tokens exceed the context window.
@@ -1944,15 +1958,29 @@ def __call__(
         Args:
             prompt: The prompt to generate text from.
             suffix: A suffix to append to the generated text. If None, no suffix is appended.
-            max_tokens: The maximum number of tokens to generate. If max_tokens <= 0, the maximum number of tokens to generate is unlimited and depends on n_ctx.
+            max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
             temperature: The temperature to use for sampling.
-            top_p: The top-p value to use for sampling.
+            top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+            min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+            typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
             logprobs: The number of logprobs to return. If None, no logprobs are returned.
             echo: Whether to echo the prompt.
             stop: A list of strings to stop generation when encountered.
+            frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
+            presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
             repeat_penalty: The penalty to apply to repeated tokens.
-            top_k: The top-k value to use for sampling.
+            top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
             stream: Whether to stream the results.
+            seed: The seed to use for sampling.
+            tfs_z: The tail-free sampling parameter. Tail Free Sampling described in https://www.trentonbricken.com/Tail-Free-Sampling/.
+            mirostat_mode: The mirostat sampling mode.
+            mirostat_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.
+            mirostat_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.
+            model: The name to use for the model in the completion object.
+            stopping_criteria: A list of stopping criteria to use.
+            logits_processor: A list of logits processors to use.
+            grammar: A grammar to use for constrained sampling.
+            logit_bias: A logit bias to use.
 
         Raises:
             ValueError: If the requested tokens exceed the context window.
@@ -2024,13 +2052,31 @@ def create_chat_completion(
 
         Args:
             messages: A list of messages to generate a response for.
+            functions: A list of functions to use for the chat completion.
+            function_call: A function call to use for the chat completion.
+            tools: A list of tools to use for the chat completion.
+            tool_choice: A tool choice to use for the chat completion.
             temperature: The temperature to use for sampling.
-            top_p: The top-p value to use for sampling.
-            top_k: The top-k value to use for sampling.
+            top_p: The top-p value to use for nucleus sampling. Nucleus sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+            top_k: The top-k value to use for sampling. Top-K sampling described in academic paper "The Curious Case of Neural Text Degeneration" https://arxiv.org/abs/1904.09751
+            min_p: The min-p value to use for minimum p sampling. Minimum P sampling as described in https://github.com/ggerganov/llama.cpp/pull/3841
+            typical_p: The typical-p value to use for sampling. Locally Typical Sampling implementation described in the paper https://arxiv.org/abs/2202.00666.
             stream: Whether to stream the results.
             stop: A list of strings to stop generation when encountered.
+            seed: The seed to use for sampling.
+            response_format: The response format to use for the chat completion. Use { "type": "json_object" } to contstrain output to only valid json.
             max_tokens: The maximum number of tokens to generate. If max_tokens <= 0 or None, the maximum number of tokens to generate is unlimited and depends on n_ctx.
+            presence_penalty: The penalty to apply to tokens based on their presence in the prompt.
+            frequency_penalty: The penalty to apply to tokens based on their frequency in the prompt.
             repeat_penalty: The penalty to apply to repeated tokens.
+            tfs_z: The tail-free sampling parameter.
+            mirostat_mode: The mirostat sampling mode.
+            mirostat_tau: The mirostat sampling tau parameter.
+            mirostat_eta: The mirostat sampling eta parameter.
+            model: The name to use for the model in the completion object.
+            logits_processor: A list of logits processors to use.
+            grammar: A grammar to use.
+            logit_bias: A logit bias to use.
 
         Returns:
             Generated chat completion or a stream of chat completion chunks.